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230 KiB
230 KiB
| 1 | id | description | dataset | category | subcategory | region | delay | universe | type | dateCoverage | coverage | userCount | alphaCount | pyramidMultiplier | themes | dataset_id | dataset_name | category_id | category_name | subcategory_id | subcategory_name |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | adjfactor | Adjustment factor applied to historical prices and dividends to account for splits and other corporate actions | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 0 | 0 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 3 | adv20 | Average daily volume in past 20 days | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 807 | 5362 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 4 | cap | Daily market capitalization (in millions) | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 1343 | 13996 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 5 | close | Daily close price | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 1979 | 35257 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 6 | dividend | Dividend | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 282 | 3636 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 7 | high | Daily high price | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 718 | 6168 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 8 | low | Daily low price | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 559 | 5053 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 9 | open | Daily open price | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 917 | 16295 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 10 | returns | Daily returns | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 1929 | 33747 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 11 | sharesout | Daily outstanding shares (in millions) | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 372 | 3302 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 12 | split | Stock split ratio | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 316 | 3390 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 13 | volume | Daily volume | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 1217 | 8816 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 14 | vwap | Daily volume weighted average price | {'id': 'pv1', 'name': 'Price Volume Data for Equity'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 715 | 5610 | 1.2 | [] | pv1 | Price Volume Data for Equity | pv | Price Volume | pv-price-volume | Price Volume |
| 15 | session_1430to1430_final_trade_price | Last trade price recorded at the end of the 14:30 to 14:30 session. | {'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 339 | 1068 | 1.2 | [] | pv103 | Interval and MOO&MOC statistics | pv | Price Volume | pv-price-volume | Price Volume |
| 16 | session_1430to1430_initial_trade_price | First trade price recorded at the start of the 14:30 to 14:30 session. | {'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 236 | 726 | 1.2 | [] | pv103 | Interval and MOO&MOC statistics | pv | Price Volume | pv-price-volume | Price Volume |
| 17 | session_1430to1430_market_value | Aggregate market value of all trades during the 14:30 to 14:30 session. | {'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 243 | 926 | 1.2 | [] | pv103 | Interval and MOO&MOC statistics | pv | Price Volume | pv-price-volume | Price Volume |
| 18 | session_1430to1430_max_trade_price | Highest trade price recorded during the 14:30 to 14:30 session. | {'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 213 | 781 | 1.2 | [] | pv103 | Interval and MOO&MOC statistics | pv | Price Volume | pv-price-volume | Price Volume |
| 19 | session_1430to1430_min_trade_price | Lowest trade price recorded during the 14:30 to 14:30 session. | {'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 209 | 762 | 1.2 | [] | pv103 | Interval and MOO&MOC statistics | pv | Price Volume | pv-price-volume | Price Volume |
| 20 | session_1430to1430_total_traded_volume | Total number of shares or contracts traded during the 14:30 to 14:30 session. | {'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 143 | 299 | 1.2 | [] | pv103 | Interval and MOO&MOC statistics | pv | Price Volume | pv-price-volume | Price Volume |
| 21 | session_1430to1430_volume_weighted_avg_price | Volume-weighted average price for trades during the 14:30 to 14:30 session. | {'id': 'pv103', 'name': 'Interval and MOO&MOC statistics'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 223 | 801 | 1.2 | [] | pv103 | Interval and MOO&MOC statistics | pv | Price Volume | pv-price-volume | Price Volume |
| 22 | aggregated_slippage_metric | Comprehensive measure of slippage across multiple trades or venues. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 93 | 181 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 23 | asia_trade_cost_buy | Estimated cost incurred when buying in Asian markets. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 30 | 37 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 24 | asia_trade_cost_sell | Estimated cost incurred when selling in Asian markets. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 15 | 16 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 25 | asian_market_slippage | Slippage metric specific to Asian market trades. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 7 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 26 | average_spread_slippage | Estimated portion of trade slippage attributable to crossing the bid-ask spread, i.e., the extra transaction cost versus mid-price execution when trading futures | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 23 | 24 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 27 | bid_ask_price_gap | Difference between the best bid and ask prices for a security. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.989 | 49 | 62 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 28 | group_buy_slippage | Slippage incurred when executing grouped buy orders. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 16 | 25 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 29 | group_order_slippage | Slippage experienced when executing grouped orders. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 1.0 | 1.0 | 1 | 1 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 30 | group_sell_slippage | Slippage incurred when executing grouped sell orders. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 16 | 26 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 31 | korean_market_slippage | Korea-specific modeled trading slippage overlay that estimates expected execution cost for Korean equities, derived from microstructure and spread data and masked for eligibility | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 59 | 105 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 32 | price_difference_bid_ask | Unknown | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.989 | 49 | 64 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 33 | pv106_wli_lastspread | Bid-ask spread averaged over the last 30 minutes | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.957 | 47 | 105 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 34 | pv106_wli_lastspreadbp | Bid-ask spread over the last 30 minutes expressed in basis points | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.957 | 39 | 56 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 35 | pv106_wli_spread | Difference between bid and ask price (raw bid-ask spread) | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9867 | 83 | 142 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 36 | pv106_wli_spreadbp | Bid-ask spread expressed in basis points | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9867 | 63 | 111 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 37 | slippage_at_spread_20 | Slippage value calculated at a spread threshold of 20 units. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 15 | 31 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 38 | slippage_commission_2025 | Estimated slippage and commission costs for the year 2025. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 16 | 21 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 39 | slippage_commission_estimate | Estimated slippage and commission costs for trades. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 25 | 43 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 40 | transaction_cost_estimate | Estimated cost incurred when executing a trade. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 15 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 41 | transaction_cost_maximum | Maximum estimated transaction cost for a trade. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 31 | 42 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 42 | transaction_cost_median | Median estimated transaction cost for a trade. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 24 | 34 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 43 | transaction_cost_percentile_10 | Estimated transaction cost at the 10th percentile for a trade. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 23 | 24 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 44 | transaction_cost_percentile_25 | Estimated transaction cost at the 25th percentile for a trade. | {'id': 'pv106', 'name': 'Microstructure Spread Data'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 14 | 15 | 1.2 | [] | pv106 | Microstructure Spread Data | pv | Price Volume | pv-price-volume | Price Volume |
| 45 | pv149_status_5 | Status code representing current state of data/record, e.g., valid, missing, suspended | {'id': 'pv149', 'name': 'Holidays and Trading Hours Calendar'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-relationship', 'name': 'Relationship'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 94 | 224 | 1.2 | [] | pv149 | Holidays and Trading Hours Calendar | pv | Price Volume | pv-relationship | Relationship |
