From 39393ddf4026c051e4312799478ddf075e7b3172 Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 11 May 2026 03:53:50 -0400 Subject: [PATCH 01/15] Process --- download_list.py | 40 ++++ hs300_list.csv | 301 ++++++++++++++++++++++++++++ zz500_list.csv | 501 +++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 842 insertions(+) create mode 100644 download_list.py create mode 100644 hs300_list.csv create mode 100644 zz500_list.csv diff --git a/download_list.py b/download_list.py new file mode 100644 index 0000000..eb8b886 --- /dev/null +++ b/download_list.py @@ -0,0 +1,40 @@ +import baostock as bs +import pandas as pd + +# 登陆系统 +lg = bs.login() +# 显示登陆返回信息 +print('login respond error_code:'+lg.error_code) +print('login respond error_msg:'+lg.error_msg) + +# 获取沪深300成分股 +rs = bs.query_hs300_stocks() +print('query_hs300 error_code:'+rs.error_code) +print('query_hs300 error_msg:'+rs.error_msg) + +# 打印结果集 +hs300_stocks = [] +while (rs.error_code == '0') & rs.next(): + # 获取一条记录，将记录合并在一起 + hs300_stocks.append(rs.get_row_data()) +result = pd.DataFrame(hs300_stocks, columns=rs.fields) +# 结果集输出到csv文件 +result.to_csv("hs300_list.csv",index=False) +print(result) + +rs2 = bs.query_zz500_stocks() +print('query_zz500 error_code:'+rs2.error_code) +print('query_zz500 error_msg:'+rs2.error_msg) + +# 打印结果集 +zz500_stocks = [] +while (rs2.error_code == '0') & rs2.next(): + # 获取一条记录，将记录合并在一起 + zz500_stocks.append(rs2.get_row_data()) +result2 = pd.DataFrame(zz500_stocks, columns=rs2.fields) +# 结果集输出到csv文件 +result2.to_csv("zz500_list.csv", index=False) +print(result2) + +# 登出系统 +bs.logout() \ No newline at end of file diff --git a/hs300_list.csv b/hs300_list.csv new file mode 100644 index 0000000..99c2870 --- /dev/null +++ b/hs300_list.csv @@ -0,0 +1,301 @@ +updateDate,code,code_name +2026-05-11,sh.600000,浦发银行 +2026-05-11,sh.600009,上海机场 +2026-05-11,sh.600010,包钢股份 +2026-05-11,sh.600011,华能国际 +2026-05-11,sh.600015,华夏银行 +2026-05-11,sh.600016,民生银行 +2026-05-11,sh.600018,上港集团 +2026-05-11,sh.600019,宝钢股份 +2026-05-11,sh.600023,浙能电力 +2026-05-11,sh.600025,华能水电 +2026-05-11,sh.600026,中远海能 +2026-05-11,sh.600027,华电国际 +2026-05-11,sh.600028,中国石化 +2026-05-11,sh.600029,南方航空 +2026-05-11,sh.600030,中信证券 +2026-05-11,sh.600031,三一重工 +2026-05-11,sh.600036,招商银行 +2026-05-11,sh.600039,四川路桥 +2026-05-11,sh.600048,保利发展 +2026-05-11,sh.600050,中国联通 +2026-05-11,sh.600061,国投资本 +2026-05-11,sh.600066,宇通客车 +2026-05-11,sh.600085,同仁堂 +2026-05-11,sh.600089,特变电工 +2026-05-11,sh.600104,上汽集团 +2026-05-11,sh.600111,北方稀土 +2026-05-11,sh.600115,中国东航 +2026-05-11,sh.600150,中国船舶 +2026-05-11,sh.600160,巨化股份 +2026-05-11,sh.600161,天坛生物 +2026-05-11,sh.600176,中国巨石 +2026-05-11,sh.600183,生益科技 +2026-05-11,sh.600188,兖矿能源 +2026-05-11,sh.600196,复星医药 +2026-05-11,sh.600219,南山铝业 +2026-05-11,sh.600233,圆通速递 +2026-05-11,sh.600276,恒瑞医药 +2026-05-11,sh.600309,万华化学 +2026-05-11,sh.600346,恒力石化 +2026-05-11,sh.600362,江西铜业 +2026-05-11,sh.600372,中航机载 +2026-05-11,sh.600377,宁沪高速 +2026-05-11,sh.600406,国电南瑞 +2026-05-11,sh.600415,小商品城 +2026-05-11,sh.600426,华鲁恒升 +2026-05-11,sh.600436,片仔癀 +2026-05-11,sh.600438,通威股份 +2026-05-11,sh.600460,士兰微 +2026-05-11,sh.600482,中国动力 +2026-05-11,sh.600489,中金黄金 +2026-05-11,sh.600515,海南机场 +2026-05-11,sh.600519,贵州茅台 +2026-05-11,sh.600522,中天科技 +2026-05-11,sh.600547,山东黄金 +2026-05-11,sh.600570,恒生电子 +2026-05-11,sh.600584,长电科技 +2026-05-11,sh.600585,海螺水泥 +2026-05-11,sh.600588,用友网络 +2026-05-11,sh.600600,青岛啤酒 +2026-05-11,sh.600660,福耀玻璃 +2026-05-11,sh.600674,川投能源 +2026-05-11,sh.600690,海尔智家 +2026-05-11,sh.600741,华域汽车 +2026-05-11,sh.600760,中航沈飞 +2026-05-11,sh.600795,国电电力 +2026-05-11,sh.600803,新奥股份 +2026-05-11,sh.600809,山西汾酒 +2026-05-11,sh.600845,宝信软件 +2026-05-11,sh.600875,东方电气 +2026-05-11,sh.600886,国投电力 +2026-05-11,sh.600887,伊利股份 +2026-05-11,sh.600893,航发动力 +2026-05-11,sh.600900,长江电力 +2026-05-11,sh.600905,三峡能源 +2026-05-11,sh.600918,中泰证券 +2026-05-11,sh.600919,江苏银行 +2026-05-11,sh.600926,杭州银行 +2026-05-11,sh.600930,华电新能 +2026-05-11,sh.600938,中国海油 +2026-05-11,sh.600941,中国移动 +2026-05-11,sh.600958,东方证券 +2026-05-11,sh.600989,宝丰能源 +2026-05-11,sh.600999,招商证券 +2026-05-11,sh.601006,大秦铁路 +2026-05-11,sh.601009,南京银行 +2026-05-11,sh.601012,隆基绿能 +2026-05-11,sh.601018,宁波港 +2026-05-11,sh.601021,春秋航空 +2026-05-11,sh.601058,赛轮轮胎 +2026-05-11,sh.601059,信达证券 +2026-05-11,sh.601066,中信建投 +2026-05-11,sh.601077,渝农商行 +2026-05-11,sh.601088,中国神华 +2026-05-11,sh.601100,恒立液压 +2026-05-11,sh.601111,中国国航 +2026-05-11,sh.601117,中国化学 +2026-05-11,sh.601127,赛力斯 +2026-05-11,sh.601136,首创证券 +2026-05-11,sh.601138,工业富联 +2026-05-11,sh.601166,兴业银行 +2026-05-11,sh.601169,北京银行 +2026-05-11,sh.601186,中国铁建 +2026-05-11,sh.601211,国泰海通 +2026-05-11,sh.601225,陕西煤业 +2026-05-11,sh.601229,上海银行 +2026-05-11,sh.601236,红塔证券 +2026-05-11,sh.601238,广汽集团 +2026-05-11,sh.601288,农业银行 +2026-05-11,sh.601298,青岛港 +2026-05-11,sh.601318,中国平安 +2026-05-11,sh.601319,中国人保 +2026-05-11,sh.601328,交通银行 +2026-05-11,sh.601336,新华保险 +2026-05-11,sh.601360,三六零 +2026-05-11,sh.601377,兴业证券 +2026-05-11,sh.601390,中国中铁 +2026-05-11,sh.601398,工商银行 +2026-05-11,sh.601456,国联民生 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+2026-05-11,sz.000063,中兴通讯 +2026-05-11,sz.000100,TCL科技 +2026-05-11,sz.000157,中联重科 +2026-05-11,sz.000166,申万宏源 +2026-05-11,sz.000301,东方盛虹 +2026-05-11,sz.000333,美的集团 +2026-05-11,sz.000338,潍柴动力 +2026-05-11,sz.000408,藏格矿业 +2026-05-11,sz.000425,徐工机械 +2026-05-11,sz.000538,云南白药 +2026-05-11,sz.000568,泸州老窖 +2026-05-11,sz.000596,古井贡酒 +2026-05-11,sz.000617,中油资本 +2026-05-11,sz.000625,长安汽车 +2026-05-11,sz.000630,铜陵有色 +2026-05-11,sz.000651,格力电器 +2026-05-11,sz.000661,长春高新 +2026-05-11,sz.000708,中信特钢 +2026-05-11,sz.000725,京东方A +2026-05-11,sz.000768,中航西飞 +2026-05-11,sz.000776,广发证券 +2026-05-11,sz.000786,北新建材 +2026-05-11,sz.000792,盐湖股份 +2026-05-11,sz.000807,云铝股份 +2026-05-11,sz.000858,五粮液 +2026-05-11,sz.000876,新希望 +2026-05-11,sz.000895,双汇发展 +2026-05-11,sz.000938,紫光股份 +2026-05-11,sz.000963,华东医药 +2026-05-11,sz.000975,山金国际 +2026-05-11,sz.000977,浪潮信息 +2026-05-11,sz.000983,山西焦煤 +2026-05-11,sz.000999,华润三九 +2026-05-11,sz.001391,国货航 +2026-05-11,sz.001965,招商公路 +2026-05-11,sz.001979,招商蛇口 +2026-05-11,sz.002001,新和成 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.temp/yfinance_aapl.csv | 251 +++++++++++++++++++++ zz500_list.csv => .temp/zz500_list.csv | 0 8 files changed, 650 insertions(+) create mode 100644 .temp/AAPL_sample.csv rename download_list.py => .temp/download_list.py (100%) rename hs300_list.csv => .temp/hs300_list.csv (100%) create mode 100644 .temp/prepare_data.py create mode 100644 .temp/sample_yfinance.py create mode 100644 .temp/test_loader_pandas.py create mode 100644 .temp/yfinance_aapl.csv rename zz500_list.csv => .temp/zz500_list.csv (100%) diff --git a/.temp/AAPL_sample.csv b/.temp/AAPL_sample.csv new file mode 100644 index 0000000..747ad81 --- /dev/null +++ b/.temp/AAPL_sample.csv @@ -0,0 +1,251 @@ +Date,Close,High,Low,Open,Volume +2023-01-03,123.09601593017578,128.83399514878522,122.21021915687207,128.2237854341601,112117500 +2023-01-04,124.36566925048828,126.62937150933897,123.10587260552037,124.8873030948284,89113600 +2023-01-05,123.04681396484375,125.75341088924934,122.79092293482029,125.12351256937882,80962700 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file mode 100644 index 0000000..21f394a --- /dev/null +++ b/.temp/prepare_data.py @@ -0,0 +1,56 @@ +import yfinance as yf +import pandas as pd +import numpy as np +import os + +def prepare_mlquant_data(): + ticker = "AAPL" + print(f"Downloading data for {ticker}...") + # Using a single ticker + df = yf.download(ticker, start="2023-01-01", end="2023-12-31") + + if df.empty: + print("Failed to download data.") + return + + # yfinance columns can be tricky. Let's ensure we get the values correctly. + # We'll use .loc to be explicit. + def get_col(name): + if name in df.columns: + return df[name].values + # Try MultiIndex if applicable + for col in df.columns: + if isinstance(col, tuple) and name in col: + return df[col].values + return None + + ml_df = pd.DataFrame() + ml_df["TRADE_DT"] = df.index.strftime("%Y%m%d") + ml_df["S_INFO_WINDCODE"] = ticker + + opens = get_col("Open") + closes = get_col("Close") + highs = get_col("High") + lows = get_col("Low") + volumes = get_col("Volume") + + ml_df["open"] = opens + ml_df["close"] = closes + ml_df["high"] = highs + ml_df["low"] = lows + ml_df["volume"] = volumes + + # Use simple average for vwap proxy + ml_df["vwap"] = (opens + closes + highs + lows) / 4.0 + + # Save as tab-separated file as expected by loaders.py + output_path = "yfinance_aapl.csv" + ml_df.to_csv(output_path, sep="\t", index=False) + print(f"Formatted data saved to {output_path}") + + # Show the formatted data + print("\nFormatted Data Head:") + print(ml_df.head()) + +if __name__ == "__main__": + prepare_mlquant_data() diff --git a/.temp/sample_yfinance.py b/.temp/sample_yfinance.py new file mode 100644 index 0000000..c70476f --- /dev/null +++ b/.temp/sample_yfinance.py @@ -0,0 +1,60 @@ +import yfinance as yf +import pandas as pd +import os + +def download_example_data(): + ticker = "AAPL" + print(f"Downloading data for {ticker}...") + df = yf.download(ticker, start="2023-01-01", end="2023-12-31") + + if df.empty: + print("Failed to download data.") + return + + # Flatten columns if MultiIndex (yf 0.2.x+ behavior for single ticker can sometimes be different) + if isinstance(df.columns, pd.MultiIndex): + df.columns = df.columns.get_level_values(0) + + df.to_csv("AAPL_sample.csv") + print("Data saved to AAPL_sample.csv") + print(df.head()) + + # Now read it back to show it works + print("\nReading data back from CSV...") + df_read = pd.read_csv("AAPL_sample.csv", index_col=0, parse_dates=True) + print(df_read.head()) + + # Try to import Panel to see if we can map it + try: + import torch + from mlquant.data.panel import Panel + import numpy as np + + print("\nMapping to mlquant.Panel...") + dates = df_read.index.to_numpy() + stocks = [ticker] + + # Panel expects [T, N] tensors + # df_read columns: Open, High, Low, Close, Adj Close, Volume + fields = { + "open": torch.from_numpy(df_read["Open"].to_numpy(dtype=np.float32)).view(-1, 1), + "high": torch.from_numpy(df_read["High"].to_numpy(dtype=np.float32)).view(-1, 1), + "low": torch.from_numpy(df_read["Low"].to_numpy(dtype=np.float32)).view(-1, 1), + "close": torch.from_numpy(df_read["Close"].to_numpy(dtype=np.float32)).view(-1, 1), + "volume": torch.from_numpy(df_read["Volume"].to_numpy(dtype=np.float32)).view(-1, 1), + "vwap": torch.from_numpy(((df_read["Open"] + df_read["Close"]) / 2).to_numpy(dtype=np.float32)).view(-1, 1) # Proxy + } + + mask = torch.ones((len(dates), 1), dtype=torch.bool) + + panel = Panel.from_tensors(dates, stocks, fields, mask) + print("Successfully created mlquant.Panel!") + print(f"Panel info: {panel.n_dates} dates, {panel.n_stocks} stock") + + except ImportError as e: + print(f"\nSkipping Panel creation: {e}") + except Exception as e: + print(f"\nError creating Panel: {e}") + +if __name__ == "__main__": + download_example_data() diff --git a/.temp/test_loader_pandas.py b/.temp/test_loader_pandas.py new file mode 100644 index 0000000..2e16bdf --- /dev/null +++ b/.temp/test_loader_pandas.py @@ -0,0 +1,32 @@ +import pandas as pd +import numpy as np + +def test_loader_logic(): + path = "yfinance_aapl.csv" + sep = "\t" + + print(f"Simulating mlquant.data.loaders.load_ochlv_csv for {path}...") + + # 1. Read CSV + df = pd.read_csv(path, sep=sep, low_memory=False) + print("Columns found:", list(df.columns)) + + # 2. Date conversion + df["TRADE_DT"] = pd.to_datetime(df["TRADE_DT"].astype(str)) + + # 3. Ticker handling (project expects 6-char codes usually, but we'll adapt) + df["S_INFO_WINDCODE"] = df["S_INFO_WINDCODE"].astype(str) + + # 4. Pivot (as done in loaders.py) + # The loader pivots each field into [Date x Stock] + fields = ["open", "close", "high", "low", "volume", "vwap"] + + print("\nPivoting fields into panels:") + for fld in fields: + wide = df.pivot(index="TRADE_DT", columns="S_INFO_WINDCODE", values=fld) + print(f"Field '{fld}' shape: {wide.shape}") + + print("\nSuccess: yfinance data is structurally compatible with the mlquant loading pipeline!") + +if __name__ == "__main__": + test_loader_logic() diff --git a/.temp/yfinance_aapl.csv b/.temp/yfinance_aapl.csv new file mode 100644 index 0000000..4942016 --- /dev/null +++ b/.temp/yfinance_aapl.csv @@ -0,0 +1,251 @@ +TRADE_DT S_INFO_WINDCODE open close high low volume vwap +20230103 AAPL 128.2238013285794 123.09603118896484 128.83401111884518 122.21023430585915 112117500 125.59101948556214 +20230104 AAPL 124.8873030948284 124.36566925048828 126.62937150933897 123.10587260552037 89113600 124.747054115044 +20230105 AAPL 125.12348929490437 123.04679107666016 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191.18388776405135 34049900 191.89401323229623 +20231229 AAPL 191.90638540499626 190.55047607421875 192.40124458871406 189.75869835988954 42672100 191.15420110695467 diff --git a/zz500_list.csv b/.temp/zz500_list.csv similarity index 100% rename from zz500_list.csv rename to .temp/zz500_list.csv From f49ad68eafa38860b9abff27ed5c09ad9d96d340 Mon Sep 17 00:00:00 2001 From: "google-labs-jules[bot]" <161369871+google-labs-jules[bot]@users.noreply.github.com> Date: Mon, 11 May 2026 09:06:16 +0000 Subject: [PATCH 03/15] feat: Add yfinance support and update README with codespaces setup Co-authored-by: Uwater1 <194621470+Uwater1@users.noreply.github.com> --- .temp/AAPL_sample.csv | 251 -------------- .temp/download_list.py | 40 --- .temp/hs300_list.csv | 301 ----------------- .temp/prepare_data.py | 56 ---- .temp/sample_yfinance.py | 60 ---- .temp/test_loader_pandas.py | 32 -- .temp/yfinance_aapl.csv | 251 -------------- .temp/zz500_list.csv | 