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Jack 1 month ago
parent da451dc684
commit a7cf93946a
2 changed files with 38 additions and 58 deletions
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  1. 2
      load_data_sets.py
  2. 94
      optimize_prompt.txt

2
load_data_sets.py
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@ -18,7 +18,7 @@ try:
# 创建游标 # 创建游标
cur = conn.cursor() cur = conn.cursor()
keys = ["pcr_oi_90", "implied_volatility_call_90", "implied_volatility_put_90", "pcr_oi_270", "implied_volatility_call_270", "implied_volatility_put_270", "volume", "open_interest", "volume_call", "volume_put", "open_interest_call", "open_interest_put", "close", "vwap", "adv20", "market_cap", "subindustry", "sector", "industry"] keys = ["implied_volatility_call_90", "implied_volatility_put_90", "implied_volatility_call_30", "implied_volatility_put_30", "implied_volatility_call_60", "implied_volatility_put_60", "close", "volume", "adv20", "low", "high"]
for key in keys: for key in keys:
# SQL 查询语句 # SQL 查询语句

94
optimize_prompt.txt
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@ -1,7 +1,7 @@
trade_when(pcr_oi_270 < 1, (implied_volatility_call_270-implied_volatility_put_270), -1) -ts_sum(reverse(subtract(implied_volatility_call_90, implied_volatility_put_90)), 5)
我需要优化这个因子, 现在模拟之后的结果是 我需要优化这个因子, 现在模拟之后的结果是
{ {
"id": "KPRvr7YN", "id": "gJzgYd7J",
"type": "REGULAR", "type": "REGULAR",
"author": "YC93384", "author": "YC93384",
"settings": { "settings": {
@ -9,8 +9,8 @@ trade_when(pcr_oi_270 < 1, (implied_volatility_call_270-implied_volatility_put_2
"region": "USA", "region": "USA",
"universe": "TOP3000", "universe": "TOP3000",
"delay": 1, "delay": 1,
"decay": 4, "decay": 0,
"neutralization": "SUBINDUSTRY", "neutralization": "INDUSTRY",
"truncation": 0.08, "truncation": 0.08,
"pasteurization": "ON", "pasteurization": "ON",
"unitHandling": "VERIFY", "unitHandling": "VERIFY",
@ -19,75 +19,79 @@ trade_when(pcr_oi_270 < 1, (implied_volatility_call_270-implied_volatility_put_2
"language": "FASTEXPR", "language": "FASTEXPR",
"visualization": false, "visualization": false,
"startDate": "2018-01-20", "startDate": "2018-01-20",
"endDate": "2023-01-20", "endDate": "2023-01-20"
"testPeriod": "P1Y"
}, },
"regular": { "regular": {
"code": "trade_when(pcr_oi_90 < ts_mean(pcr_oi_90, 126), subtract(implied_volatility_call_90, implied_volatility_put_90), -1)", "code": "-ts_sum(reverse(subtract(implied_volatility_call_90, implied_volatility_put_90)), 5)",
"description": null, "description": null,
"operatorCount": 4 "operatorCount": 4
}, },
"dateCreated": "2025-12-21T03:10:00-05:00", "dateCreated": "2025-12-21T05:31:30-05:00",
"dateSubmitted": null, "dateSubmitted": null,
"dateModified": "2025-12-21T03:10:00-05:00", "dateModified": "2025-12-21T05:31:31-05:00",
"name": null, "name": null,
"favorite": false, "favorite": false,
"hidden": false, "hidden": false,
"color": null, "color": null,
"category": null, "category": null,
"tags": [], "tags": [],
"classifications": [], "classifications": [
"grade": "GOOD", {
"id": "DATA_USAGE:SINGLE_DATA_SET",
"name": "Single Data Set Alpha"
}
],
"grade": "EXCELLENT",
"stage": "IS", "stage": "IS",
"status": "UNSUBMITTED", "status": "UNSUBMITTED",