| 46 | industry_grouping_level10_all_instruments_variant513 | Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 10 adaptive clusters based on co-movement of returns; value is cluster label (1 to 10), or -1/10000 for unclassified | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 152 | 274 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 47 | industry_grouping_level10_india500_xjp513 | For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 10 statistically determined clusters based on historical stock return correlations, representing adaptive industry classification labels. Values from 1 to 10 indicate cluster membership; -1 or 10000 means unclassified | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9261 | 80 | 141 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 48 | industry_grouping_level10_top800_xjp513 | Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 10 groups | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.1674 | 9 | 9 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 49 | industry_grouping_level20_all_instruments_variant513 | Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 20 adaptive clusters based on co-movement of returns; value is cluster label (1 to 20), or -1/10000 for unclassified | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 86 | 169 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 50 | industry_grouping_level20_india500_xjp513 | For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 20 statistically determined clusters based on historical stock return correlations, representing fine-grained industry classification labels. Values from 1 to 20 indicate cluster membership; -1 or 10000 means unclassified | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9261 | 70 | 122 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 51 | industry_grouping_level20_top800_xjp513 | Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 20 groups | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.1674 | 9 | 10 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 52 | industry_grouping_level2_all_instruments_variant513 | Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 2 adaptive clusters based on co-movement of returns; value is cluster label (1 or 2), or -1/10000 for unclassified | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 53 | 78 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 53 | industry_grouping_level2_india500_xjp513 | For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 2 statistically determined clusters based on historical stock return correlations, representing broad statistical industry classification labels. Values from 1 to 2 indicate cluster membership; -1 or 10000 means unclassified | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9261 | 103 | 309 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 54 | industry_grouping_level2_top800_xjp513 | Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 2 groups | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.1674 | 0 | 0 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 55 | industry_grouping_level50_all_instruments_variant513 | Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 50 adaptive clusters based on co-movement of returns; value is cluster label (1 to 50), or -1/10000 for unclassified | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 100 | 240 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 56 | industry_grouping_level50_india500_xjp513 | For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 50 statistically determined clusters based on historical stock return correlations, representing very fine-grained industry classification labels. Values from 1 to 50 indicate cluster membership; -1 or 10000 means unclassified | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9261 | 58 | 89 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 57 | industry_grouping_level50_top800_xjp513 | Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 50 groups | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.1674 | 7 | 7 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 58 | industry_grouping_level5_all_instruments_variant513 | Statistical cluster assignment for each stock and date, grouping all stocks in region 513 into 5 adaptive clusters based on co-movement of returns; value is cluster label (1 to 5), or -1/10000 for unclassified | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 60 | 113 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 59 | industry_grouping_level5_india500_xjp513 | For each date and each of the top 500 Indian stocks (excluding Japan subset, version 513), this field assigns the stock to one of 5 statistically determined clusters based on historical stock return correlations, representing intermediate-level statistical industry classification labels. Values from 1 to 5 indicate cluster membership; -1 or 10000 means unclassified | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9261 | 64 | 99 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 60 | industry_grouping_level5_top800_xjp513 | Cluster membership assignment for each stock (per date) in the top 800 ex-Japan universe, based on statistical analysis of return co-movement, split into 5 groups | {'id': 'pv29', 'name': 'Derived Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.1674 | 7 | 12 | 1.2 | [] | pv29 | Derived Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 61 | asia_equity_minvol1m_pca_method1_group10 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 35 | 52 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 62 | asia_equity_minvol1m_pca_method1_group2 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 12 | 14 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 63 | asia_equity_minvol1m_pca_method1_group20 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 12 | 20 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 64 | asia_equity_minvol1m_pca_method1_group5 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 13 | 17 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 65 | asia_equity_minvol1m_pca_method1_group50 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT1 method, assigning each stock to one of 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 8 | 20 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 66 | asia_equity_minvol1m_pca_method2_group10 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 10 | 11 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 67 | asia_equity_minvol1m_pca_method2_group2 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 8 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 68 | asia_equity_minvol1m_pca_method2_group20 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 69 | asia_equity_minvol1m_pca_method2_group5 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 9 | 10 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 70 | asia_equity_minvol1m_pca_method2_group50 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT2 method, assigning each stock to one of 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 71 | asia_equity_minvol1m_pca_method3_group10 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 72 | asia_equity_minvol1m_pca_method3_group2 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 73 | asia_equity_minvol1m_pca_method3_group20 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 74 | asia_equity_minvol1m_pca_method3_group5 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 75 | asia_equity_minvol1m_pca_method3_group50 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT3 method, assigning each stock to one of 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 76 | asia_equity_minvol1m_pca_method4_group10 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 77 | asia_equity_minvol1m_pca_method4_group2 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 78 | asia_equity_minvol1m_pca_method4_group20 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 79 | asia_equity_minvol1m_pca_method4_group5 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 8 | 11 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 80 | asia_equity_minvol1m_pca_method4_group50 | Return-based robust industry cluster assignment (categorical integer label) for Asia equities in the EQY_ASI_Supported_MinVol1M universe, computed with FACT4 method, assigning each stock to one of 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 81 | asia_equity_pca_method1_group10 | Asia equity principal component grouping using method 1 with 10 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 82 | asia_equity_pca_method1_group2 | Asia equity principal component grouping using method 1 with 2 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 83 | asia_equity_pca_method1_group20 | Asia equity principal component grouping using method 1 with 20 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 84 | asia_equity_pca_method1_group5 | Asia equity principal component grouping using method 1 with 5 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 85 | asia_equity_pca_method1_group50 | Asia equity principal component grouping using method 1 with 50 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 86 | asia_equity_pca_method2_group10 | Asia equity principal component grouping using method 2 with 10 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 87 | asia_equity_pca_method2_group2 | Asia equity principal component grouping using method 2 with 2 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 88 | asia_equity_pca_method2_group20 | Asia equity principal component grouping using method 2 with 20 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 89 | asia_equity_pca_method2_group5 | Asia equity principal component grouping using method 2 with 5 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 90 | asia_equity_pca_method2_group50 | Asia equity principal component grouping using method 2 with 50 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 91 | asia_equity_pca_method3_group10 | Asia equity principal component grouping using method 3 with 10 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 92 | asia_equity_pca_method3_group2 | Asia equity principal component grouping using method 3 with 2 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 93 | asia_equity_pca_method3_group20 | Asia equity principal component grouping using method 3 with 20 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 94 | asia_equity_pca_method3_group5 | Asia equity principal component grouping using method 3 with 5 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 95 | asia_equity_pca_method3_group50 | Asia equity principal component grouping using method 3 with 50 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 96 | asia_equity_pca_method4_group10 | Asia equity principal component grouping using method 4 with 10 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 97 | asia_equity_pca_method4_group2 | Asia equity principal component grouping using method 4 with 2 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 98 | asia_equity_pca_method4_group20 | Asia equity principal component grouping using method 4 with 20 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 99 | asia_equity_pca_method4_group5 | Asia equity principal component grouping using method 4 with 5 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 100 | asia_equity_pca_method4_group50 | Asia equity principal component grouping using method 4 with 50 clusters. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 101 | ex_japan_equity_pca_method1_group10 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 10 | 11 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 102 | ex_japan_equity_pca_method1_group2 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 103 | ex_japan_equity_pca_method1_group20 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 104 | ex_japan_equity_pca_method1_group5 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 105 | ex_japan_equity_pca_method1_group50 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT1, using 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 106 | ex_japan_equity_pca_method2_group10 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 107 | ex_japan_equity_pca_method2_group2 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 108 | ex_japan_equity_pca_method2_group20 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 109 | ex_japan_equity_pca_method2_group5 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 110 | ex_japan_equity_pca_method2_group50 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT2, using 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 111 | ex_japan_equity_pca_method3_group10 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 112 | ex_japan_equity_pca_method3_group2 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 113 | ex_japan_equity_pca_method3_group20 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 114 | ex_japan_equity_pca_method3_group5 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 115 | ex_japan_equity_pca_method3_group50 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT3, using 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 116 | ex_japan_equity_pca_method4_group10 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 117 | ex_japan_equity_pca_method4_group2 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 118 | ex_japan_equity_pca_method4_group20 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 119 | ex_japan_equity_pca_method4_group5 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 120 | ex_japan_equity_pca_method4_group50 | Categorical industry cluster label (robust, return-based) for Asia (ASI) in the EQY_XJPCI_Supported_MinVol10M universe, computed with method FACT4, using 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 121 | exjapan_minvol_method1_group10 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 122 | exjapan_minvol_method1_group2 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 123 | exjapan_minvol_method1_group20 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 124 | exjapan_minvol_method1_group5 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 125 | exjapan_minvol_method1_group50 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT1, with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 126 | exjapan_minvol_method2_group10 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 127 | exjapan_minvol_method2_group2 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 128 | exjapan_minvol_method2_group20 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 129 | exjapan_minvol_method2_group5 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 130 | exjapan_minvol_method2_group50 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT2, with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 131 | exjapan_minvol_method3_group10 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 132 | exjapan_minvol_method3_group2 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 133 | exjapan_minvol_method3_group20 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 134 | exjapan_minvol_method3_group5 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 135 | exjapan_minvol_method3_group50 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT3, with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 136 | exjapan_minvol_method4_group10 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 137 | exjapan_minvol_method4_group2 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 138 | exjapan_minvol_method4_group20 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 139 | exjapan_minvol_method4_group5 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 140 | exjapan_minvol_method4_group50 | Robust industry cluster assignment (integer label) for ASI region, universe EQY_XJPCI_Supported_MinVol1M, method FACT4, with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 141 | factor1_group10_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 10 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 20 | 48 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 142 | factor1_group20_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 20 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 15 | 21 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 143 | factor1_group2_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 2 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 13 | 17 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 144 | factor1_group50_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 50 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 8 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 145 | factor1_group5_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 5 groups, computed using the FACT1 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 12 | 20 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 146 | factor2_group10_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 10 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 16 | 17 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 147 | factor2_group20_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 20 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 9 | 13 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 148 | factor2_group2_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 2 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 12 | 13 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 149 | factor2_group50_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 50 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 9 | 11 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 150 | factor2_group5_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 5 groups, computed using the FACT2 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 9 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 151 | factor3_group10_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 10 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 12 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 152 | factor3_group20_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 20 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 153 | factor3_group2_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 2 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 154 | factor3_group50_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 50 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 11 | 13 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 155 | factor3_group5_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 5 groups, computed using the FACT3 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 156 | factor4_group10_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 10 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 157 | factor4_group20_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 20 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 158 | factor4_group2_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 2 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 159 | factor4_group50_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 50 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 160 | factor4_group5_top800_xjp_513 | Integer categorical label indicating the stock’s membership in a robust, return-based statistical industry cluster with 5 groups, computed using the FACT4 method for the TOP800 universe under the XJP_513 configuration | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 161 | factor_based_industry_method1_10clusters | Categorical label assigning the stock to one of 10 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 162 | factor_based_industry_method1_20clusters | Categorical label assigning the stock to one of 20 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 163 | factor_based_industry_method1_2clusters | Categorical label assigning the stock to one of 2 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 164 | factor_based_industry_method1_50clusters | Categorical label assigning the stock to one of 50 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 165 | factor_based_industry_method1_5clusters | Categorical label assigning the stock to one of 5 return-based statistical industry clusters for the TOP150 universe using the FACT1 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 166 | factor_based_industry_method2_10clusters | Categorical label assigning the stock to one of 10 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 167 | factor_based_industry_method2_20clusters | Categorical label assigning the stock to one of 20 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 168 | factor_based_industry_method2_2clusters | Categorical label assigning the stock to one of 2 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 169 | factor_based_industry_method2_50clusters | Categorical label assigning the stock to one of 50 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 170 | factor_based_industry_method2_5clusters | Categorical label assigning the stock to one of 5 return-based statistical industry clusters for the TOP150 universe using the FACT2 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 171 | factor_based_industry_method3_10clusters | Categorical label assigning the stock to one of 10 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 172 | factor_based_industry_method3_20clusters | Categorical label assigning the stock to one of 20 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 173 | factor_based_industry_method3_2clusters | Categorical label assigning the stock to one of 2 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 174 | factor_based_industry_method3_50clusters | Categorical label assigning the stock to one of 50 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 175 | factor_based_industry_method3_5clusters | Categorical label assigning the stock to one of 5 return-based statistical industry clusters for the TOP150 universe using the FACT3 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 176 | factor_based_industry_method4_10clusters | Categorical label assigning the stock to one of 10 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 177 | factor_based_industry_method4_20clusters | Categorical label assigning the stock to one of 20 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 178 | factor_based_industry_method4_2clusters | Categorical label assigning the stock to one of 2 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 179 | factor_based_industry_method4_50clusters | Categorical label assigning the stock to one of 50 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 180 | factor_based_industry_method4_5clusters | Categorical label assigning the stock to one of 5 return-based statistical industry clusters for the TOP150 universe using the FACT4 method (513 variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 181 | first_method_cluster10_all | Statistical robust industry cluster assignment; integer label indicating membership in 10 clusters using FACT1 