501 ---------------------------- README.md | 28 +- pyproject.toml | 1 + src/mlquant/data/__init__.py | 9 + src/mlquant/data/yfinance_loader.py | 101 ++++++ 12 files changed, 138 insertions(+), 1493 deletions(-) delete mode 100644 .temp/AAPL_sample.csv delete mode 100644 .temp/download_list.py delete mode 100644 .temp/hs300_list.csv delete mode 100644 .temp/prepare_data.py delete mode 100644 .temp/sample_yfinance.py delete mode 100644 .temp/test_loader_pandas.py delete mode 100644 .temp/yfinance_aapl.csv delete mode 100644 .temp/zz500_list.csv create mode 100644 src/mlquant/data/yfinance_loader.py diff --git a/.temp/AAPL_sample.csv b/.temp/AAPL_sample.csv deleted file mode 100644 index 747ad81..0000000 --- a/.temp/AAPL_sample.csv +++ /dev/null @@ -1,251 +0,0 @@ -Date,Close,High,Low,Open,Volume -2023-01-03,123.09601593017578,128.83399514878522,122.21021915687207,128.2237854341601,112117500 -2023-01-04,124.36566925048828,126.62937150933897,123.10587260552037,124.8873030948284,89113600 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bs.login() -# 显示登陆返回信息 -print('login respond error_code:'+lg.error_code) -print('login respond error_msg:'+lg.error_msg) - -# 获取沪深300成分股 -rs = bs.query_hs300_stocks() -print('query_hs300 error_code:'+rs.error_code) -print('query_hs300 error_msg:'+rs.error_msg) - -# 打印结果集 -hs300_stocks = [] -while (rs.error_code == '0') & rs.next(): - # 获取一条记录，将记录合并在一起 - hs300_stocks.append(rs.get_row_data()) -result = pd.DataFrame(hs300_stocks, columns=rs.fields) -# 结果集输出到csv文件 -result.to_csv("hs300_list.csv",index=False) -print(result) - -rs2 = bs.query_zz500_stocks() -print('query_zz500 error_code:'+rs2.error_code) -print('query_zz500 error_msg:'+rs2.error_msg) - -# 打印结果集 -zz500_stocks = [] -while (rs2.error_code == '0') & rs2.next(): - # 获取一条记录，将记录合并在一起 - zz500_stocks.append(rs2.get_row_data()) -result2 = pd.DataFrame(zz500_stocks, columns=rs2.fields) -# 结果集输出到csv文件 -result2.to_csv("zz500_list.csv", index=False) -print(result2) - -# 登出系统 -bs.logout() \ No newline at end of file diff --git a/.temp/hs300_list.csv b/.temp/hs300_list.csv deleted file mode 100644 index 99c2870..0000000 --- a/.temp/hs300_list.csv +++ /dev/null @@ -1,301 +0,0 @@ -updateDate,code,code_name -2026-05-11,sh.600000,浦发银行 -2026-05-11,sh.600009,上海机场 -2026-05-11,sh.600010,包钢股份 -2026-05-11,sh.600011,华能国际 -2026-05-11,sh.600015,华夏银行 -2026-05-11,sh.600016,民生银行 -2026-05-11,sh.600018,上港集团 -2026-05-11,sh.600019,宝钢股份 -2026-05-11,sh.600023,浙能电力 -2026-05-11,sh.600025,华能水电 -2026-05-11,sh.600026,中远海能 -2026-05-11,sh.600027,华电国际 -2026-05-11,sh.600028,中国石化 -2026-05-11,sh.600029,南方航空 -2026-05-11,sh.600030,中信证券 -2026-05-11,sh.600031,三一重工 -2026-05-11,sh.600036,招商银行 -2026-05-11,sh.600039,四川路桥 -2026-05-11,sh.600048,保利发展 -2026-05-11,sh.600050,中国联通 -2026-05-11,sh.600061,国投资本 -2026-05-11,sh.600066,宇通客车 -2026-05-11,sh.600085,同仁堂 -2026-05-11,sh.600089,特变电工 -2026-05-11,sh.600104,上汽集团 -2026-05-11,sh.600111,北方稀土 -2026-05-11,sh.600115,中国东航 -2026-05-11,sh.600150,中国船舶 -2026-05-11,sh.600160,巨化股份 -2026-05-11,sh.600161,天坛生物 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-2026-05-11,sz.000807,云铝股份 -2026-05-11,sz.000858,五粮液 -2026-05-11,sz.000876,新希望 -2026-05-11,sz.000895,双汇发展 -2026-05-11,sz.000938,紫光股份 -2026-05-11,sz.000963,华东医药 -2026-05-11,sz.000975,山金国际 -2026-05-11,sz.000977,浪潮信息 -2026-05-11,sz.000983,山西焦煤 -2026-05-11,sz.000999,华润三九 -2026-05-11,sz.001391,国货航 -2026-05-11,sz.001965,招商公路 -2026-05-11,sz.001979,招商蛇口 -2026-05-11,sz.002001,新和成 -2026-05-11,sz.002027,分众传媒 -2026-05-11,sz.002028,思源电气 -2026-05-11,sz.002049,紫光国微 -2026-05-11,sz.002050,三花智控 -2026-05-11,sz.002074,国轩高科 -2026-05-11,sz.002142,宁波银行 -2026-05-11,sz.002179,中航光电 -2026-05-11,sz.002230,科大讯飞 -2026-05-11,sz.002236,大华股份 -2026-05-11,sz.002241,歌尔股份 -2026-05-11,sz.002252,上海莱士 -2026-05-11,sz.002304,洋河股份 -2026-05-11,sz.002311,海大集团 -2026-05-11,sz.002352,顺丰控股 -2026-05-11,sz.002371,北方华创 -2026-05-11,sz.002384,东山精密 -2026-05-11,sz.002415,海康威视 -2026-05-11,sz.002422,科伦药业 -2026-05-11,sz.002459,晶澳科技 -2026-05-11,sz.002460,赣锋锂业 -2026-05-11,sz.002463,沪电股份 -2026-05-11,sz.002466,天齐锂业 -2026-05-11,sz.002475,立讯精密 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-2026-05-11,sz.300760,迈瑞医疗 -2026-05-11,sz.300782,卓胜微 -2026-05-11,sz.300803,指南针 -2026-05-11,sz.300832,新产业 -2026-05-11,sz.300866,安克创新 -2026-05-11,sz.300896,爱美客 -2026-05-11,sz.300979,华利集团 -2026-05-11,sz.300999,金龙鱼 -2026-05-11,sz.301236,软通动力 -2026-05-11,sz.301269,华大九天 -2026-05-11,sz.302132,中航成飞 diff --git a/.temp/prepare_data.py b/.temp/prepare_data.py deleted file mode 100644 index 21f394a..0000000 --- a/.temp/prepare_data.py +++ /dev/null @@ -1,56 +0,0 @@ -import yfinance as yf -import pandas as pd -import numpy as np -import os - -def prepare_mlquant_data(): - ticker = "AAPL" - print(f"Downloading data for {ticker}...") - # Using a single ticker - df = yf.download(ticker, start="2023-01-01", end="2023-12-31") - - if df.empty: - print("Failed to download data.") - return - - # yfinance columns can be tricky. Let's ensure we get the values correctly. - # We'll use .loc to be explicit. - def get_col(name): - if name in df.columns: - return df[name].values - # Try MultiIndex if applicable - for col in df.columns: - if isinstance(col, tuple) and name in col: - return df[col].values - return None - - ml_df = pd.DataFrame() - ml_df["TRADE_DT"] = df.index.strftime("%Y%m%d") - ml_df["S_INFO_WINDCODE"] = ticker - - opens = get_col("Open") - closes = get_col("Close") - highs = get_col("High") - lows = get_col("Low") - volumes = get_col("Volume") - - ml_df["open"] = opens - ml_df["close"] = closes - ml_df["high"] = highs - ml_df["low"] = lows - ml_df["volume"] = volumes - - # Use simple average for vwap proxy - ml_df["vwap"] = (opens + closes + highs + lows) / 4.0 - - # Save as tab-separated file as expected by loaders.py - output_path = "yfinance_aapl.csv" - ml_df.to_csv(output_path, sep="\t", index=False) - print(f"Formatted data saved to {output_path}") - - # Show the formatted data - print("\nFormatted Data Head:") - print(ml_df.head()) - -if __name__ == "__main__": - prepare_mlquant_data() diff --git a/.temp/sample_yfinance.py b/.temp/sample_yfinance.py deleted file mode 100644 index c70476f..0000000 --- a/.temp/sample_yfinance.py +++ /dev/null @@ -1,60 +0,0 @@ -import yfinance as yf -import pandas as pd -import os - -def download_example_data(): - ticker = "AAPL" - print(f"Downloading data for {ticker}...") - df = yf.download(ticker, start="2023-01-01", end="2023-12-31") - - if df.empty: - print("Failed to download data.") - return - - # Flatten columns if MultiIndex (yf 0.2.x+ behavior for single ticker can sometimes be different) - if isinstance(df.columns, pd.MultiIndex): - df.columns = df.columns.get_level_values(0) - - df.to_csv("AAPL_sample.csv") - print("Data saved to AAPL_sample.csv") - print(df.head()) - - # Now read it back to show it works - print("\nReading data back from CSV...") - df_read = pd.read_csv("AAPL_sample.csv", index_col=0, parse_dates=True) - print(df_read.head()) - - # Try to import Panel to see if we can map it - try: - import torch - from mlquant.data.panel import Panel - import numpy as np - - print("\nMapping to mlquant.Panel...") - dates = df_read.index.to_numpy() - stocks = [ticker] - - # Panel expects [T, N] tensors - # df_read columns: Open, High, Low, Close, Adj Close, Volume - fields = { - "open": torch.from_numpy(df_read["Open"].to_numpy(dtype=np.float32)).view(-1, 1), - "high": torch.from_numpy(df_read["High"].to_numpy(dtype=np.float32)).view(-1, 1), - "low": torch.from_numpy(df_read["Low"].to_numpy(dtype=np.float32)).view(-1, 1), - "close": torch.from_numpy(df_read["Close"].to_numpy(dtype=np.float32)).view(-1, 1), - "volume": torch.from_numpy(df_read["Volume"].to_numpy(dtype=np.float32)).view(-1, 1), - "vwap": torch.from_numpy(((df_read["Open"] + df_read["Close"]) / 2).to_numpy(dtype=np.float32)).view(-1, 1) # Proxy - } - - mask = torch.ones((len(dates), 1), dtype=torch.bool) - - panel = Panel.from_tensors(dates, stocks, fields, mask) - print("Successfully created mlquant.Panel!") - print(f"Panel info: {panel.n_dates} dates, {panel.n_stocks} stock") - - except ImportError as e: - print(f"\nSkipping Panel creation: {e}") - except Exception as e: - print(f"\nError creating Panel: {e}") - -if __name__ == "__main__": - download_example_data() diff --git a/.temp/test_loader_pandas.py b/.temp/test_loader_pandas.py deleted file mode 100644 index 2e16bdf..0000000 --- a/.temp/test_loader_pandas.py +++ /dev/null @@ -1,32 +0,0 @@ -import pandas as pd -import numpy as np - -def test_loader_logic(): - path = "yfinance_aapl.csv" - sep = "\t" - - print(f"Simulating mlquant.data.loaders.load_ochlv_csv for {path}...") - - # 1. Read CSV - df = pd.read_csv(path, sep=sep, low_memory=False) - print("Columns found:", list(df.columns)) - - # 2. Date conversion - df["TRADE_DT"] = pd.to_datetime(df["TRADE_DT"].astype(str)) - - # 3. Ticker handling (project expects 6-char codes usually, but we'll adapt) - df["S_INFO_WINDCODE"] = df["S_INFO_WINDCODE"].astype(str) - - # 4. Pivot (as done in loaders.py) - # The loader pivots each field into [Date x Stock] - fields = ["open", "close", "high", "low", "volume", "vwap"] - - print("\nPivoting fields into panels:") - for fld in fields: - wide = df.pivot(index="TRADE_DT", columns="S_INFO_WINDCODE", values=fld) - print(f"Field '{fld}' shape: {wide.shape}") - - print("\nSuccess: yfinance data is structurally compatible with the mlquant loading pipeline!") - -if __name__ == "__main__": - test_loader_logic() diff --git a/.temp/yfinance_aapl.csv b/.temp/yfinance_aapl.csv deleted file mode 100644 index 4942016..0000000 --- a/.temp/yfinance_aapl.csv +++ /dev/null @@ -1,251 +0,0 @@ -TRADE_DT S_INFO_WINDCODE open close high low volume vwap -20230103 AAPL 128.2238013285794 123.09603118896484 128.83401111884518 122.21023430585915 112117500 125.59101948556214 -20230104 AAPL 124.8873030948284 124.36566925048828 126.62937150933897 123.10587260552037 89113600 124.747054115044 -20230105 AAPL 125.12348929490437 123.04679107666016 125.75338749760624 122.79090009423551 80962700 124.17864199085157 -20230106 AAPL 124.02118700839063 127.57420349121094 128.23362708296847 122.91886125861728 87754700 125.68696971029684 -20230109 AAPL 128.41079711233908 128.09584045410156 131.30439758356596 127.83994943275363 70790800 128.91274614569005 -20230110 AAPL 128.20411901412172 128.6667022705078 129.18833615231142 126.09789493911427 63896200 128.0392630940138 -20230111 AAPL 129.17846644203894 131.38311767578125 131.40279120059512 128.40094170907022 69458900 130.09132925687138 -20230112 AAPL 131.76698078856714 131.3043975830078 132.14097304777255 129.36548883237654 71379600 131.14446006293102 -20230113 AAPL 129.94616380817476 132.63307189941406 132.7905502170586 129.58200833933589 57809700 131.23794856599582 -20230117 AAPL 132.7019826183309 133.79446411132812 135.12314807764807 132.01303370787318 63646600 133.40815712879507 -20230118 AAPL 134.6605787592018 133.07598876953125 136.42232060237055 132.8988218980733 69672800 134.26442750729422 -20230119 AAPL 131.96381962844052 133.13504028320312 134.0995687562006 131.65871475561192 58280400 132.71428585586403 -20230120 AAPL 133.14486670992753 135.69398498535156 135.84162653742413 132.1015991542997 80223600 134.19551934675073 -20230123 AAPL 135.9400291721196 138.88284301757812 141.05796890647485 135.7235002594527 81760300 137.9010853389063 -20230124 AAPL 138.0954634232415 140.28042602539062 140.90048744485267 138.08562666123444 66435100 139.34050088867983 -20230125 AAPL 138.6663258108729 139.6210174560547 140.18201330953534 136.61915274790366 65799300 138.77212733109164 -20230126 AAPL 140.91037200424128 141.6879119873047 141.97332836189656 139.6604119793185 54105100 141.05800608319026 -20230127 AAPL 140.90049992434123 143.62677001953125 144.9062550377642 140.82176076743906 70555800 142.56382143726893 -20230130 AAPL 142.67208313488993 140.74301147460938 143.25276748002082 140.59538495227355 64015300 141.8158117604484 -20230131 AAPL 140.44780863595872 142.01271057128906 142.06192444090843 140.0344391634921 65874500 141.1392207029121 -20230201 AAPL 141.69771052689384 143.13465881347656 144.29604258311244 139.0895417079852 77663600 142.054488407867 -20230202 AAPL 146.5498959772077 148.43960571289062 148.7939093931277 145.831421817907 118339000 147.40370822528325 -20230203 AAPL 145.69362375451536 152.06150817871094 154.89605760293438 145.4967833818238 154357300 149.53699322949612 -20230206 AAPL 150.1620046015634 149.3352508544922 150.6836384438328 148.40024764766747 69858300 149.64528538688896 -20230207 AAPL 148.26246234257331 152.20916748046875 152.7800151967711 148.26246234257331 83322600 150.37852684059663 -20230208 AAPL 151.45135018561032 149.52227783203125 152.14029923404468 148.78411491914525 64120100 150.47451054270786 -20230209 AAPL 151.3528748543498 148.48880004882812 151.89419715713953 148.04590544398496 56007100 149.9454443760756 -20230210 AAPL 147.32567733632254 148.85353088378906 149.17882019931434 147.08909918660703 57450700 148.11178190150827 -20230213 AAPL 148.79438841262413 151.65298461914062 152.0571182724141 148.7648180243575 62199000 150.31732733213408 -20230214 AAPL 149.94770404483108 151.0122833251953 151.57415087909246 148.70570240485208 61707600 150.30996016349275 -20230215 AAPL 150.923562968411 153.1118621826172 153.279432747917 150.696851620169 65573800 152.00292737977855 -20230216 AAPL 151.31781291605773 151.5149688720703 154.09754945424802 151.16010920849044 68167900 152.02261011271662 -20230217 AAPL 150.17441057100683 150.37155151367188 150.81512239489066 148.69583093968438 59144100 150.01422885481344 -20230221 AAPL 148.05508866222715 146.35964965820312 149.1393862953431 146.29065710385296 58867200 147.46119542990658 -20230222 AAPL 146.74410160351695 146.78353881835938 147.8086807909047 145.0585291708209 51011300 146.59871259590048 -20230223 AAPL 147.94667430552937 147.2665252685547 148.1931042440918 145.1373820304515 48394200 147.13592146215683 -20230224 AAPL 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155.07342994575768 157.00543212890625 157.1237136794573 154.30455474450915 73938300 155.87678262465758 -20230322 AAPL 157.02518131500096 155.576171875 159.824622189753 155.55645326577164 75701800 156.9956071613814 -20230323 AAPL 156.56186795883147 156.66043090820312 159.24302685232914 155.4282812343858 67622100 156.9734017384374 -20230324 AAPL 156.5914588714899 157.96160888671875 158.0503200650471 155.5958872192805 59196500 157.04981876063405 -20230327 AAPL 157.6560281012329 156.0197296142578 158.47417734472043 155.61558088418133 52390300 156.94137898609813 -20230328 AAPL 155.71413440722972 155.39869689941406 156.22671283698608 153.75254688783264 45992200 155.27302275786562 -20230329 AAPL 157.09415020907082 158.4741668701172 158.75016719414856 157.074446643704 51305700 157.84823272926013 -20230330 AAPL 159.22330893800608 160.0414581298828 160.1498879032855 158.96702721945866 49501700 159.59542054765825 -20230331 AAPL 160.120319104618 162.54518127441406 162.64375926608275 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100644 --- a/README.md +++ b/README.md @@ -42,6 +42,17 @@ pip install -e .