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toor
2025-12-21 19:43:55 +08:00
parent da451dc684
commit a7cf93946a
2 changed files with 38 additions and 58 deletions
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load_data_sets.py
+1 -1
View File
@@ -18,7 +18,7 @@ try:
# 创建游标
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:
# SQL 查询语句
optimize_prompt.txt
+37 -57
View File
@@ -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
{
"id": "KPRvr7YN",
"id": "gJzgYd7J",
"type": "REGULAR",
"author": "YC93384",
"settings": {
@@ -9,8 +9,8 @@ trade_when(pcr_oi_270 < 1, (implied_volatility_call_270-implied_volatility_put_2
"region": "USA",
"universe": "TOP3000",
"delay": 1,
"decay": 4,
"neutralization": "SUBINDUSTRY",
"decay": 0,
"neutralization": "INDUSTRY",
"truncation": 0.08,
"pasteurization": "ON",
"unitHandling": "VERIFY",
@@ -19,75 +19,79 @@ trade_when(pcr_oi_270 < 1, (implied_volatility_call_270-implied_volatility_put_2
"language": "FASTEXPR",
"visualization": false,
"startDate": "2018-01-20",
"endDate": "2023-01-20",
"testPeriod": "P1Y"
"endDate": "2023-01-20"
},
"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,
"operatorCount": 4
},
"dateCreated": "2025-12-21T03:10:00-05:00",
"dateCreated": "2025-12-21T05:31:30-05:00",
"dateSubmitted": null,
"dateModified": "2025-12-21T03:10:00-05:00",
"dateModified": "2025-12-21T05:31:31-05:00",
"name": null,
"favorite": false,
"hidden": false,
"color": null,
"category": null,
"tags": [],
"classifications": [],
"grade": "GOOD",
"classifications": [
{
"id": "DATA_USAGE:SINGLE_DATA_SET",
"name": "Single Data Set Alpha"
}
],
"grade": "EXCELLENT",
"stage": "IS",
"status": "UNSUBMITTED",
"is": {
"pnl": 9402399,
"pnl": 13250629,
"bookSize": 20000000,
"longCount": 1533,
"shortCount": 1142,
"turnover": 0.319,
"returns": 0.19,
"drawdown": 0.0881,
"margin": 0.001191,
"sharpe": 2.01,
"fitness": 1.55,
"longCount": 1810,
"shortCount": 1032,
"turnover": 0.2738,
"returns": 0.2678,
"drawdown": 0.1008,
"margin": 0.001956,
"sharpe": 2.38,
"fitness": 2.35,
"startDate": "2018-01-20",
"checks": [
{
"name": "LOW_SHARPE",
"result": "PASS",
"limit": 1.25,
"value": 2.01
"value": 2.38
},
{
"name": "LOW_FITNESS",
"result": "PASS",
"limit": 1.0,
"value": 1.55
"value": 2.35
},
{
"name": "LOW_TURNOVER",
"result": "PASS",
"limit": 0.01,
"value": 0.319
"value": 0.2738
},
{
"name": "HIGH_TURNOVER",
"result": "PASS",
"limit": 0.7,
"value": 0.319
"value": 0.2738
},
{
"name": "CONCENTRATED_WEIGHT",
"result": "FAIL",
"date": "2021-10-15",
"date": "2021-10-13",
"limit": 0.1,
"value": 0.500002
"value": 0.500003
},
{
"name": "LOW_SUB_UNIVERSE_SHARPE",
"result": "FAIL",
"limit": 0.87,
"limit": 1.03,
"value": 0.77
},
{
@@ -107,32 +111,8 @@ trade_when(pcr_oi_270 < 1, (implied_volatility_call_270-implied_volatility_put_2
]
},
"os": null,
"train": {
"pnl": 5589755,
"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"
},
"train": null,
"test": null,
"prod": null,
"competitions": 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)
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
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
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
1, 注意这不是 python, 是 websim 表达式, 你会用到这里面的操作符
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
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