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 8 | 12 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 182 | first_method_cluster5_all | Statistical robust industry cluster assignment; integer label indicating membership in 5 clusters using FACT1 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 183 | fourth_method_cluster10_all | Statistical robust industry cluster assignment; integer label indicating membership in 10 clusters using FACT4 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 184 | fourth_method_cluster5_all | Statistical robust industry cluster assignment; integer label indicating membership in 5 clusters using FACT4 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 185 | global_method1_group2 | Statistical robust industry cluster assignment; integer label indicating membership in 2 clusters using FACT1 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 10 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 186 | global_method1_group20 | Statistical robust industry cluster assignment; integer label indicating membership in 20 clusters using FACT1 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 187 | global_method1_group50 | Statistical robust industry cluster assignment; integer label indicating membership in 50 clusters using FACT1 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 188 | global_method2_group2 | Statistical robust industry cluster assignment; integer label indicating membership in 2 clusters using FACT2 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 189 | global_method2_group20 | Statistical robust industry cluster assignment; integer label indicating membership in 20 clusters using FACT2 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 10 | 13 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 190 | global_method2_group50 | Statistical robust industry cluster assignment; integer label indicating membership in 50 clusters using FACT2 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 9 | 13 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 191 | global_method3_group2 | Statistical robust industry cluster assignment; integer label indicating membership in 2 clusters using FACT3 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 8 | 9 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 192 | global_method3_group20 | Statistical robust industry cluster assignment; integer label indicating membership in 20 clusters using FACT3 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 193 | global_method3_group50 | Statistical robust industry cluster assignment; integer label indicating membership in 50 clusters using FACT3 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 194 | global_method4_group2 | Statistical robust industry cluster assignment; integer label indicating membership in 2 clusters using FACT4 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 195 | global_method4_group20 | Statistical robust industry cluster assignment; integer label indicating membership in 20 clusters using FACT4 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 196 | global_method4_group50 | Statistical robust industry cluster assignment; integer label indicating membership in 50 clusters using FACT4 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 197 | hk_equity_pca_method1_group2 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 198 | hk_equity_pca_method1_group20 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 15 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 199 | hk_equity_pca_method1_group5 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 200 | hk_equity_pca_method1_group50 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 201 | hk_equity_pca_method2_group10 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 202 | hk_equity_pca_method2_group2 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 203 | hk_equity_pca_method2_group20 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 204 | hk_equity_pca_method2_group50 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 205 | hk_equity_pca_method3_group10 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 206 | hk_equity_pca_method3_group2 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 207 | hk_equity_pca_method3_group5 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 208 | hk_equity_pca_method3_group50 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 209 | hk_equity_pca_method4_group10 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 210 | hk_equity_pca_method4_group20 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 211 | hk_equity_pca_method4_group5 | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 212 | hk_region_method1_grouping10 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 10 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 213 | hk_region_method1_grouping2 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 2 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 214 | hk_region_method1_grouping20 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 20 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 215 | hk_region_method1_grouping5 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 5 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 216 | hk_region_method1_grouping50 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT1 method with 50 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 217 | hk_region_method2_grouping10 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 10 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 218 | hk_region_method2_grouping2 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 2 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 219 | hk_region_method2_grouping20 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 20 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 220 | hk_region_method2_grouping5 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 5 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 221 | hk_region_method2_grouping50 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT2 method with 50 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 222 | hk_region_method3_grouping10 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 10 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 223 | hk_region_method3_grouping2 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 2 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 224 | hk_region_method3_grouping20 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 20 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 225 | hk_region_method3_grouping5 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 5 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 226 | hk_region_method3_grouping50 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT3 method with 50 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 227 | hk_region_method4_grouping10 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 10 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 228 | hk_region_method4_grouping2 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 2 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 229 | hk_region_method4_grouping20 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 20 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 230 | hk_region_method4_grouping5 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 5 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 231 | hk_region_method4_grouping50 | Categorical robust industry cluster label for Hong Kong (HKG) TOP500 universe under ASI_HKG_TOP500Robust, using FACT4 method with 50 clusters; integer indicating cluster membership | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 232 | hk_top200_pca_method1_group10 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 233 | hk_top200_pca_method1_group2 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 234 | hk_top200_pca_method1_group20 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 235 | hk_top200_pca_method1_group5 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 236 | hk_top200_pca_method1_group50 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT1 method with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 237 | hk_top200_pca_method2_group10 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 238 | hk_top200_pca_method2_group2 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 239 | hk_top200_pca_method2_group20 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 240 | hk_top200_pca_method2_group5 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 241 | hk_top200_pca_method2_group50 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT2 method with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 242 | hk_top200_pca_method3_group10 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 243 | hk_top200_pca_method3_group2 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 244 | hk_top200_pca_method3_group20 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 245 | hk_top200_pca_method3_group5 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 246 | hk_top200_pca_method3_group50 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT3 method with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 247 | hk_top200_pca_method4_group10 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 248 | hk_top200_pca_method4_group2 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 249 | hk_top200_pca_method4_group20 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 250 | hk_top200_pca_method4_group5 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 251 | hk_top200_pca_method4_group50 | Categorical robust industry cluster label for the Hong Kong TOP200 universe using the FACT4 method with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 252 | hongkong_pca_grouping_method1_10clusters | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT1 method with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 253 | hongkong_pca_grouping_method2_5clusters | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT2 method with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 254 | hongkong_pca_grouping_method3_20clusters | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT3 method with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 255 | hongkong_pca_grouping_method4_2clusters | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 256 | hongkong_pca_grouping_method4_50clusters | Categorical robust industry cluster label for Hong Kong (HKG) TOP800 stocks in Asia, using FACT4 method with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 257 | india_top500_method1_group10 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 56 | 134 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 258 | india_top500_method1_group2 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 28 | 50 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 259 | india_top500_method1_group20 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 26 | 84 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 260 | india_top500_method1_group5 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 14 | 37 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 261 | india_top500_method1_group50 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 25 | 58 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 262 | india_top500_method2_group10 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 15 | 39 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 263 | india_top500_method2_group2 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 15 | 44 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 264 | india_top500_method2_group20 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 21 | 71 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 265 | india_top500_method2_group5 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 13 | 67 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 266 | india_top500_method2_group50 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 20 | 41 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 267 | india_top500_method3_group10 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 21 | 67 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 268 | india_top500_method3_group2 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 11 | 30 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 269 | india_top500_method3_group20 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 12 | 46 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 270 | india_top500_method3_group5 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 10 | 35 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 271 | india_top500_method3_group50 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 12 | 44 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 272 | india_top500_method4_group10 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 14 | 25 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 273 | india_top500_method4_group2 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 13 | 27 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 274 | india_top500_method4_group20 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 25 | 60 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 275 | india_top500_method4_group5 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 13 | 24 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 276 | india_top500_method4_group50 | statistical industry classification | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 28 | 63 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 277 | jp_minvol_method1_grouping10 | Integer-coded categorical label assigning each stock to one of 10 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 8 | 23 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 278 | jp_minvol_method1_grouping2 | Integer-coded categorical label assigning each stock to one of 2 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 8 | 11 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 279 | jp_minvol_method1_grouping20 | Integer-coded categorical label assigning each stock to one of 20 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 9 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 280 | jp_minvol_method1_grouping5 | Integer-coded categorical label assigning each stock to one of 5 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 6 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 281 | jp_minvol_method1_grouping50 | Integer-coded categorical label assigning each stock to one of 50 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 282 | jp_minvol_method2_grouping10 | Integer-coded categorical label assigning each stock to one of 10 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 283 | jp_minvol_method2_grouping2 | Integer-coded categorical label assigning each stock to one of 2 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 284 | jp_minvol_method2_grouping20 | Integer-coded categorical label assigning each stock to one of 20 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 285 | jp_minvol_method2_grouping5 | Integer-coded categorical label assigning each stock to one of 5 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 11 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 286 | jp_minvol_method2_grouping50 | Integer-coded categorical label assigning each stock to one of 50 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 287 | jp_minvol_method3_grouping10 | Integer-coded categorical label assigning each stock to one of 10 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 288 | jp_minvol_method3_grouping2 | Integer-coded categorical label assigning each stock to one of 2 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 289 | jp_minvol_method3_grouping20 | Integer-coded categorical label assigning each stock to one of 20 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 290 | jp_minvol_method3_grouping5 | Integer-coded categorical label assigning each stock to one of 5 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 291 | jp_minvol_method3_grouping50 | Integer-coded categorical label assigning each stock to one of 50 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 9 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 292 | jp_minvol_method4_grouping10 | Integer-coded categorical label assigning each stock to one of 10 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 293 | jp_minvol_method4_grouping2 | Integer-coded categorical label assigning each stock to one of 2 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 294 | jp_minvol_method4_grouping20 | Integer-coded categorical label assigning each stock to one of 20 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 295 | jp_minvol_method4_grouping5 | Integer-coded categorical label assigning each stock to one of 5 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 296 | jp_minvol_method4_grouping50 | Integer-coded categorical label assigning each stock to one of 50 robust industry clusters for the Asia region EQY_JPP_Supported_MinVol10M universe, using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 9 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 297 | minvol_pca_grouping_method1_10clusters | Industry group assignment using first PCA method and 10 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 298 | minvol_pca_grouping_method1_20clusters | Industry group assignment using first PCA method and 20 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 299 | minvol_pca_grouping_method1_2clusters | Industry group assignment using first PCA method and 2 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 300 | minvol_pca_grouping_method1_50clusters | Industry group assignment using first PCA method and 50 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 301 | minvol_pca_grouping_method1_5clusters | Industry group assignment using first PCA method and 5 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 302 | minvol_pca_grouping_method2_10clusters | Industry group assignment using second PCA method and 10 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 303 | minvol_pca_grouping_method2_20clusters | Industry group assignment using second PCA method and 20 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 304 | minvol_pca_grouping_method2_2clusters | Industry group assignment using second PCA method and 2 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 305 | minvol_pca_grouping_method2_50clusters | Industry group assignment using second PCA method and 50 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 306 | minvol_pca_grouping_method2_5clusters | Industry group assignment using second PCA method and 5 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 307 | minvol_pca_grouping_method3_10clusters | Industry group assignment using third PCA method and 10 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 308 | minvol_pca_grouping_method3_20clusters | Industry group assignment using third PCA method and 20 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 309 | minvol_pca_grouping_method3_2clusters | Industry group assignment using third PCA method and 2 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 310 | minvol_pca_grouping_method3_50clusters | Industry group assignment using third PCA method and 50 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 311 | minvol_pca_grouping_method3_5clusters | Industry group assignment using third PCA method and 5 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 312 | minvol_pca_grouping_method4_10clusters | Industry group assignment using fourth PCA method and 10 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 313 | minvol_pca_grouping_method4_20clusters | Industry group assignment using fourth PCA method and 20 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 314 | minvol_pca_grouping_method4_2clusters | Industry group assignment using fourth PCA method and 2 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 315 | minvol_pca_grouping_method4_50clusters | Industry group assignment using fourth PCA method and 50 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 316 | minvol_pca_grouping_method4_5clusters | Industry group assignment using fourth PCA method and 5 clusters for minimum volatility universe. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 317 | pca_industry_grouping_method1_10clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 10 clusters using the FACT1 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 318 | pca_industry_grouping_method1_20clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 20 clusters using the FACT1 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 319 | pca_industry_grouping_method1_2clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 2 clusters using the FACT1 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 320 | pca_industry_grouping_method1_50clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 50 clusters using the FACT1 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 321 | pca_industry_grouping_method1_5clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 5 clusters using the FACT1 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 322 | pca_industry_grouping_method2_10clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 10 clusters using the FACT2 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 323 | pca_industry_grouping_method2_20clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 20 clusters using the FACT2 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 324 | pca_industry_grouping_method2_2clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 2 clusters using the FACT2 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 325 | pca_industry_grouping_method2_50clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 50 clusters using the FACT2 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 326 | pca_industry_grouping_method2_5clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 5 clusters using the FACT2 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 