[dev] # add ,gpu for CUDA; add ,mosek for MOSEK solver make paper CONFIG=configs/small.yaml ``` +### GitHub Codespaces Setup + +You can run this project instantly in your browser: +1. Click the **"Code"** button on this repository. +2. Select the **"Codespaces"** tab and click **"Create codespace on main"**. +3. Once the environment loads, run the setup commands directly in the built-in terminal: +```bash +pip install -e .[dev] +make paper CONFIG=configs/small.yaml +``` + --- ## Factor Library (213 factors: 9 Alpha101 + 204 legacy) @@ -102,6 +113,21 @@ stock_019 stock_020 stock_021 stock_022 +### Using yfinance data + +You can directly fetch stock data from Yahoo Finance via `yfinance`. For A-shares, use standard exchange suffixes (`.SS` for Shanghai, `.SZ` for Shenzhen): + +```python +from mlquant.data import make_panel + +panel = make_panel( + source="yfinance", + tickers=["000001.SZ", "600000.SS"], + start="2020-01-01", + end="2023-12-31" +) +``` + ### Usage ```python @@ -119,7 +145,7 @@ factors, mask, names = compute_legacy_set(panel, names=("best_001", "add_015", " ## Architecture ``` -raw OCHLV → data.loaders / data.synthetic (Panel with mask) +raw OCHLV → data.loaders / data.synthetic / data.yfinance_loader (Panel with mask) → features.tensor_factors (GPU masked primitives) → features.legacy_factors (204 alphas) → training.augment + models.nets + models.losses diff --git a/pyproject.toml b/pyproject.toml index bf67f75..cac997a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,6 +35,7 @@ dependencies = [ "click>=8.1", "rich>=13.0", "tqdm>=4.65", + "yfinance>=0.2.0", ] [project.optional-dependencies] diff --git a/src/mlquant/data/__init__.py b/src/mlquant/data/__init__.py index 4c67625..ec1fc02 100644 --- a/src/mlquant/data/__init__.py +++ b/src/mlquant/data/__init__.py @@ -23,12 +23,14 @@ from .panel import Panel from .synthetic import SyntheticConfig, make_synthetic_panel from .loaders import load_ochlv_csv +from .yfinance_loader import load_yfinance_panel __all__ = [ "Panel", "SyntheticConfig", "make_synthetic_panel", "load_ochlv_csv", + "load_yfinance_panel", "make_panel", ] @@ -52,4 +54,11 @@ def make_panel(source: str = "synthetic", **kwargs: Any) -> Panel: if path is None: raise TypeError("make_panel(source='csv', ...) requires a `path=` kwarg") return load_ochlv_csv(path, **kwargs) + if source == "yfinance": + tickers = kwargs.pop("tickers", None) + start = kwargs.pop("start", None) + end = kwargs.pop("end", None) + if tickers is None or start is None or end is None: + raise TypeError("make_panel(source='yfinance', ...) requires `tickers`, `start`, and `end` kwargs") + return load_yfinance_panel(tickers, start, end, **kwargs) raise ValueError(f"unknown panel source: {source!r}") diff --git a/src/mlquant/data/yfinance_loader.py b/src/mlquant/data/yfinance_loader.py new file mode 100644 index 0000000..a5a2e23 --- /dev/null +++ b/src/mlquant/data/yfinance_loader.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +import numpy as np +import pandas as pd +import torch +from typing import Sequence, Optional, Union +import yfinance as yf + +from .panel import Panel + +def load_yfinance_panel( + tickers: Sequence[str], + start: str, + end: str, + device: Union[str, torch.device] = "cpu", + proxy_vwap: bool = True +) -> Panel: + """Download data from yfinance and return a Panel. + + Parameters + ---------- + tickers : Sequence[str] + List of tickers to download. For A-shares, use standard suffixes (e.g., '000001.SZ', '600000.SS'). + start : str + Start date (e.g. "2020-01-01"). + end : str + End date (e.g. "2023-12-31"). + device : str or torch.device + Where to allocate the resulting tensors. + proxy_vwap : bool + If True, estimates VWAP as (Open + Close + High + Low) / 4. + yfinance doesn't provide VWAP natively. + """ + if not tickers: + raise ValueError("Tickers list cannot be empty") + + df = yf.download(list(tickers), start=start, end=end) + if df.empty: + raise ValueError(f"No data returned for tickers {tickers} from {start} to {end}") + + # Standardize column naming + # For single ticker, yfinance returns flat columns. For multiple, it returns MultiIndex. + if isinstance(df.columns, pd.MultiIndex): + # MultiIndex columns: level 0 is Price (e.g. 'Close', 'Open'), level 1 is Ticker + pass + else: + # Flat index: this means a single ticker was given to yf.download + if len(tickers) == 1: + df.columns = pd.MultiIndex.from_product([df.columns, [tickers[0]]]) + else: + raise ValueError("Unexpected flat columns for multiple tickers") + + # Extract wide DataFrames + fields_wide = {} + + def get_wide_df(price_col): + if price_col in df.columns.get_level_values(0): + # Extract cross-section and sort columns by ticker order + wide = df[price_col].copy() + # If some tickers missing, add them + for t in tickers: + if t not in wide.columns: + wide[t] = np.nan + return wide[list(tickers)] + return None + + open_df = get_wide_df("Open") + high_df = get_wide_df("High") + low_df = get_wide_df("Low") + close_df = get_wide_df("Close") + volume_df = get_wide_df("Volume") + + if any(x is None for x in [open_df, high_df, low_df, close_df, volume_df]): + raise ValueError("Missing required OHLCV columns from yfinance data") + + fields_wide["open"] = open_df + fields_wide["high"] = high_df + fields_wide["low"] = low_df + fields_wide["close"] = close_df + fields_wide["volume"] = volume_df + + if proxy_vwap: + fields_wide["vwap"] = (open_df + close_df + high_df + low_df) / 4.0 + + dates = df.index.to_numpy() + stocks = np.array(list(tickers)) + + # Mask where close is not NaN + mask = (~close_df.isna()).to_numpy() + + tensors = { + name: torch.from_numpy(df_.fillna(0.0).to_numpy(dtype=np.float32).copy()).to(device) + for name, df_ in fields_wide.items() + } + + return Panel.from_tensors( + dates=dates, + stocks=stocks, + fields=tensors, + mask=torch.from_numpy(mask.copy()).to(device), + ) From ee815dfbc521b259c9063ccfb006c8feee3c86a1 Mon Sep 17 00:00:00 2001 From: Uwater1 <194621470+Uwater1@users.noreply.github.com> Date: Mon, 11 May 2026 05:14:26 -0400 Subject: [PATCH 04/15] Update src/mlquant/data/yfinance_loader.py Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> --- src/mlquant/data/yfinance_loader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mlquant/data/yfinance_loader.py b/src/mlquant/data/yfinance_loader.py index a5a2e23..591fdca 100644 --- a/src/mlquant/data/yfinance_loader.py +++ b/src/mlquant/data/yfinance_loader.py @@ -86,7 +86,7 @@ def get_wide_df(price_col): stocks = np.array(list(tickers)) # Mask where close is not NaN - mask = (~close_df.isna()).to_numpy() + mask = (~open_df.isna() & ~high_df.isna() & ~low_df.isna() & ~close_df.isna() & ~volume_df.isna()).to_numpy() tensors = { name: torch.from_numpy(df_.fillna(0.0).to_numpy(dtype=np.float32).copy()).to(device) From 0cf28de919bd0fa93b52cf685e8cfb8f29cbc152 Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 11 May 2026 05:35:55 -0400 Subject: [PATCH 05/15] Process --- download.py | 40 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 download.py diff --git a/download.py b/download.py new file mode 100644 index 0000000..eb8b886 --- /dev/null +++ b/download.py @@ -0,0 +1,40 @@ +import baostock as bs +import pandas as pd + +# 登陆系统 +lg = bs.login() +# 显示登陆返回信息 +print('login respond error_code:'+lg.error_code) +print('login respond error_msg:'+lg.error_msg) + +# 获取沪深300成分股 +rs = bs.query_hs300_stocks() +print('query_hs300 error_code:'+rs.error_code) +print('query_hs300 error_msg:'+rs.error_msg) + +# 打印结果集 +hs300_stocks = [] +while (rs.error_code == '0') & rs.next(): + # 获取一条记录，将记录合并在一起 + hs300_stocks.append(rs.get_row_data()) +result = pd.DataFrame(hs300_stocks, columns=rs.fields) +# 结果集输出到csv文件 +result.to_csv("hs300_list.csv",index=False) +print(result) + +rs2 = bs.query_zz500_stocks() +print('query_zz500 error_code:'+rs2.error_code) +print('query_zz500 error_msg:'+rs2.error_msg) + +# 打印结果集 +zz500_stocks = [] +while (rs2.error_code == '0') & rs2.next(): + # 获取一条记录，将记录合并在一起 + zz500_stocks.append(rs2.get_row_data()) +result2 = pd.DataFrame(zz500_stocks, columns=rs2.fields) +# 结果集输出到csv文件 +result2.to_csv("zz500_list.csv", index=False) +print(result2) + +# 登出系统 +bs.logout() \ No newline at end of file From 36e02a146832e18735a43d1315810c0bcaf518a1 Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 11 May 2026 05:38:13 -0400 Subject: [PATCH 06/15] Process --- .temp/baoExample.py | 33 ++++++++++++++++++++++++++++++++ download.py => .temp/download.py | 0 2 files changed, 33 insertions(+) create mode 100644 .temp/baoExample.py rename download.py => .temp/download.py (100%) diff --git a/.temp/baoExample.py b/.temp/baoExample.py new file mode 100644 index 0000000..c897d3c --- /dev/null +++ b/.temp/baoExample.py @@ -0,0 +1,33 @@ + import baostock as bs + import pandas as pd + + #### 登陆系统 #### + lg = bs.login() + # 显示登陆返回信息 + print('login respond error_code:'+lg.error_code) + print('login respond error_msg:'+lg.error_msg) + + #### 获取沪深A股历史K线数据 #### + # 详细指标参数，参见“历史行情指标参数”章节；“分钟线”参数与“日线”参数不同。“分钟线”不包含指数。 + # 分钟线指标：date,time,code,open,high,low,close,volume,amount,adjustflag + # 周月线指标：date,code,open,high,low,close,volume,amount,adjustflag,turn,pctChg + rs = bs.query_history_k_data_plus("sh.600000", + "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST", + start_date='2024-07-01', end_date='2024-12-31', + frequency="d", adjustflag="3") + print('query_history_k_data_plus respond error_code:'+rs.error_code) + print('query_history_k_data_plus respond error_msg:'+rs.error_msg) + + #### 打印结果集 #### + data_list = [] + while (rs.error_code == '0') & rs.next(): + # 获取一条记录，将记录合并在一起 + data_list.append(rs.get_row_data()) + result = pd.DataFrame(data_list, columns=rs.fields) + + #### 结果集输出到csv文件 #### + result.to_csv("D:\\history_A_stock_k_data.csv", index=False) + print(result) + + #### 登出系统 #### + bs.logout() \ No newline at end of file diff --git a/download.py b/.temp/download.py similarity index 100% rename from download.py rename to .temp/download.py From 9ea0a685d0b43a90553e166b073f861f5b1f6d77 Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 11 May 2026 05:47:25 -0400 Subject: [PATCH 07/15] Process --- arXiv-2507.07107v1.tex | 652 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 652 insertions(+) create mode 100644 arXiv-2507.07107v1.tex diff --git a/arXiv-2507.07107v1.tex b/arXiv-2507.07107v1.tex new file mode 100644 index 0000000..10399cd --- /dev/null +++ b/arXiv-2507.07107v1.tex @@ -0,0 +1,652 @@ +\documentclass[conference]{IEEEtran} +\IEEEoverridecommandlockouts +\usepackage{cite} +\usepackage{amsmath,amssymb,amsfonts} +\usepackage{algorithmic} +\usepackage{graphicx} +\usepackage{textcomp} +\usepackage{xcolor} +\usepackage{booktabs} +\usepackage{multirow} +\usepackage{url} + +\def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em + T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}} + +\begin{document} + +\title{Machine Learning Enhanced Multi-Factor Quantitative Trading: A Cross-Sectional Portfolio Optimization Approach with Bias Correction} + +\author{\IEEEauthorblockN{Yimin Du} +\IEEEauthorblockA{ +Beijing, China \\ +sa613403@mail.ustc.edu.cn} +} + +\maketitle + +\begin{abstract} +This paper presents a comprehensive machine learning framework for quantitative trading that achieves superior risk-adjusted returns through systematic factor engineering, real-time computation optimization, and cross-sectional portfolio construction. Our approach integrates multi-factor alpha discovery with bias correction techniques, leveraging PyTorch-accelerated factor computation and advanced portfolio optimization. The system processes 500-1000 factors derived from open-source alpha101 extensions and proprietary market microstructure signals. Key innovations include tensor-based factor computation acceleration, geometric Brownian motion data augmentation, and cross-sectional neutralization strategies. Empirical validation on Chinese A-share markets (2010-2024) demonstrates annualized returns of 20\% with Sharpe ratios exceeding 2.0, significantly outperforming traditional approaches. Our analysis reveals the critical importance of bias correction in factor construction and the substantial impact of cross-sectional portfolio optimization on strategy performance.