{ "regression_neut(divide(, sqrt()), log(cap))": { "template_explanation": "A direct variation of the seed alpha that transforms a generic investment\u2011quality metric using a square\u2011root compression and then removes any linear size effect via regression neutralization against log market cap. This isolates the residual quality signal that is orthogonal to firm size.", "seed_alpha_settings": { "instrumentType": "EQUITY", "region": "IND", "universe": "TOP500", "delay": 1, "decay": 6, "neutralization": "NONE", "truncation": 0.02, "pasteurization": "ON", "unitHandling": "VERIFY", "nanHandling": "ON", "maxTrade": "ON", "maxPosition": "OFF", "language": "FASTEXPR", "visualization": false, "startDate": "2014-01-01", "endDate": "2023-12-31" }, "placeholder_candidates": { "": { "type": "data_field", "candidates": [] } } }, "group_neutralize(ts_rank(, ), bucket(rank(cap), range=\"0,1,0.1\"))": { "template_explanation": "Constructs a time\u2011series rank of a fundamental metric over a short window, then removes the systematic size bias by neutralising against market\u2011cap buckets. The result is a size\u2011adjusted relative strength signal that can be compared across the universe.", "seed_alpha_settings": { "instrumentType": "EQUITY", "region": "IND", "universe": "TOP500", "delay": 1, "decay": 6, "neutralization": "NONE", "truncation": 0.02, "pasteurization": "ON", "unitHandling": "VERIFY", "nanHandling": "ON", "maxTrade": "ON", "maxPosition": "OFF", "language": "FASTEXPR", "visualization": false, "startDate": "2014-01-01", "endDate": "2023-12-31" }, "placeholder_candidates": { "": { "type": "data_field", "candidates": [] }, "": { "type": "integer_parameter", "candidates": [ { "value": 5 }, { "value": 10 }, { "value": 20 }, { "value": 60 }, { "value": 120 } ] } } }, "regression_neut(signed_power(ts_zscore(, ), ), log(cap))": { "template_explanation": "Standardises the investment metric with a rolling Z\u2011score, applies a signed power transformation to capture non\u2011linear relationships, and finally neutralises the effect of log market cap. This approach enhances sensitivity to extreme values while controlling for size.", "seed_alpha_settings": { "instrumentType": "EQUITY", "region": "IND", "universe": "TOP500", "delay": 1, "decay": 6, "neutralization": "NONE", "truncation": 0.02, "pasteurization": "ON", "unitHandling": "VERIFY", "nanHandling": "ON", "maxTrade": "ON", "maxPosition": "OFF", "language": "FASTEXPR", "visualization": false, "startDate": "2014-01-01", "endDate": "2023-12-31" }, "placeholder_candidates": { "": { "type": "data_field", "candidates": [] }, "": { "type": "integer_parameter", "candidates": [ { "value": 5 }, { "value": 20 }, { "value": 60 }, { "value": 120 }, { "value": 252 } ] }, "": { "type": "float_parameter", "candidates": [ { "value": 0.25 }, { "value": 0.5 }, { "value": 1.0 }, { "value": 2.0 }, { "value": 3.0 } ] } } }, "ts_zscore(ts_delta(, ), ) - regression_neut(, log(cap))": { "template_explanation": "Combines the short\u2011term change of a metric (captured by its rolling delta and Z\u2011score) with the size\u2011neutralised level of the metric. The difference isolates momentum in the metric that is not explained by firm size.", "seed_alpha_settings": { "instrumentType": "EQUITY", "region": "IND", "universe": "TOP500", "delay": 1, "decay": 6, "neutralization": "NONE", "truncation": 0.02, "pasteurization": "ON", "unitHandling": "VERIFY", "nanHandling": "ON", "maxTrade": "ON", "maxPosition": "OFF", "language": "FASTEXPR", "visualization": false, "startDate": "2014-01-01", "endDate": "2023-12-31" }, "placeholder_candidates": { "": { "type": "data_field", "candidates": [ { "id": "anl39_atanbvps", "description": "Book value (tangible) per share - most recent fiscal year" }, { "id": "anl39_qtanbvps", "description": "Book value (tangible) per share - most recent quarter" }, { "id": "anl39_spvba", "description": "Book value (Common Equity) per share - most recent fiscal year" }, { "id": "anl39_spvbq", "description": "Book value (Common Equity) per share - most recent quarter" }, { "id": "anl4_bvps_high", "description": "Book value - the highest estimation, per share" }, { "id": "anl4_bvps_low", "description": "Book value - the lowest estimation, per share" }, { "id": "anl4_bvps_median", "description": "Book value per share - Median value among forecasts" }, { "id": "anl4_bvps_number", "description": "Book value per share - number of estimations" }, { "id": "est_bookvalue_ps", "description": "Book value per share - average of estimations" } ] }, "": { "type": "integer_parameter", "candidates": [ { "value": 5 }, { "value": 10 }, { "value": 20 }, { "value": 60 }, { "value": 120 } ] } } }, "group_rank(ts_rank(, ), industry)": { "template_explanation": "First ranks the metric temporally within each stock, then applies a cross\u2011sectional industry ranking. This double\u2011ranking approach extracts the industry\u2011relative performance trend while abstracting from absolute magnitude.", "seed_alpha_settings": { "instrumentType": "EQUITY", "region": "IND", "universe": "TOP500", "delay": 1, "decay": 6, "neutralization": "NONE", "truncation": 0.02, "pasteurization": "ON", "unitHandling": "VERIFY", "nanHandling": "ON", "maxTrade": "ON", "maxPosition": "OFF", "language": "FASTEXPR", "visualization": false, "startDate": "2014-01-01", "endDate": "2023-12-31" }, "placeholder_candidates": { "": { "type": "data_field", "candidates": [] }, "": { "type": "integer_parameter", "candidates": [ { "value": 10 }, { "value": 20 }, { "value": 60 }, { "value": 120 }, { "value": 252 } ] } } } }