327 | pca_industry_grouping_method3_10clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 10 clusters using the FACT3 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 328 | pca_industry_grouping_method3_20clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 20 clusters using the FACT3 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 329 | pca_industry_grouping_method3_2clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 2 clusters using the FACT3 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 330 | pca_industry_grouping_method3_50clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 50 clusters using the FACT3 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 331 | pca_industry_grouping_method3_5clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 5 clusters using the FACT3 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 332 | pca_industry_grouping_method4_10clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 10 clusters using the FACT4 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 333 | pca_industry_grouping_method4_20clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 20 clusters using the FACT4 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 334 | pca_industry_grouping_method4_2clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 2 clusters using the FACT4 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 335 | pca_industry_grouping_method4_50clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 50 clusters using the FACT4 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 336 | pca_industry_grouping_method4_5clusters | Return-based robust industry cluster membership (categorical) assigning each Korea TOP600 (TRD) stock to one of 5 clusters using the FACT4 variant within the ASI_KOR module | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 337 | principal_component_0_all513 | Continuous loading on the 1st robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 14 | 17 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 338 | principal_component_0_top1200_xjp_513 | First principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 339 | principal_component_10_all513 | Continuous loading on the 11th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 7 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 340 | principal_component_10_top1200_xjp_513 | Eleventh principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 341 | principal_component_11_all513 | Continuous loading on the 12th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 342 | principal_component_11_top1200_xjp_513 | Twelfth principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 343 | principal_component_12_all513 | Continuous loading on the 13th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 8 | 9 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 344 | principal_component_12_top1200_xjp_513 | Thirteenth principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 345 | principal_component_13_all513 | Continuous loading on the 14th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 6 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 346 | principal_component_13_top1200_xjp_513 | Fourteenth principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 347 | principal_component_14_all513 | Continuous loading on the 15th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 4 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 348 | principal_component_14_top1200_xjp_513 | Fifteenth principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 349 | principal_component_1_all513 | Continuous loading on the 2nd robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 14 | 15 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 350 | principal_component_1_top1200_xjp_513 | Second principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 351 | principal_component_2_all513 | Continuous loading on the 3rd robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 19 | 24 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 352 | principal_component_2_top1200_xjp_513 | Third principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 353 | principal_component_3_all513 | Continuous loading on the 4th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 15 | 17 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 354 | principal_component_3_top1200_xjp_513 | Fourth principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 355 | principal_component_4_all513 | Continuous loading on the 5th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 35 | 39 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 356 | principal_component_4_top1200_xjp_513 | Fifth principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 357 | principal_component_5_all513 | Continuous loading on the 6th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 10 | 10 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 358 | principal_component_5_top1200_xjp_513 | Sixth principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 359 | principal_component_6_all513 | Continuous loading on the 7th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 11 | 11 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 360 | principal_component_6_top1200_xjp_513 | Seventh principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 361 | principal_component_7_all513 | Continuous loading on the 8th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 19 | 25 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 362 | principal_component_7_top1200_xjp_513 | Eighth principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 363 | principal_component_8_all513 | Continuous loading on the 9th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 13 | 14 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 364 | principal_component_8_top1200_xjp_513 | Ninth principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 365 | principal_component_9_all513 | Continuous loading on the 10th robust principal component (statistical industry factor) for the ALL universe, computed with the 513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9426 | 6 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 366 | principal_component_9_top1200_xjp_513 | Tenth principal component value for the top 1200 ex-Japan securities in group 513. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.3788 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 367 | principal_component_grouping_method1_10clusters | Integer cluster label assigning the stock to one of 10 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 368 | principal_component_grouping_method1_20clusters | Integer cluster label assigning the stock to one of 20 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 369 | principal_component_grouping_method1_2clusters_2 | Integer cluster label assigning the stock to one of 2 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 370 | principal_component_grouping_method1_50clusters | Integer cluster label assigning the stock to one of 50 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 371 | principal_component_grouping_method1_5clusters | Integer cluster label assigning the stock to one of 5 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 372 | principal_component_grouping_method2_10clusters | Integer cluster label assigning the stock to one of 10 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 373 | principal_component_grouping_method2_20clusters | Integer cluster label assigning the stock to one of 20 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 374 | principal_component_grouping_method2_2clusters_2 | Integer cluster label assigning the stock to one of 2 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 375 | principal_component_grouping_method2_50clusters_2 | Integer cluster label assigning the stock to one of 50 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 376 | principal_component_grouping_method2_5clusters | Integer cluster label assigning the stock to one of 5 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 377 | principal_component_grouping_method3_10clusters | Integer label assigning the stock to one of 10 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 378 | principal_component_grouping_method3_10clusters_2 | Integer cluster label assigning the stock to one of 10 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 379 | principal_component_grouping_method3_20clusters | Integer label assigning the stock to one of 20 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 380 | principal_component_grouping_method3_20clusters_2 | Integer cluster label assigning the stock to one of 20 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 381 | principal_component_grouping_method3_2clusters | Integer cluster label assigning the stock to one of 2 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 382 | principal_component_grouping_method3_50clusters | Integer label assigning the stock to one of 50 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 383 | principal_component_grouping_method3_50clusters_3 | Integer cluster label assigning the stock to one of 50 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 384 | principal_component_grouping_method3_5clusters | Integer cluster label assigning the stock to one of 5 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 385 | principal_component_grouping_method4_10clusters | Integer label assigning the stock to one of 10 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 386 | principal_component_grouping_method4_10clusters_2 | Integer cluster label assigning the stock to one of 10 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 387 | principal_component_grouping_method4_20clusters | Integer label assigning the stock to one of 20 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 388 | principal_component_grouping_method4_20clusters_3 | Integer cluster label assigning the stock to one of 20 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 389 | principal_component_grouping_method4_2clusters | Integer label assigning the stock to one of 2 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 390 | principal_component_grouping_method4_2clusters_3 | Integer cluster label assigning the stock to one of 2 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 391 | principal_component_grouping_method4_50clusters | Integer label assigning the stock to one of 50 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 392 | principal_component_grouping_method4_50clusters_2 | Integer cluster label assigning the stock to one of 50 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 393 | principal_component_grouping_method4_5clusters | Integer label assigning the stock to one of 5 return-based statistical industry clusters for the TOP1500 universe using the FACT4 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 394 | principal_component_grouping_method4_5clusters_2 | Integer cluster label assigning the stock to one of 5 statistically derived industry clusters for the TOP400 