\footnote{Code and experimental implementations are available at: \url{https://github.com/initial-d/ml-quant-trading}} +\end{abstract} + +\begin{IEEEkeywords} +quantitative trading, machine learning, factor models, portfolio optimization, bias correction, cross-sectional analysis +\end{IEEEkeywords} + + +\section{Introduction} + +The quantitative finance landscape has undergone significant transformation, driven by advances in machine learning, computational acceleration, and sophisticated portfolio optimization techniques. Modern quantitative trading systems have evolved beyond traditional statistical arbitrage, incorporating deep learning architectures, real-time factor computation, and advanced bias correction methods. This evolution reflects the increasing complexity of financial markets and the need for robust, scalable trading frameworks. + +Recent developments in factor investing have moved beyond traditional Fama-French factors to explore hundreds of potential alpha sources derived from market microstructure and alternative data streams. However, the computational demands of processing large factor universes in real-time have created significant bottlenecks, while systematic biases in factor construction often compromise strategy performance through unintended risk exposures. + +The emergence of GPU-accelerated computation and tensor-based operations has enabled order-of-magnitude improvements in factor processing speed, making real-time computation of complex factor universes feasible. Simultaneously, advances in bias correction techniques, particularly cross-sectional neutralization methods, have addressed the challenge of factor crowding and systematic risk exposures that plague traditional approaches. + +Cross-sectional portfolio optimization has gained prominence over traditional time-series methods, focusing on relative performance within investment universes rather than absolute return prediction. This paradigm shift naturally hedges market risk while concentrating on security selection alpha, leading to more stable risk-adjusted returns. + +This paper presents a comprehensive machine learning framework that systematically integrates these technological advances through: (1) a systematic factor engineering pipeline incorporating state-of-the-art bias correction and stability assessment; (2) a PyTorch-based computational acceleration framework achieving substantial speedup through tensor-based operations; (3) comprehensive comparison across multiple model architectures including gradient boosting, deep neural networks, and transformer models; (4) novel application of geometric Brownian motion data augmentation for financial time series; and (5) rigorous cross-sectional portfolio optimization incorporating transaction costs and multiple constraint types. + +Our empirical validation on Chinese A-share markets demonstrates the framework's effectiveness, achieving annualized returns of approximately 20\% with Sharpe ratios exceeding 2.0 during 2021-2024, using models trained exclusively on 2010-2020 data. The framework's modular architecture enables systematic analysis of individual component contributions, revealing the critical importance of bias correction and cross-sectional optimization in strategy performance. + +\section{Related Work} + +\subsection{Factor Models and Engineering} + +Factor-based investing traces its foundations to the Capital Asset Pricing Model (CAPM) \cite{b1} and Arbitrage Pricing Theory (APT) \cite{b2}, establishing the mathematical framework for multi-factor return decomposition. The Fama-French three-factor and five-factor models introduced size, value, profitability, and investment factors that became cornerstones of modern portfolio theory. + +The Alpha101 factor library represents a systematic approach to factor construction, providing 101 mathematically defined factors spanning momentum, reversal, volatility, and cross-sectional patterns. However, recent research has revealed significant limitations including high correlation structures, regime instability, and systematic biases that compromise out-of-sample performance. + +Green, Hand, and Zhang's comprehensive analysis of 330+ anomalies demonstrated the critical importance of robust factor evaluation methodologies, highlighting how publication bias and data mining concerns affect factor reliability. Their work established rigorous standards for factor validation that influence contemporary systematic factor construction. + +\subsection{Machine Learning in Financial Markets} + +Tree-based models, particularly gradient boosting machines, have shown exceptional performance in financial applications. Chen and Guestrin's XGBoost algorithm \cite{b3} provided superior predictive performance and interpretability through feature importance analysis. Subsequent developments including LightGBM \cite{b4} further refined these approaches with improvements in categorical variable handling and overfitting reduction. + +The transformer architecture \cite{b5}, originally developed for natural language processing, has found success in financial time series modeling. Self-attention mechanisms effectively capture complex temporal dependencies in factor effectiveness, leading to superior return prediction accuracy compared to traditional approaches. + +Deep learning applications have progressed from simple feedforward networks to sophisticated architectures incorporating attention mechanisms and recurrent structures. Recent work demonstrates how these architectures can capture long-range dependencies in market data previously inaccessible to conventional time series models. + +\subsection{Computational Acceleration and Bias Correction} + +The computational demands of modern quantitative systems have driven innovations in parallel processing and hardware acceleration. The transition from CPU-based pandas operations to GPU-accelerated tensor computations enables real-time processing of large-scale factor universes. + +Systematic biases in factor construction have emerged as critical concerns in quantitative finance. Traditional approaches often embed unintended exposures to market capitalization, industry membership, and other systematic risk factors. Industry and size neutralization techniques have evolved from simple cross-sectional regression to sophisticated machine learning-based methods that balance bias removal with signal preservation. + +Recent work by Harvey, Liu, and Zhu demonstrates how traditional neutralization methods can be enhanced through regularization techniques, while dynamic neutralization approaches adapt neutralization strength based on market regime characteristics. + +\subsection{Portfolio Optimization} + +Modern portfolio optimization has evolved beyond the mean-variance framework to incorporate transaction costs, market impact functions, and complex constraint structures. Cross-sectional portfolio optimization represents a paradigm shift from traditional time-series approaches, focusing on relative performance within investment universes rather than absolute return prediction. + +Risk parity and volatility targeting strategies have gained prominence as alternatives to traditional mean-variance optimization. Multi-objective optimization frameworks handle competing objectives through Pareto-optimal solutions, explicitly balancing return maximization, risk minimization, and transaction cost control. + +Recent advances in quadratic programming solvers and constraint formulation techniques enable efficient solution of large-scale portfolio optimization problems with complex constraint structures, making sophisticated portfolio construction feasible in real-time trading environments. + + +\section{Methodology} + +Our methodology encompasses a comprehensive machine learning framework designed to address the fundamental challenges in quantitative trading: factor stability, computational efficiency, model robustness, and portfolio optimization. This section details the core algorithmic innovations and design considerations that enable superior risk-adjusted performance in dynamic market environments. + +\subsection{Factor Engineering and Discovery Pipeline} + +\subsubsection{Multi-Source Factor Construction} + +The factor engineering pipeline begins with systematic construction of a comprehensive factor universe spanning multiple signal categories. Our approach extends the established Alpha101 foundation through principled factor family expansion and custom signal development. + +The Alpha101 factors provide a mathematically rigorous foundation covering momentum, reversal, volatility, and cross-sectional ranking patterns. Each factor undergoes careful implementation with attention to numerical stability and edge case handling: + +\begin{equation} +\alpha_{i,t} = \text{rank}\left(\frac{\text{close}_{i,t} - \text{close}_{i,t-d}}{\text{close}_{i,t-d}}\right) \times \text{rank}(\text{volume}_{i,t}) +\end{equation} + +where rank operations are computed cross-sectionally across the investment universe at each time point $t$. + +Beyond Alpha101, we develop proprietary market microstructure factors that capture systematic patterns in large-cap distribution dynamics. These factors recognize that individual security performance often reflects broader market regime characteristics: + +\begin{equation} +\beta_{market,i,t} = \frac{\text{cov}(r_{i,t-w:t}, r_{market,t-w:t})}{\text{var}(r_{market,t-w:t})} +\end{equation} + +where $w$ represents the estimation window and $r_{market,t}$ denotes market-wide return patterns derived from large-cap index constituents. + +\subsubsection{Factor Quality Assessment Framework} + +Factor effectiveness evaluation employs a multi-dimensional assessment framework that balances predictive power with stability considerations. The core evaluation metric, Information Coefficient (IC), measures the cross-sectional correlation between factor values and subsequent returns: + +\begin{equation} +IC_t = \rho\left(f_{i,t}, r_{i,t+1}\right) = \frac{\sum_i (f_{i,t} - \bar{f}_t)(r_{i,t+1} - \bar{r}_{t+1})}{\sqrt{\sum_i (f_{i,t} - \bar{f}_t)^2 \sum_i (r_{i,t+1} - \bar{r}_{t+1})^2}} +\end{equation} + +where $f_{i,t}$ represents the factor value for security $i$ at time $t$, and $r_{i,t+1}$ denotes the forward return. + +The Information Ratio (IR) provides a risk-adjusted measure of factor effectiveness by normalizing mean IC by its temporal volatility: + +\begin{equation} +IR = \frac{E[IC]}{\sigma(IC)} = \frac{\frac{1}{T}\sum_{t=1}^T IC_t}{\sqrt{\frac{1}{T-1}\sum_{t=1}^T (IC_t - \bar{IC})^2}} +\end{equation} + +This metric captures both the average predictive power and the consistency of factor performance across different market regimes. + +Factor decay analysis examines the temporal persistence of predictive relationships through rolling IC calculations across multiple prediction horizons. Factors demonstrating rapid decay or unstable performance patterns are excluded from the final universe to ensure strategy robustness. + +\subsection{Bias Correction and Neutralization Algorithms} + +\subsubsection{Systematic Risk Factor Identification} + +Raw factor signals often embed unintended systematic exposures that compromise strategy performance and introduce unwanted risk concentrations. Our bias correction framework systematically identifies and removes these exposures while preserving alpha-generating characteristics. + +The neutralization process begins with systematic risk factor identification through principal component analysis of factor loadings across different risk dimensions: + +\begin{equation} +F_{raw} = \sum_{k=1}^K \lambda_k \mathbf{v}_k \mathbf{v}_k^T + \epsilon +\end{equation} + +where $\lambda_k$ and $\mathbf{v}_k$ represent the eigenvalues and eigenvectors of the factor correlation matrix, and $K$ denotes the number of significant risk factors to neutralize. + +\subsubsection{Multi-Stage Neutralization Process} + +The bias correction algorithm employs a multi-stage approach that sequentially removes different categories of systematic bias. Industry neutralization addresses sector-specific biases through cross-sectional regression: + +\begin{equation} +f_{i,t}^{(1)} = f_{i,t}^{raw} - \sum_{j=1}^{J} \beta_{j,t} \mathbf{I}_{i,j,t} +\end{equation} + +where $\mathbf{I}_{i,j,t}$ represents industry dummy variables and $\beta_{j,t}$ denotes industry-specific factor loadings estimated through robust regression techniques. + +Market capitalization neutralization removes size-related biases through log-linear adjustment: + +\begin{equation} +f_{i,t}^{(2)} = f_{i,t}^{(1)} - \gamma_t \log(\text{MarketCap}_{i,t}) - \delta_t [\log(\text{MarketCap}_{i,t})]^2 +\end{equation} + +The quadratic term captures non-linear size effects that may not be adequately addressed through linear adjustment alone. + +\subsubsection{Adaptive Neutralization Strength} + +Rather than applying fixed neutralization parameters, our framework incorporates adaptive strength adjustment based on market regime characteristics. The neutralization strength parameter $\alpha_t$ varies based on factor stability and market volatility: + +\begin{equation} +\alpha_t = \alpha_0 \times \left(1 + \beta_{vol} \times \frac{\sigma_{t-w:t} - \sigma_{long}}{\sigma_{long}}\right) +\end{equation} + +where $\sigma_{t-w:t}$ represents short-term market volatility, $\sigma_{long}$ denotes long-term volatility baseline, and $\beta_{vol}$ controls sensitivity to volatility regime changes. + +This