"is": { "is": {
"pnl": 9402399, "pnl": 13250629,
"bookSize": 20000000, "bookSize": 20000000,
"longCount": 1533, "longCount": 1810,
"shortCount": 1142, "shortCount": 1032,
"turnover": 0.319, "turnover": 0.2738,
"returns": 0.19, "returns": 0.2678,
"drawdown": 0.0881, "drawdown": 0.1008,
"margin": 0.001191, "margin": 0.001956,
"sharpe": 2.01, "sharpe": 2.38,
"fitness": 1.55, "fitness": 2.35,
"startDate": "2018-01-20", "startDate": "2018-01-20",
"checks": [ "checks": [
{ {
"name": "LOW_SHARPE", "name": "LOW_SHARPE",
"result": "PASS", "result": "PASS",
"limit": 1.25, "limit": 1.25,
"value": 2.01 "value": 2.38
}, },
{ {
"name": "LOW_FITNESS", "name": "LOW_FITNESS",
"result": "PASS", "result": "PASS",
"limit": 1.0, "limit": 1.0,
"value": 1.55 "value": 2.35
}, },
{ {
"name": "LOW_TURNOVER", "name": "LOW_TURNOVER",
"result": "PASS", "result": "PASS",
"limit": 0.01, "limit": 0.01,
"value": 0.319 "value": 0.2738
}, },
{ {
"name": "HIGH_TURNOVER", "name": "HIGH_TURNOVER",
"result": "PASS", "result": "PASS",
"limit": 0.7, "limit": 0.7,
"value": 0.319 "value": 0.2738
}, },
{ {
"name": "CONCENTRATED_WEIGHT", "name": "CONCENTRATED_WEIGHT",
"result": "FAIL", "result": "FAIL",
"date": "2021-10-15", "date": "2021-10-13",
"limit": 0.1, "limit": 0.1,
"value": 0.500002 "value": 0.500003
}, },
{ {
"name": "LOW_SUB_UNIVERSE_SHARPE", "name": "LOW_SUB_UNIVERSE_SHARPE",
"result": "FAIL", "result": "FAIL",
"limit": 0.87, "limit": 1.03,
"value": 0.77 "value": 0.77
}, },
{ {
@ -107,32 +111,8 @@ trade_when(pcr_oi_270 < 1, (implied_volatility_call_270-implied_volatility_put_2
] ]
}, },
"os": null, "os": null,
"train": { "train": null,
"pnl": 5589755, "test": null,
"bookSize": 20000000,
"longCount": 1484,
"shortCount": 1148,
"turnover": 0.3144,
"returns": 0.1417,
"drawdown": 0.0881,
"margin": 0.000901,
"fitness": 1.04,
"sharpe": 1.55,
"startDate": "2018-01-20"
},
"test": {
"pnl": 3768513,
"bookSize": 20000000,
"longCount": 1722,
"shortCount": 1121,
"turnover": 0.3372,
"returns": 0.3739,
"drawdown": 0.0495,
"margin": 0.002217,
"fitness": 3.74,
"sharpe": 3.55,
"startDate": "2022-01-20"
},
"prod": null, "prod": null,
"competitions": null, "competitions": null,
"themes": null, "themes": null,
@ -340,7 +320,7 @@ Description: Normalizes the values in a group to be between 0 and 1. (x - groupm
Operator: group_zscore(x, group) Operator: group_zscore(x, group)
Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group. Description: Calculates group Z-score - numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. zscore = (data - mean) / stddev of x for each instrument within its group.
注意这不是 python, 是 websim 表达式, 你有可能会用到这里面的操作符, 去改进这个因子, 或者你还需要用到什么数据字段, 我可以给你 1, 注意这不是 python, 是 websim 表达式, 你会用到这里面的操作符
不要出现操作符列表里面没有的东西, 如果你不知道,或者想要别的数据集, 你可以问我, 我去找找有没有 2, 去改进这个因子或者你还需要用到大概什么数据字段, 你告诉我, 我可以给你
3, 不要出现操作符列表里面没有的东西, 如果你不知道,或者想要别的数据集, 你可以问我, 我在数据库中查询有没有
你不需要分析我当前的因子, 我手上有很多数据集, 先不需要帮我写alpha, 优化这个alpha需要用到的数据集, 然后我会在数据库找出需要的数据集给你. 这些大概需要用到的数据集, 所有需要用到的数据集, 用一个python的列表给我, 不要写注释, 我会在本地使用python查数据库然后给你 4, 你不需要分析我当前的因子, 我手上有很多数据集, 先不需要帮我写alpha, 优化这个alpha需要用到的数据集, 然后我会在数据库找出需要的数据集给你. 这些大概需要用到的数据集, 所有需要用到的数据集, 用一个python的列表给我, 不要写注释, 我会在本地使用python查数据库然后给你
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