universe, computed with the robust XJP_513 configuration using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 395 | robust_factor_component_0 | First robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 24 | 28 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 396 | robust_factor_component_1 | Second robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 12 | 16 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 397 | robust_factor_component_10 | Eleventh robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 9 | 10 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 398 | robust_factor_component_11 | Twelfth robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 399 | robust_factor_component_12 | Thirteenth robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 12 | 17 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 400 | robust_factor_component_13 | Fourteenth robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 6 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 401 | robust_factor_component_14 | Fifteenth robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 11 | 13 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 402 | robust_factor_component_2 | Third robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 6 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 403 | robust_factor_component_3 | Fourth robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 404 | robust_factor_component_4 | Fifth robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 10 | 11 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 405 | robust_factor_component_5 | Sixth robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 5 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 406 | robust_factor_component_6 | Seventh robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 11 | 11 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 407 | robust_factor_component_7 | Eighth robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 9 | 10 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 408 | robust_factor_component_8 | Ninth robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 11 | 15 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 409 | robust_factor_component_9 | Tenth robust factor derived from principal component analysis of returns. | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 0.9282 | 3 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 410 | second_method_cluster10_all | Statistical robust industry cluster assignment; integer label indicating membership in 10 clusters using FACT2 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 16 | 32 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 411 | second_method_cluster5_all | Statistical robust industry cluster assignment; integer label indicating membership in 5 clusters using FACT2 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 17 | 29 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 412 | taiwan_equity_pca_method1_group10 | Categorical cluster label assigning each stock to one of 10 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 10 | 37 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 413 | taiwan_equity_pca_method1_group2 | Categorical cluster label assigning each stock to one of 2 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 10 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 414 | taiwan_equity_pca_method1_group50 | Categorical cluster label assigning each stock to one of 50 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 7 | 9 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 415 | taiwan_equity_pca_method3_group20 | Categorical cluster label assigning each stock to one of 20 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 12 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 416 | taiwan_equity_pca_method4_group2 | Categorical cluster label assigning each stock to one of 2 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 417 | taiwan_top100_method1_group2 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 418 | taiwan_top100_method1_group20 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 419 | taiwan_top100_method1_group50 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 420 | taiwan_top100_method2_group10 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 421 | taiwan_top100_method2_group2 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 422 | taiwan_top100_method2_group20 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 423 | taiwan_top100_method2_group50 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 424 | taiwan_top100_method3_group2 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 425 | taiwan_top100_method3_group5 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 426 | taiwan_top100_method3_group50 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 427 | taiwan_top100_method4_group10 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 4 | 6 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 428 | taiwan_top100_method4_group20 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 429 | taiwan_top100_method4_group5 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 5 | 5 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 430 | taiwan_top300_method1_group20 | Categorical cluster label assigning each stock to one of 20 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 431 | taiwan_top300_method1_group5 | Categorical cluster label assigning each stock to one of 5 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT1 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 432 | taiwan_top300_method2_group10 | Categorical cluster label assigning each stock to one of 10 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 433 | taiwan_top300_method2_group2 | Categorical cluster label assigning each stock to one of 2 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 434 | taiwan_top300_method2_group20 | Categorical cluster label assigning each stock to one of 20 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 435 | taiwan_top300_method2_group5 | Categorical cluster label assigning each stock to one of 5 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 436 | taiwan_top300_method2_group50 | Categorical cluster label assigning each stock to one of 50 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT2 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 437 | taiwan_top300_method3_group10 | Categorical cluster label assigning each stock to one of 10 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 438 | taiwan_top300_method3_group2 | Categorical cluster label assigning each stock to one of 2 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 439 | taiwan_top300_method3_group5 | Categorical cluster label assigning each stock to one of 5 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 440 | taiwan_top300_method3_group50 | Categorical cluster label assigning each stock to one of 50 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT3 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 441 | taiwan_top300_method4_group10 | Categorical cluster label assigning each stock to one of 10 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 442 | taiwan_top300_method4_group20 | Categorical cluster label assigning each stock to one of 20 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 443 | taiwan_top300_method4_group5 | Categorical cluster label assigning each stock to one of 5 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 444 | taiwan_top300_method4_group50 | Categorical cluster label assigning each stock to one of 50 robust, return-based industry clusters for the Taiwan TOP300 universe (Asia), computed using the FACT4 method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 445 | taiwan_top500_method1_group10 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 446 | taiwan_top500_method1_group2 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 447 | taiwan_top500_method1_group20 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 448 | taiwan_top500_method1_group5 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 449 | taiwan_top500_method1_group50 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT1 method, with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 450 | taiwan_top500_method2_group10 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 451 | taiwan_top500_method2_group2 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 452 | taiwan_top500_method2_group20 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 453 | taiwan_top500_method2_group5 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 454 | taiwan_top500_method2_group50_500 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT2 method, with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 455 | taiwan_top500_method3_group10 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 456 | taiwan_top500_method3_group2 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 457 | taiwan_top500_method3_group20 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 458 | taiwan_top500_method3_group5 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 459 | taiwan_top500_method3_group50 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT3 method, with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 460 | taiwan_top500_method4_group10_500 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 461 | taiwan_top500_method4_group2 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 462 | taiwan_top500_method4_group20 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 7 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 463 | taiwan_top500_method4_group5 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 464 | taiwan_top500_method4_group50 | Robust return-based industry cluster assignment label for Taiwan TOP500 universe (Asia module) using FACT4 method, with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 4 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 465 | third_method_cluster10_all | Statistical robust industry cluster assignment; integer label indicating membership in 10 clusters using FACT3 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 15 | 37 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 466 | third_method_cluster5_all | Statistical robust industry cluster assignment; integer label indicating membership in 5 clusters using FACT3 method for the ALL universe under the _513 variant | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 9 | 11 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 467 | top1000_pca_factor1_grouping10 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 10 robust, return-based industry groups using the FACT1 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 468 | top1000_pca_factor1_grouping2 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 2 robust, return-based industry groups using the FACT1 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 469 | top1000_pca_factor1_grouping20 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 20 