adaptive approach recognizes that optimal neutralization strength varies across different market conditions, with higher volatility periods typically requiring stronger bias correction to maintain factor stability. + +\subsection{PyTorch-Based Computational Acceleration} + +\subsubsection{Tensor Operation Optimization} + +Traditional pandas-based factor computation becomes computationally prohibitive when processing large factor universes in real-time. Our PyTorch optimization framework addresses this challenge through systematic transformation of sequential operations into parallel tensor computations. + +The key insight involves recognizing that many financial operations can be reformulated as tensor operations amenable to GPU acceleration. Rolling window operations, fundamental to many factor calculations, are transformed into convolution operations: + +\begin{equation} +\text{Rolling}(X, w) \rightarrow \text{Conv1D}(X, \text{kernel}=\mathbf{1}_w, \text{padding}=w-1) +\end{equation} + +where $\mathbf{1}_w$ represents a uniform kernel of length $w$, and appropriate padding ensures output dimensionality consistency. + +Cross-sectional ranking operations, critical for many Alpha101 factors, are accelerated through parallel sorting algorithms: + +\begin{equation} +\text{Rank}(X) \rightarrow \text{argsort}(\text{argsort}(X, \text{dim}=1), \text{dim}=1) + 1 +\end{equation} + +This double argsort operation efficiently computes ranks across the cross-sectional dimension while maintaining numerical stability. + +\subsubsection{Memory-Efficient Factor Computation} + +Large-scale factor computation requires careful memory management to prevent GPU memory overflow while maintaining computational efficiency. Our implementation employs chunked processing with intelligent caching: + +\begin{equation} +F_{total} = \bigcup_{c=1}^C \text{compute\_chunk}(X_c, \text{cache}_{c-1}) +\end{equation} + +where $C$ represents the number of processing chunks, and $\text{cache}_{c-1}$ contains relevant historical data from previous chunks. + +Exponentially weighted moving averages (EWMA), commonly used in factor construction, benefit from recursive computation optimization: + +\begin{equation} +\text{EWMA}_t = \alpha \times X_t + (1-\alpha) \times \text{EWMA}_{t-1} +\end{equation} + +The recursive structure enables efficient GPU implementation with minimal memory footprint through in-place updates. + +\subsection{Machine Learning Model Architecture Design} + +\subsubsection{Ensemble Architecture Framework} + +Our modeling approach employs a sophisticated ensemble framework that combines multiple complementary architectures to capture different aspects of return generation processes. Each component model addresses specific characteristics of financial time series while the ensemble framework balances their contributions optimally. + +Tree-based models excel in capturing non-linear factor interactions and handling mixed data types without extensive preprocessing. The gradient boosting framework naturally provides feature importance rankings and interpretable factor contributions: + +\begin{equation} +\hat{y}_m = \hat{y}_{m-1} + \eta \times h_m(x) +\end{equation} + +where $h_m(x)$ represents the $m$-th weak learner, $\eta$ denotes the learning rate, and the sequential addition captures residual patterns missed by previous iterations. + +\subsubsection{Deep Neural Network Architecture} + +Deep neural networks offer superior capacity for learning complex factor combinations and non-linear transformations. Our DNN architecture incorporates several key innovations tailored for financial applications: + +Residual connections address gradient flow challenges in deep networks while enabling effective training of complex factor relationships: + +\begin{equation} +h_{l+1} = \sigma(W_l h_l + b_l) + h_l +\end{equation} + +where the skip connection preserves information flow across multiple layers. + +Batch normalization addresses the non-stationary nature of financial data by normalizing inputs at each layer: + +\begin{equation} +\text{BN}(x) = \gamma \frac{x - \mu_B}{\sqrt{\sigma_B^2 + \epsilon}} + \beta +\end{equation} + +where $\mu_B$ and $\sigma_B^2$ represent batch mean and variance, while $\gamma$ and $\beta$ are learnable parameters. + +\subsubsection{Transformer Architecture for Sequential Dependencies} + +Transformer models capture long-range dependencies in factor time series through self-attention mechanisms. The multi-head attention computation enables the model to focus on different aspects of temporal relationships: + +\begin{equation} +\text{MultiHead}(Q, K, V) = \text{Concat}(\text{head}_1, ..., \text{head}_h)W^O +\end{equation} + +where each attention head is computed as: + +\begin{equation} +\text{head}_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V) +\end{equation} + +The attention mechanism computes: + +\begin{equation} +\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V +\end{equation} + +This formulation enables the model to dynamically weight different time periods based on their relevance to current prediction tasks. + +\subsection{Geometric Brownian Motion Data Augmentation} + +\subsubsection{Synthetic Data Generation Framework} + +Financial machine learning faces fundamental challenges related to limited training data and non-stationary return distributions. Our data augmentation approach employs Geometric Brownian Motion (GBM) simulation to generate synthetic price paths that preserve essential statistical properties while expanding the training dataset. + +The GBM process generates realistic price evolution through stochastic differential equation simulation: + +\begin{equation} +dS_t = \mu S_t dt + \sigma S_t dW_t +\end{equation} + +where $\mu$ represents the drift parameter, $\sigma$ denotes volatility, and $dW_t$ is a Wiener process increment. + +The discrete-time implementation employs the Euler-Maruyama scheme: + +\begin{equation} +S_{t+\Delta t} = S_t \exp\left[\left(\mu - \frac{\sigma^2}{2}\right)\Delta t + \sigma \sqrt{\Delta t} Z_t\right] +\end{equation} + +where $Z_t \sim \mathcal{N}(0,1)$ represents standard normal random variables. + +\subsubsection{Parameter Estimation and Calibration} + +GBM parameter estimation employs maximum likelihood estimation on historical return data. The drift parameter is estimated as: + +\begin{equation} +\hat{\mu} = \frac{1}{n\Delta t} \sum_{i=1}^n \log\left(\frac{S_{t_i}}{S_{t_{i-1}}}\right) + \frac{\hat{\sigma}^2}{2} +\end{equation} + +Volatility estimation uses the standard sample volatility of log returns: + +\begin{equation} +\hat{\sigma}^2 = \frac{1}{(n-1)\Delta t} \sum_{i=1}^n \left[\log\left(\frac{S_{t_i}}{S_{t_{i-1}}}\right) - \hat{\mu}\Delta t\right]^2 +\end{equation} + +The augmented dataset maintains statistical consistency with observed data while providing sufficient samples for robust model training across different market regimes. + +\subsection{Cross-Sectional Portfolio Optimization} + +\subsubsection{Multi-Objective Optimization Formulation} + +Portfolio construction employs a sophisticated multi-objective optimization framework that balances return maximization, risk minimization, and transaction cost control. The optimization problem is formulated as: + +\begin{align} +\max_{w} \quad & w^T \hat{\mu} - \frac{\lambda}{2} w^T \Sigma w - \gamma \sum_i c_i |w_i - w_{i,t-1}| \\ +\text{subject to} \quad & \sum_i w_i = 0 \quad \text{(market neutral)} \\ +& |w_i| \leq w_{\max} \quad \text{(position limits)} \\ +& \sum_i |w_i| \leq L \quad \text{(leverage constraint)} \\ +& \sum_{i \in S_j} w_i = 0 \quad \text{(sector neutral)} +\end{align} + +where $\hat{\mu}$ represents predicted returns, $\Sigma$ denotes the covariance matrix, $c_i$ represents transaction costs, and $\lambda, \gamma$ are risk and transaction cost aversion parameters. + +\subsubsection{Risk Model Construction} + +The risk model construction employs a multi-factor approach that decomposes return covariance into systematic and idiosyncratic components: + +\begin{equation} +\Sigma = B \Omega B^T + \Delta +\end{equation} + +where $B$ represents factor loadings, $\Omega$ denotes factor covariance, and $\Delta$ is the diagonal matrix of idiosyncratic variances. + +Factor risk estimation employs exponentially weighted covariance matrices to adapt to changing market conditions: + +\begin{equation} +\Omega_t = (1-\lambda) \sum_{s=0}^{\infty} \lambda^s f_{t-s-1} f_{t-s-1}^T +\end{equation} + +where $\lambda$ represents the decay parameter and $f_t$ denotes factor returns at time $t$. + +\subsubsection{Optimization Algorithm Implementation} + +The constrained optimization problem is solved using the MOSEK quadratic programming solver, which efficiently handles the quadratic objective function with linear equality and inequality constraints. The solver employs interior-point methods that scale well with portfolio size and constraint complexity. + +To ensure numerical stability and convergence, the optimization problem is reformulated using appropriate scaling and regularization techniques: + +\begin{equation} +\tilde{\Sigma} = \Sigma + \epsilon I +\end{equation} + +where $\epsilon$ represents a small regularization parameter that ensures positive definiteness of the covariance matrix. + +The optimization process incorporates warm-start procedures that utilize previous solutions as initial points, significantly improving computational efficiency in dynamic rebalancing scenarios. The warm-start approach recognizes that consecutive portfolio optimization problems share similar structure and optimal solutions tend to evolve gradually rather than changing dramatically between rebalancing periods. + + +\section{Experimental Setup} + +Our experimental framework is designed to provide comprehensive evaluation of the proposed machine learning-enhanced quantitative trading system across multiple dimensions. The experimental design addresses key challenges in financial machine learning including data quality, model selection, hyperparameter optimization, and performance attribution. This section details the experimental configuration, comparative baselines, and evaluation methodologies employed to validate our approach. + +\subsection{Dataset Construction and Preprocessing} + +\subsubsection{Primary Dataset} + +Our empirical analysis utilizes high-quality daily Chinese A-share market data spanning January 2010 to December 2024, representing one of the world's largest and most dynamic equity markets. The dataset encompasses comprehensive coverage of over 4,000 individual securities listed on the Shanghai and Shenzhen stock exchanges, including main board, ChiNext, and STAR Market securities. + +The temporal split follows a rigorous walk-forward validation approach: training data covers 2010-2020 (11 years), validation data spans 2020-2021 (1 year), and out-of-sample testing encompasses 2021-2024 (3 years). This configuration ensures sufficient training history while providing robust out-of-sample validation across different market regimes, including the COVID-19 market disruption, regulatory policy changes, and evolving market microstructure. + +Data quality control procedures include comprehensive survivorship bias correction, dividend and split adjustments, and systematic outlier detection. Securities with insufficient trading history (less than 250 trading days), extreme price movements (daily returns exceeding 20\%), or irregular trading patterns are excluded from the analysis. + +\subsubsection{Auxiliary Data Sources} + +Beyond standard price and volume data, our analysis incorporates multiple auxiliary data streams essential for comprehensive factor construction. Fundamental data includes quarterly financial statements, analyst estimates, and corporate actions, sourced from major Chinese financial data providers with rigorous quality validation procedures. + +Index composition data enables precise sector classification and benchmark construction, utilizing both GICS and local Chinese industry classification standards. Market microstructure data, including intraday tick-level information, supports the construction of sophisticated market microstructure factors that capture short-term trading dynamics. + +Macroeconomic indicators, including interest rates, exchange rates, commodity prices, and policy announcement timings, provide context for regime identification and risk factor construction. These macro variables enable the construction of systematic risk factors that complement security-specific alpha signals. + +\subsection{Factor Universe Construction} + +\subsubsection{Alpha101 Factor Implementation} + +The foundational factor universe builds upon the open-source Alpha101 factor library, with careful implementation ensuring mathematical accuracy and computational efficiency. Each factor undergoes rigorous validation against published specifications, with particular attention to edge cases and numerical stability concerns. + +Factor computation employs rolling windows ranging from 5 to 252 trading days, capturing effects across multiple time horizons from short-term momentum to long-term reversal patterns. Cross-sectional ranking operations utilize rank-based transformations to ensure factor distributions remain stable across different market regimes. + +The Alpha101 implementation is extended through systematic parameter variations, creating factor families that explore different lookback periods, smoothing parameters, and cross-sectional universe definitions. This systematic exploration generates approximately 300-400 factors from the core Alpha101 specifications. + +\subsubsection{Proprietary Factor Development} + +Custom factor development focuses on market microstructure signals and systematic distribution patterns not captured by traditional technical indicators. Large-cap distribution signals examine the relationship between individual security performance and broad market movements, capturing systematic patterns in market leadership and rotation effects. + +Volatility regime factors incorporate heteroskedasticity patterns and volatility clustering effects through sophisticated statistical measures including realized volatility, volatility-of-volatility metrics, and regime-dependent volatility estimates. These factors capture the time-varying nature of market risk that traditional volatility measures may miss. + +Cross-sectional momentum and reversal factors extend traditional time-series approaches by incorporating relative performance dynamics within sector and size cohorts. These factors recognize that momentum and reversal effects often manifest differently across market segments. + +\subsubsection{Factor Quality Control} + +Factor quality control procedures ensure robustness and stability across the extended evaluation period. Each factor undergoes systematic evaluation across multiple quality metrics including Information Coefficient (IC) stability, turnover characteristics, and correlation with systematic risk factors. + +Factors demonstrating IC decay, excessive parameter sensitivity, or unstable cross-sectional distributions are excluded from the final universe. The quality control process typically reduces the candidate factor universe from 1,000+ potential factors to approximately 500-800 factors that meet stringent stability and effectiveness criteria. + +\subsection{Model Architecture Configurations} + +\subsubsection{Tree-Based Model Settings} + +XGBoost configurations employ extensive hyperparameter optimization across multiple dimensions. Tree depth varies from 3 to 8 levels, balancing model complexity with overfitting control. Learning rates range from 0.01 to 0.3, with early stopping criteria preventing excessive training beyond optimal generalization points. + +Regularization parameters including L1 and L2 penalties undergo systematic grid search optimization. Subsample ratios and feature sampling rates provide additional overfitting protection while maintaining model expressiveness. The final XGBoost configuration typically employs 1000-3000 estimators with learning rates of 0.05-0.1. + +LightGBM implementations explore gradient-based one-side sampling (GOSS) and exclusive feature bundling (EFB) techniques for improved computational efficiency. Categorical feature handling employs both traditional one-hot encoding and LightGBM's native categorical support, with performance comparison informing final configuration choices. + +Random Forest implementations serve as ensemble baselines, with tree counts ranging from 500 to 2000 and maximum depth controls preventing overfitting. Feature importance analysis from Random Forest models provides interpretability benchmarks for comparison with gradient boosting approaches. + +\subsubsection{Deep Neural Network Architectures} + +Deep neural network configurations explore multiple architectural variations to identify optimal designs for financial prediction tasks. Feed-forward architectures range from 2 to 6 hidden layers, with layer widths varying from 64 to 1024 neurons per layer. + +Residual connections are systematically evaluated for their impact on gradient flow and training stability in financial applications. Batch normalization and layer normalization techniques are compared for their effectiveness in handling the non-stationary nature of financial data. + +Dropout rates undergo careful optimization, with values ranging from 0.1 to 0.5 across different layers. The financial data's high noise characteristics require careful balance between regularization strength and model capacity. + +Advanced activation functions including Swish, GELU, and Mish are evaluated against traditional ReLU activations. Learning rate scheduling employs cosine annealing and warm restart techniques to improve convergence characteristics. + +\subsubsection{Transformer Model Specifications} + +Transformer architectures are adapted for financial time series through careful attention mechanism design and positional encoding strategies. Multi-head attention configurations range from 4 to 16 heads, with embedding dimensions from 128 to 512. + +Sequence length optimization balances computational efficiency with temporal dependency capture. Window sizes from 10 to 60 trading days are systematically evaluated to identify optimal temporal context for return prediction. + +Positional encoding strategies include both absolute and relative position embeddings, with custom financial time encodings that account for trading calendar irregularities and market closure effects. + +Transformer regularization employs attention dropout, feed-forward dropout, and gradient clipping techniques specifically tuned for financial applications. The non-stationary nature of financial data requires careful regularization to prevent overfitting to specific market regimes. + +\subsection{Training and Validation Procedures} + +\subsubsection{Cross-Validation Strategy} + +Time series cross-validation employs expanding window approaches that respect temporal dependencies in financial data. Training windows expand progressively while maintaining consistent validation periods, ensuring that model evaluation reflects realistic deployment scenarios. + +Purged cross-validation techniques address data leakage concerns inherent in financial time series, ensuring that validation samples maintain appropriate temporal separation from training data. Gap periods between training and validation data prevent information leakage through overlapping return calculations. + +Walk-forward optimization provides the most realistic evaluation framework, with models retrained periodically to adapt to changing market conditions. The retraining frequency varies from monthly to quarterly, with performance comparison informing optimal update schedules. + +\subsubsection{Hyperparameter Optimization} + +Bayesian optimization techniques employ Gaussian process models to efficiently explore hyperparameter spaces across multiple model architectures. Objective functions incorporate both prediction accuracy and risk-adjusted performance metrics to ensure robust model selection. + +Grid search and random search baselines provide comparison points for Bayesian optimization effectiveness. Multi-objective optimization explores Pareto frontiers between prediction accuracy and model interpretability, particularly relevant for institutional deployment requirements. + +Hyperparameter stability analysis examines model sensitivity to parameter variations, ensuring that optimal configurations remain robust across different market conditions. This analysis identifies critical parameters requiring careful tuning versus those with minimal performance impact. + +\subsection{Baseline Comparisons and Benchmarks} + +\subsubsection{Traditional Quantitative Baselines} + +Linear regression baselines with L1 and L2 regularization provide fundamental performance comparisons, establishing minimum performance thresholds for more sophisticated approaches. Ridge and Lasso implementations employ cross-validation for optimal regularization strength selection. + +Traditional technical analysis indicators, including moving averages, RSI, and MACD signals, serve as naive baselines representing common practitioner approaches. These baselines establish the performance improvement achieved through systematic factor engineering and machine learning techniques. + +Fama-French factor models provide academic benchmarks, with three-factor and five-factor implementations establishing performance standards from traditional asset pricing literature. These benchmarks help contextualize the economic significance of machine learning improvements. + +\subsubsection{Advanced Machine Learning Baselines} + +Support Vector Machine (SVM) implementations with multiple kernel configurations provide non-linear baseline comparisons. RBF, polynomial, and linear kernels undergo systematic evaluation with hyperparameter optimization ensuring fair comparison. + +Ensemble methods including Random Forest, Extra Trees, and Gradient Boosting variants provide robust baseline comparisons. These implementations employ identical feature sets and evaluation procedures to ensure fair performance attribution. + +Time series specific models including LSTM, GRU, and traditional ARIMA approaches provide temporal modeling baselines. These comparisons highlight the relative advantages of cross-sectional versus time-series modeling approaches in financial prediction. + +\subsection{Performance Evaluation Framework} + +\subsubsection{Statistical Performance Metrics} + +Prediction accuracy evaluation employs multiple correlation measures including Pearson correlation, Spearman rank correlation, and Kendall's tau to capture different aspects of predictive performance. These metrics provide comprehensive assessment of both linear and monotonic relationships between predictions and realized returns. + +Mean squared error (MSE) and mean absolute error (MAE) provide loss function perspectives on model performance, with particular attention to tail performance characteristics important in financial applications. + +Information Coefficient (IC) analysis includes rolling IC calculation, IC decay analysis, and IC stability metrics that capture the temporal consistency of predictive performance. These metrics are particularly relevant for quantitative trading applications where consistency matters more than peak performance. + +\subsubsection{Economic Performance Metrics} + +Portfolio-level performance evaluation employs comprehensive risk-adjusted return metrics including Sharpe ratio, Information ratio, Calmar ratio, and Maximum Drawdown analysis. These metrics capture the economic significance of statistical improvements in prediction accuracy. + +Transaction cost analysis incorporates realistic cost assumptions including bid-ask spreads, market impact functions, and commission structures relevant to Chinese A-share markets. Net performance after transaction costs provides realistic assessment of strategy viability. + +Capacity analysis examines strategy performance across different portfolio sizes and turnover levels, providing insights into scalability limitations and optimal deployment parameters. + +Attribution analysis decomposes performance sources across factor categories, model components, and market regimes. This analysis provides insights into strategy robustness and identifies sources of alpha generation. + +\subsubsection{Risk Analysis and Stress Testing} + +Factor exposure analysis ensures that strategy performance derives from intended alpha sources rather than unintended systematic risk exposures. Style analysis decomposes returns across size, value, momentum, and quality factors. + +Regime analysis examines strategy performance across different market conditions including bull markets, bear markets, high volatility periods, and regulatory change periods relevant to Chinese markets. + +Tail risk analysis employs Value-at-Risk (VaR) and Expected Shortfall (ES) measures to characterize downside risk characteristics. Monte Carlo simulation provides additional perspective on extreme scenario performance. + +Rolling performance analysis with varying window sizes provides insights into strategy stability and adaptation requirements across different market conditions. + + +\section{Results and Analysis} + +\subsection{Factor Engineering Results} + +Factor engineering produces a stable universe of 742 factors after bias correction and stability filtering. The neutralization process significantly improves factor quality, with mean IC increasing from 0.023 to 0.041 after industry and size neutralization. + +Table \ref{tab:factor_performance} summarizes factor performance metrics before and after bias correction. The neutralization process demonstrates substantial improvements across all quality metrics, validating the importance of systematic bias removal. + +\begin{table}[htbp] +\centering +\caption{Factor Performance Before and After Bias Correction} +\label{tab:factor_performance} +\begin{tabular}{lcc} +\toprule +Metric & Raw Factors & Neutralized Factors \\ +\midrule +Mean IC & 0.023 & 0.041 \\ +IC Std & 0.156 & 0.089 \\ +Information Ratio & 0.147 & 0.461 \\ +Positive IC Rate & 54.2\% & 67.8\% \\ +\bottomrule +\end{tabular} +\end{table} + +\subsection{Computational Performance} + +PyTorch optimization achieves substantial computational improvements over traditional pandas operations. Table \ref{tab:computation_speedup} demonstrates speedup factors across different operation types. + +\begin{table}[htbp] +\centering +\caption{Computational Speedup with PyTorch Optimization} +\label{tab:computation_speedup} +\begin{tabular}{lcc} +\toprule +Operation Type & Pandas Time (s) & PyTorch Speedup \\ +\midrule +Rolling Mean & 12.4 & 23.1x \\ +Cross-sectional Rank & 8.7 & 31.5x \\ +EWMA & 15.2 & 18.9x \\ +Complex Factor & 45.6 & 42.3x \\ +\bottomrule +\end{tabular} +\end{table} + +The acceleration enables real-time factor computation, crucial for high-frequency strategy implementation and dynamic portfolio rebalancing. + +\subsection{Model Performance Comparison} + +Table \ref{tab:model_comparison} presents comprehensive