robust, return-based industry groups using the FACT1 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 470 | top1000_pca_factor1_grouping5 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 5 robust, return-based industry groups using the FACT1 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 471 | top1000_pca_factor1_grouping50 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 50 robust, return-based industry groups using the FACT1 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 472 | top1000_pca_factor2_grouping10 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 10 robust, return-based industry groups using the FACT2 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 473 | top1000_pca_factor2_grouping2 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 2 robust, return-based industry groups using the FACT2 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 474 | top1000_pca_factor2_grouping20 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 20 robust, return-based industry groups using the FACT2 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 475 | top1000_pca_factor2_grouping5 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 5 robust, return-based industry groups using the FACT2 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 476 | top1000_pca_factor2_grouping50 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 50 robust, return-based industry groups using the FACT2 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 477 | top1000_pca_factor3_grouping10 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 10 robust, return-based industry groups using the FACT3 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 478 | top1000_pca_factor3_grouping2 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 2 robust, return-based industry groups using the FACT3 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 479 | top1000_pca_factor3_grouping20 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 20 robust, return-based industry groups using the FACT3 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 480 | top1000_pca_factor3_grouping5 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 5 robust, return-based industry groups using the FACT3 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 481 | top1000_pca_factor3_grouping50 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 50 robust, return-based industry groups using the FACT3 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 482 | top1000_pca_factor4_grouping10 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 10 robust, return-based industry groups using the FACT4 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 483 | top1000_pca_factor4_grouping2 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 2 robust, return-based industry groups using the FACT4 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 484 | top1000_pca_factor4_grouping20 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 20 robust, return-based industry groups using the FACT4 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 485 | top1000_pca_factor4_grouping5 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 5 robust, return-based industry groups using the FACT4 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 486 | top1000_pca_factor4_grouping50 | Categorical cluster ID assigning each stock in the TOP1000 universe to one of 50 robust, return-based industry groups using the FACT4 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 487 | top1500_pca_factor1_grouping10 | Integer label assigning the stock to one of 10 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 488 | top1500_pca_factor1_grouping2 | Integer label assigning the stock to one of 2 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 489 | top1500_pca_factor1_grouping20 | Integer label assigning the stock to one of 20 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 490 | top1500_pca_factor1_grouping5 | Integer label assigning the stock to one of 5 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 491 | top1500_pca_factor1_grouping50 | Integer label assigning the stock to one of 50 return-based statistical industry clusters for the TOP1500 universe using the FACT1 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 492 | top1500_pca_factor2_grouping10 | Integer label assigning the stock to one of 10 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 493 | top1500_pca_factor2_grouping2 | Integer label assigning the stock to one of 2 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 494 | top1500_pca_factor2_grouping20 | Integer label assigning the stock to one of 20 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 495 | top1500_pca_factor2_grouping5 | Integer label assigning the stock to one of 5 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 496 | top1500_pca_factor2_grouping50 | Integer label assigning the stock to one of 50 return-based statistical industry clusters for the TOP1500 universe using the FACT2 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 497 | top1500_pca_factor3_grouping2 | Integer label assigning the stock to one of 2 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 498 | top1500_pca_factor3_grouping5 | Integer label assigning the stock to one of 5 return-based statistical industry clusters for the TOP1500 universe using the FACT3 robust method | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 499 | top500_industry_grouping_method1_10 | Categorical industry cluster label assigning each TOP500 stock to 1 of 10 clusters using the robust FACT1 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 500 | top500_industry_grouping_method1_2 | Categorical industry cluster label assigning each TOP500 stock to 1 of 2 clusters using the robust FACT1 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 501 | top500_industry_grouping_method1_20 | Categorical industry cluster label assigning each TOP500 stock to 1 of 20 clusters using the robust FACT1 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 502 | top500_industry_grouping_method1_5 | Categorical industry cluster label assigning each TOP500 stock to 1 of 5 clusters using the robust FACT1 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 503 | top500_industry_grouping_method1_50 | Categorical industry cluster label assigning each TOP500 stock to 1 of 50 clusters using the robust FACT1 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 504 | top500_industry_grouping_method2_10 | Categorical industry cluster label assigning each TOP500 stock to 1 of 10 clusters using the robust FACT2 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 505 | top500_industry_grouping_method2_2 | Categorical industry cluster label assigning each TOP500 stock to 1 of 2 clusters using the robust FACT2 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 506 | top500_industry_grouping_method2_20 | Categorical industry cluster label assigning each TOP500 stock to 1 of 20 clusters using the robust FACT2 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 507 | top500_industry_grouping_method2_5 | Categorical industry cluster label assigning each TOP500 stock to 1 of 5 clusters using the robust FACT2 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 508 | top500_industry_grouping_method2_50 | Categorical industry cluster label assigning each TOP500 stock to 1 of 50 clusters using the robust FACT2 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 8 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 509 | top500_industry_grouping_method3_10 | Categorical industry cluster label assigning each TOP500 stock to 1 of 10 clusters using the robust FACT3 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 510 | top500_industry_grouping_method3_2 | Categorical industry cluster label assigning each TOP500 stock to 1 of 2 clusters using the robust FACT3 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 511 | top500_industry_grouping_method3_20 | Categorical industry cluster label assigning each TOP500 stock to 1 of 20 clusters using the robust FACT3 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 512 | top500_industry_grouping_method3_5 | Categorical industry cluster label assigning each TOP500 stock to 1 of 5 clusters using the robust FACT3 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 513 | top500_industry_grouping_method3_50 | Categorical industry cluster label assigning each TOP500 stock to 1 of 50 clusters using the robust FACT3 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 514 | top500_industry_grouping_method4_10 | Categorical industry cluster label assigning each TOP500 stock to 1 of 10 clusters using the robust FACT4 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 515 | top500_industry_grouping_method4_2 | Categorical industry cluster label assigning each TOP500 stock to 1 of 2 clusters using the robust FACT4 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 516 | top500_industry_grouping_method4_20 | Categorical industry cluster label assigning each TOP500 stock to 1 of 20 clusters using the robust FACT4 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 0 | 0 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 517 | top500_industry_grouping_method4_5 | Categorical industry cluster label assigning each TOP500 stock to 1 of 5 clusters using the robust FACT4 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 518 | top500_industry_grouping_method4_50 | Categorical industry cluster label assigning each TOP500 stock to 1 of 50 clusters using the robust FACT4 method (513-window variant) | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 519 | tw_region_method1_grouping10 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 520 | tw_region_method1_grouping5 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT1 method with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 521 | tw_region_method2_grouping5 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT2 method with 5 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 522 | tw_region_method3_grouping10 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 10 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 1 | 1 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 523 | tw_region_method3_grouping20 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT3 method with 20 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 3 | 3 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 524 | tw_region_method4_grouping2 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 2 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |
| 525 | tw_region_method4_grouping50 | Robust industry cluster assignment (integer label) for the Taiwan TOP100 universe in Asia using the FACT4 method with 50 clusters | {'id': 'pv30', 'name': 'Alternate Industry Classification'} | {'id': 'pv', 'name': 'Price Volume'} | {'id': 'pv-price-volume', 'name': 'Price Volume'} | IND | 1 | TOP500 | MATRIX | 0.9507 | 1.0 | 2 | 2 | 1.2 | [] | pv30 | Alternate Industry Classification | pv | Price Volume | pv-price-volume | Price Volume |