performance comparison across different model architectures. The transformer model achieves the highest prediction accuracy, while tree-based models demonstrate superior interpretability. + +\begin{table}[htbp] +\centering +\caption{Model Performance Comparison} +\label{tab:model_comparison} +\begin{tabular}{lccc} +\toprule +Model & Correlation & Sharpe Ratio & Annual Return \\ +\midrule +XGBoost & 0.087 & 1.82 & 18.3\% \\ +LightGBM & 0.091 & 1.89 & 19.1\% \\ +DNN & 0.094 & 1.95 & 19.7\% \\ +Transformer & 0.102 & 2.01 & 20.4\% \\ +\bottomrule +\end{tabular} +\end{table} + +\subsection{Portfolio Performance Analysis} + +The complete trading strategy achieves annualized returns of 20.4\% with Sharpe ratios of 2.01 during the 2021-2024 testing period. Figure \ref{fig:cumulative_returns} illustrates cumulative performance compared to market benchmarks. + +Maximum drawdown remains below 8\%, demonstrating effective risk control. The strategy maintains consistent performance across different market regimes, including the COVID-19 market disruption and subsequent recovery. + +\subsection{Cross-Sectional Analysis} + +Cross-sectional portfolio construction proves crucial for strategy performance. Market-neutral positioning eliminates systematic market exposure while preserving alpha generation capability. The cross-sectional approach ensures that profits derive from security selection rather than market timing. + +Analysis of position dynamics reveals effective factor rotation, with the model adapting factor weights based on regime-dependent performance. This adaptability contributes significantly to strategy robustness and sustained performance. + +\section{Discussion} + +\subsection{Bias Correction Impact} + +The experimental results clearly demonstrate the critical importance of bias correction in factor engineering. Raw factors often embed systematic biases that lead to unintended exposures and poor out-of-sample performance. Industry and size neutralization transforms these biased signals into pure alpha factors. + +The neutralization process requires careful balance between bias removal and signal preservation. Excessive neutralization can eliminate valuable predictive information, while insufficient correction leaves systematic biases intact. Our cross-sectional regression approach provides an effective middle ground. + +\subsection{Cross-Sectional Portfolio Optimization} + +Cross-sectional portfolio construction represents a fundamental paradigm shift from traditional time-series approaches. Rather than predicting absolute returns, the model focuses on relative performance within the investment universe. This approach naturally hedges market risk while concentrating on security selection alpha. + +The cross-sectional framework also enables effective risk management through position limits and sector constraints. Market-neutral positioning ensures that strategy performance remains independent of overall market direction, providing genuine alpha generation. + +\subsection{Advantages and Limitations of Daily Trading} + +Daily rebalancing strategies offer several advantages including reduced transaction costs compared to intraday strategies, sufficient time for comprehensive factor computation, and alignment with fundamental analysis timeframes. The daily frequency allows for sophisticated factor engineering while maintaining implementation feasibility. + +However, daily strategies also face limitations including exposure to overnight gaps, reduced trading opportunities compared to higher-frequency approaches, and potential capacity constraints in liquid markets. The 20-day position holding period balances transaction costs with signal decay considerations. + +\subsection{Model Architecture Insights} + +The transformer architecture's superior performance highlights the importance of temporal dependencies in factor effectiveness. Self-attention mechanisms effectively capture regime-dependent factor interactions, leading to improved prediction accuracy. + +Tree-based models provide valuable interpretability through feature importance analysis, enabling factor selection and risk management. The ensemble approach combining multiple architectures offers robustness against model-specific biases and overfitting. + +\subsection{Scalability and Implementation Considerations} + +The PyTorch optimization framework addresses scalability challenges inherent in large-scale factor processing. Real-time computation capabilities enable dynamic factor selection and adaptive portfolio construction. + +Implementation considerations include data quality management, model monitoring, and performance attribution. The systematic framework provides transparency and reproducibility essential for institutional deployment. + +\section{Conclusion} + +This paper presents a comprehensive machine learning framework for quantitative trading that addresses key challenges in factor engineering, computational efficiency, and portfolio optimization. The systematic approach to bias correction, PyTorch-based acceleration, and cross-sectional portfolio construction demonstrates significant performance improvements over traditional methods. + +Key findings include the critical importance of factor neutralization for stable performance, substantial computational benefits from tensor-based optimization, and superior risk-adjusted returns from transformer architectures. The cross-sectional portfolio framework provides effective market neutrality while preserving alpha generation capability. + +The empirical validation on Chinese A-share markets demonstrates robust performance with annualized returns of 20\% and Sharpe ratios exceeding 2.0. The framework's systematic approach ensures reproducibility and scalability for institutional implementation. + +Future research directions include incorporating alternative data sources, developing regime-aware models, and extending the framework to multi-asset class portfolios. The modular architecture facilitates continuous improvement and adaptation to evolving market conditions. + +The successful combination of systematic factor engineering, computational optimization, and rigorous portfolio construction provides a template for modern quantitative trading systems. The emphasis on bias correction and cross-sectional analysis offers valuable insights for practitioners seeking to develop robust, profitable trading strategies. + +\section*{Acknowledgments} + +The authors acknowledge the computational resources provided by institutional partners and the valuable feedback from industry practitioners. Special thanks to the open-source community for providing foundational tools and methodologies. + +\begin{thebibliography}{00} +\bibitem{b1} E. Fama and K. French, "Common risk factors in the returns on stocks and bonds," \textit{Journal of Financial Economics}, vol. 33, no. 1, pp. 3-56, 1993. + +\bibitem{b2} S. Ross, "The arbitrage theory of capital asset pricing," \textit{Journal of Economic Theory}, vol. 13, no. 3, pp. 341-360, 1976. + +\bibitem{b3} T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in \textit{Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, 2016, pp. 785-794. + +\bibitem{b4} G. Ke et al., "LightGBM: A highly efficient gradient boosting decision tree," in \textit{Advances in Neural Information Processing Systems}, 2017, pp. 3146-3154. + +\bibitem{b5} A. Vaswani et al., "Attention is all you need," in \textit{Advances in Neural Information Processing Systems}, 2017, pp. 5998-6008. + +\bibitem{b6} Z. Liu et al., "Alpha158: A benchmark for factor-based stock selection," \textit{arXiv preprint arXiv:2101.02555}, 2021. + +\bibitem{b7} M. López de Prado, "The 7 reasons most machine learning funds fail," \textit{The Journal of Portfolio Management}, vol. 44, no. 6, pp. 120-133, 2018. + +\bibitem{b8} J. Bouchaud et al., "The endogenous dynamics of markets: Price impact and feedback loops," \textit{Physica A: Statistical Mechanics and its Applications}, vol. 310, no. 3-4, pp. 243-259, 2002. + +\bibitem{b9} R. Grinold and R. Kahn, \textit{Active Portfolio Management}, McGraw-Hill, 2000. + +\bibitem{b10} A. Lo and A. MacKinlay, \textit{A Non-Random Walk Down Wall Street}, Princeton University Press, 1999. +\end{thebibliography} + +\end{document} \ No newline at end of file From 633113429633f00cffd5cbbbb5e93325e95f68e6 Mon Sep 17 00:00:00 2001 From: "google-labs-jules[bot]" <161369871+google-labs-jules[bot]@users.noreply.github.com> Date: Mon, 11 May 2026 10:46:41 +0000 Subject: [PATCH 08/15] Add Baostock loader and Google Colab demo - Add `src/mlquant/data/baostock_loader.py` for fetching A-share historical data using baostock. - Wire `baostock_loader` into `src/mlquant/data/__init__.py`. - Create `demo_baostock.ipynb` showing an end-to-end strategy fetching HS300 stocks on Baostock, computing factors, training an MLP, optimizing portfolio, and backtesting. - Update `README.md` to remove GitHub Codespaces instruction and add a Google Colab badge/link for the demo. - Remove `.temp/` contents per request. Co-authored-by: Uwater1 <194621470+Uwater1@users.noreply.github.com> --- .temp/baoExample.py | 33 ---- .temp/download.py | 40 ----- README.md | 32 ++-- demo_baostock.ipynb | 259 ++++++++++++++++++++++++++++ src/mlquant/data/__init__.py | 9 + src/mlquant/data/baostock_loader.py | 130 ++++++++++++++ 6 files changed, 418 insertions(+), 85 deletions(-) delete mode 100644 .temp/baoExample.py delete mode 100644 .temp/download.py create mode 100644 demo_baostock.ipynb create mode 100644 src/mlquant/data/baostock_loader.py diff --git a/.temp/baoExample.py b/.temp/baoExample.py deleted file mode 100644 index c897d3c..0000000 --- a/.temp/baoExample.py +++ /dev/null @@ -1,33 +0,0 @@ - import baostock as bs - import pandas as pd - - #### 登陆系统 #### - lg = bs.login() - # 显示登陆返回信息 - print('login respond error_code:'+lg.error_code) - print('login respond error_msg:'+lg.error_msg) - - #### 获取沪深A股历史K线数据 #### - # 详细指标参数，参见“历史行情指标参数”章节；“分钟线”参数与“日线”参数不同。“分钟线”不包含指数。 - # 分钟线指标：date,time,code,open,high,low,close,volume,amount,adjustflag - # 周月线指标：date,code,open,high,low,close,volume,amount,adjustflag,turn,pctChg - rs = bs.query_history_k_data_plus("sh.600000", - "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST", - start_date='2024-07-01', end_date='2024-12-31', - frequency="d", adjustflag="3") - print('query_history_k_data_plus respond error_code:'+rs.error_code) - print('query_history_k_data_plus respond error_msg:'+rs.error_msg) - - #### 打印结果集 #### - data_list = [] - while (rs.error_code == '0') & rs.next(): - # 获取一条记录，将记录合并在一起 - data_list.append(rs.get_row_data()) - result = pd.DataFrame(data_list, columns=rs.fields) - - #### 结果集输出到csv文件 #### - result.to_csv("D:\\history_A_stock_k_data.csv", index=False) - print(result) - - #### 登出系统 #### - bs.logout() \ No newline at end of file diff --git a/.temp/download.py b/.temp/download.py deleted file mode 100644 index eb8b886..0000000 --- a/.temp/download.py +++ /dev/null @@ -1,40 +0,0 @@ -import baostock as bs -import pandas as pd - -# 登陆系统 -lg = bs.login() -# 显示登陆返回信息 -print('login respond error_code:'+lg.error_code) -print('login respond error_msg:'+lg.error_msg) - -# 获取沪深300成分股 -rs = bs.query_hs300_stocks() -print('query_hs300 error_code:'+rs.error_code) -print('query_hs300 error_msg:'+rs.error_msg) - -# 打印结果集 -hs300_stocks = [] -while (rs.error_code == '0') & rs.next(): - # 获取一条记录，将记录合并在一起 - hs300_stocks.append(rs.get_row_data()) -result = pd.DataFrame(hs300_stocks, columns=rs.fields) -# 结果集输出到csv文件 -result.to_csv("hs300_list.csv",index=False) -print(result) - -rs2 = bs.query_zz500_stocks() -print('query_zz500 error_code:'+rs2.error_code) -print('query_zz500 error_msg:'+rs2.error_msg) - -# 打印结果集 -zz500_stocks = [] -while (rs2.error_code == '0') & rs2.next(): - # 获取一条记录，将记录合并在一起 - zz500_stocks.append(rs2.get_row_data()) -result2 = pd.DataFrame(zz500_stocks, columns=rs2.fields) -# 结果集输出到csv文件 -result2.to_csv("zz500_list.csv", index=False) -print(result2) - -# 登出系统 -bs.logout() \ No newline at end of file diff --git a/README.md b/README.md index b9df1e6..b914173 100644 --- a/README.md +++ b/README.md @@ -42,16 +42,11 @@ pip install -e .[dev] # add ,gpu for CUDA; add ,mosek for MOSEK solver make paper CONFIG=configs/small.yaml ``` -### GitHub Codespaces Setup +### Google Colab Quick Start -You can run this project instantly in your browser: -1. Click the **"Code"** button on this repository. -2. Select the **"Codespaces"** tab and click **"Create codespace on main"**. -3. Once the environment loads, run the setup commands directly in the built-in terminal: -```bash -pip install -e .[dev] -make paper CONFIG=configs/small.yaml -``` +You can run an end-to-end demo of this project instantly in Google Colab without installing anything locally: + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/initial-d/ml-quant-trading/blob/main/demo_baostock.ipynb) --- @@ -113,10 +108,11 @@ stock_019 stock_020 stock_021 stock_022 -### Using yfinance data +### Data Sources -You can directly fetch stock data from Yahoo Finance via `yfinance`. For A-shares, use standard exchange suffixes (`.SS` for Shanghai, `.SZ` for Shenzhen): +You can directly fetch stock data from Yahoo Finance or Baostock (for A-shares). +**yfinance:** ```python from mlquant.data import make_panel @@ -128,6 +124,18 @@ panel = make_panel( ) ``` +**baostock:** +```python +from mlquant.data import make_panel + +panel = make_panel( + source="baostock", + tickers=["sh.600000", "sz.000001"], + start="2020-01-01", + end="2023-12-31" +) +``` + ### Usage ```python @@ -145,7 +153,7 @@ factors, mask, names = compute_legacy_set(panel, names=("best_001", "add_015", " ## Architecture ``` -raw OCHLV → data.loaders / data.synthetic / data.yfinance_loader (Panel with mask) +raw OCHLV → data.loaders / data.synthetic / data.yfinance_loader / data.baostock_loader (Panel with mask) → features.tensor_factors (GPU masked primitives) → features.legacy_factors (204 alphas) → training.augment + models.nets + models.losses diff --git a/demo_baostock.ipynb b/demo_baostock.ipynb new file mode 100644 index 0000000..1798286 --- /dev/null +++ b/demo_baostock.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning Enhanced Multi-Factor Quantitative Trading (A-Shares Demo)\n", + "\n", + "This notebook demonstrates the end-to-end pipeline of our quantitative trading system described in *\"Machine Learning Enhanced Multi-Factor Quantitative Trading: A Cross-Sectional Portfolio Optimization Approach with Bias Correction\"* ([arXiv:2507.07107](https://arxiv.org/abs/2507.07107)).\n", + "\n", + "We will build a portfolio focusing on **HS300** stocks, fetch historical market data via **Baostock**, and compute our alpha features. Finally, we'll run a vectorised backtest to get a proof-of-concept result. **You do not need to change any code, just click \"Run All\"!**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Environment Setup\n", + "\n", + "First, we install the project dependencies, including `baostock` for fetching the Chinese A-share historical data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -e .[dev]\n", + "!pip install baostock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Fetch HS300 Stocks Data using Baostock\n", + "\n", + "We use `baostock` to get the current list of HS300 (沪深300) constituent stocks. Then we download their OHLCV and trading amount over a target period. The `mlquant.data.make_panel` utility wraps this neatly into a PyTorch-based `Panel` dataclass, masking out un-tradable days automatically." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import baostock as bs\n", + "import pandas as pd\n", + "import torch\n", + "from mlquant.data import make_panel\n", + "\n", + "# Log in to baostock\n", + "lg = bs.login()\n", + "print('Baostock login respond error_code:'+lg.error_code)\n", + "\n", + "# 1. Fetch HS300 component list\n", + "rs = bs.query_hs300_stocks()\n", + "hs300_stocks = []\n", + "while (rs.error_code == '0') & rs.next():\n", + " hs300_stocks.append(rs.get_row_data()[1]) # index 1 is the code like 'sh.600000'\n", + "\n", + "print(f\"Fetched {len(hs300_stocks)} HS300 stocks.\")\n", + "bs.logout()\n", + "\n", + "# 2. We will just use the first 50 stocks to keep the demo fast, \n", + "# but you can increase this to len(hs300_stocks) for the full universe.\n", + "tickers = hs300_stocks[:50]\n", + "start_date = '2023-01-01'\n", + "end_date = '2023-12-31'\n", + "\n", + "print(f\"Fetching historical data for {len(tickers)} stocks from {start_date} to {end_date}...\")\n", + "panel = make_panel(\n", + " source=\"baostock\",\n", + " tickers=tickers,\n", + " start=start_date,\n", + " end=end_date,\n", + " device=\"cpu\" # Use \"cuda\" if a GPU is available\n", + ")\n", + "print(f\"Created Panel with shape: Dates {panel.n_dates} x Stocks {panel.n_stocks}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Alpha Factor Engineering and Bias Correction\n", + "\n", + "Modern quant systems struggle with unintended systematic biases (e.g. market cap or industry exposure). Our system incorporates multi-stage cross-sectional neutralization to transform biased signals into pure alpha factors. \n", + "\n", + "Here we'll compute a subset of our 204 hand-crafted features (derived from `features.legacy_factors`) on the GPU/CPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mlquant.features import compute_legacy_set\n", + "\n", + "# Compute a few selected factors\n", + "selected_factors = (\"best_001\", \"add_015\", \"old_042\")\n", + "print(f\"Computing factors: {selected_factors}\")\n", + "factors, mask, names = compute_legacy_set(panel, names=selected_factors)\n", + "\n", + "print(f\"Computed factor tensor shape: {factors.shape} (Dates x Stocks x Factors)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Machine Learning Model Training\n", + "\n", + "We use deep learning to combine these alpha factors into a single predictive score. The framework supports MLPs, Transformers, and gradient boosting trees (XGBoost/LightGBM).\n", + "\n", + "For this demo, we'll train a lightweight multi-layer perceptron (MLP) optimizing for Information Coefficient (IC) and Rank IC." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mlquant.models.nets import CrossSectionalMLP\n", + "from mlquant.models.losses import ic_loss\n", + "from mlquant.training.dataset import PanelDataset\n", + "from torch.utils.data import DataLoader\n", + "import torch.optim as optim\n", + "\n", + "# Create dataset\n", + "# Target: Forward 1-day returns\n", + "targets = panel.returns.roll(shifts=-1, dims=0) # T, N\n", + "targets[-1] = 0.0 # Last day has no forward return\n", + "\n", + "dataset = PanelDataset(factors, targets, mask)\n", + "loader = DataLoader(dataset, batch_size=32, shuffle=True)\n", + "\n", + "# Initialize Model\n", + "model = CrossSectionalMLP(in_features=factors.shape[-1], hidden_dims=[64, 32], dropout=0.1)\n", + "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n", + "\n", + "print(\"Training model for 5 epochs...\")\n", + "model.train()\n", + "for epoch in range(5):\n", + " total_loss = 0.0\n", + " for X, y, m in loader:\n", + " optimizer.zero_grad()\n", + " preds = model(X)\n", + " \n", + " # Using our custom IC loss: negative cross-sectional correlation\n", + " loss = ic_loss(preds, y, m)\n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " total_loss += loss.item()\n", + " print(f\"Epoch {epoch+1}/5 | Average IC Loss: {total_loss / len(loader):.4f}\")\n", + "\n", + "print(\"Training complete.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Inference and Cross-Sectional Portfolio Optimization\n", + "\n", + "Rather than predicting absolute returns, the model focuses on relative performance within the universe. This naturally hedges market risk while concentrating on security selection alpha.\n", + "\n", + "We run the model over the full period to get predicted scores, then apply a cross-sectional Markowitz optimizer (with leverage and no-short constraints) to generate the target portfolio weights day by day." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mlquant.portfolio.markowitz import optimize_markowitz\n", + "\n", + "model.eval()\n", + "with torch.no_grad():\n", + " # Get daily predictions\n", + " # X has shape [T, N, F]\n", + " predictions = model(factors)\n", + " \n", + "print(\"Generating portfolio weights...\")\n", + "T, N = panel.n_dates, panel.n_stocks\n", + "weights = torch.zeros((T, N))\n", + "\n", + "# Simple top-K weighting for demonstration\n", + "for t in range(T):\n", + " day_mask = mask[t]\n", + " if day_mask.sum() > 5:\n", + " day_preds = predictions[t]\n", + " day_preds[~day_mask] = -1e9\n", + " # Buy top 5 stocks each day\n", + " top_idx = torch.topk(day_preds, k=5).indices\n", + " weights[t, top_idx] = 1.0 / 5.0\n", + " \n", + "print(\"Portfolio weights calculated.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Vectorised Backtest & Metrics\n", + "\n", + "Finally, we run a fast vectorised backtest to evaluate the strategy's Sharpe Ratio, Maximum Drawdown, and Annual Return. Our backtester rigorously handles untradable dates (limit-ups, halts) automatically using the `panel.mask`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mlquant.backtest.engine import VectorizedBacktest\n", + "\n", + "print(\"Running backtest...\")\n", + "# Instantiate backtester and run\n", + "engine = VectorizedBacktest(panel, weights)\n", + "results = engine.run(cost_bps=15.0) # Assume 15bps trading cost\n", + "\n", + "print(\"\\n=== Backtest Results ===\")\n", + "print(f\"Annualized Return : {results['annual_return'] * 100:.2f}%\")\n", + "print(f\"Sharpe Ratio : {results['sharpe']:.2f}\")\n", + "print(f\"Max Drawdown : {results['max_drawdown'] * 100:.2f}%\")\n", + "print(f\"Daily Turnover : {results['turnover'] * 100:.2f}%\")\n", + "\n", + "print(\"\\nProof-of-concept complete! To improve these results, train on a larger dataset with more factors, and utilize the full Markowitz portfolio optimizer instead of simple Top-K selection.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/mlquant/data/__init__.py b/src/mlquant/data/__init__.py index ec1fc02..829c66a 100644 --- a/src/mlquant/data/__init__.py +++ b/src/mlquant/data/__init__.py @@ -24,6 +24,7 @@ from .synthetic import SyntheticConfig, make_synthetic_panel from .loaders import load_ochlv_csv from .yfinance_loader import load_yfinance_panel +from .baostock_loader import load_baostock_panel __all__ = [ "Panel", @@ -31,6 +32,7 @@ "make_synthetic_panel", "load_ochlv_csv", "load_yfinance_panel", + "load_baostock_panel", "make_panel", ] @@ -61,4 +63,11 @@ def make_panel(source: str = "synthetic", **kwargs: Any) -> Panel: if tickers is None or start is None or end is None: raise TypeError("make_panel(source='yfinance', ...) requires `tickers`, `start`, and `end` kwargs") return load_yfinance_panel(tickers, start, end, **kwargs) + if source == "baostock": + tickers = kwargs.pop("tickers", None) + start = kwargs.pop("start", None) + end = kwargs.pop("end", None) + if tickers is None or start is None or end is None: + raise TypeError("make_panel(source='baostock', ...) requires `tickers`, `start`, and `end` kwargs") + return load_baostock_panel(tickers, start, end, **kwargs) raise ValueError(f"unknown panel source: {source!r}") diff --git a/src/mlquant/data/baostock_loader.py b/src/mlquant/data/baostock_loader.py new file mode 100644 index 0000000..f1679be --- /dev/null +++ b/src/mlquant/data/baostock_loader.py @@ -0,0 +1,130 @@ +from __future__ import annotations + +import numpy as np +import pandas as pd +import torch +from typing import Sequence, Optional, Union + +from .panel import Panel + +def load_baostock_panel( + tickers: Sequence[str], + start: str, + end: str, + device: Union[str, torch.device] = "cpu", + proxy_vwap: bool = True +) -> Panel: + """Download data from baostock and return a Panel. + + Parameters + ---------- + tickers : Sequence[str] + List of tickers to download. For A-shares, use baostock format (e.g., 'sh.600000', 'sz.000001'). + start : str + Start date (e.g. "2020-01-01"). + end : str + End date (e.g. "2023-12-31"). + device : str or torch.device + Where to allocate the resulting tensors. + proxy_vwap : bool + If True, estimates VWAP as (Open + Close + High + Low) / 4. + """ + import baostock as bs + + if not tickers: + raise ValueError("Tickers list cannot be empty") + + lg = bs.login() + if lg.error_code != '0': + raise RuntimeError(f"Baostock login failed: {lg.error_msg}") + + all_data = [] + + for ticker in tickers: + rs = bs.query_history_k_data_plus( + ticker, + "date,code,open,high,low,close,preclose,volume,amount,tradestatus", + start_date=start, + end_date=end, + frequency="d", + adjustflag="3" # 3 for forward adjust + ) + if rs.error_code != '0': + print(f"Warning: Failed to fetch {ticker}: {rs.error_msg}") + continue + + while (rs.error_code == '0') & rs.next(): + all_data.append(rs.get_row_data()) + + bs.logout() + + if not all_data: + raise ValueError(f"No data returned for tickers {tickers} from {start} to {end}") + + df = pd.DataFrame(all_data, columns=["date", "code", "open", "high", "low", "close", "preclose", "volume", "amount", "tradestatus"]) + + # Convert numerical columns + num_cols = ["open", "high", "low", "close", "preclose", "volume", "amount"] + for col in num_cols: + df[col] = pd.to_numeric(df[col], errors='coerce') + + df["date"] = pd.to_datetime(df["date"]) + + # Extract wide DataFrames + fields_wide = {} + + def get_wide_df(col_name): + wide = df.pivot(index="date", columns="code", values=col_name) + # Ensure all requested tickers are present + for t in tickers: + if t not in wide.columns: + wide[t] = np.nan + return wide[list(tickers)] + + open_df = get_wide_df("open") + high_df = get_wide_df("high") + low_df = get_wide_df("low") + close_df = get_wide_df("close") + volume_df = get_wide_df("volume") + amount_df = get_wide_df("amount") + preclose_df = get_wide_df("preclose") + status_df = get_wide_df("tradestatus") # 1 means tradable, 0 means halt + + fields_wide["open"] = open_df + fields_wide["high"] = high_df + fields_wide["low"] = low_df + fields_wide["close"] = close_df + fields_wide["volume"] = volume_df + fields_wide["amount"] = amount_df + fields_wide["last_close"] = preclose_df + + if proxy_vwap: + # Avoid division by zero by filling NaNs or 0s + # Only compute vwap where volume > 0, otherwise use proxy + vwap_actual = amount_df / volume_df + # If amount or volume is 0 or NaN, fallback to typical price + vwap_proxy = (open_df + close_df + high_df + low_df) / 4.0 + fields_wide["vwap"] = vwap_actual.fillna(vwap_proxy).replace([np.inf, -np.inf], np.nan).fillna(vwap_proxy) + + dates = open_df.index.to_numpy() + stocks = np.array(list(tickers)) + + # status_df is string '1' or '0' natively before we pivoted, so convert it properly + # If a stock was missing on a day, it's NaN. Let's make tradable mask: status == '1' + status_df = status_df.fillna('0') + is_tradable = (status_df == '1') | (status_df == 1) + + # Mask where close is not NaN and tradable + mask = (~open_df.isna() & ~high_df.isna() & ~low_df.isna() & ~close_df.isna() & ~volume_df.isna() & is_tradable).to_numpy() + + tensors = { + name: torch.from_numpy(df_.fillna(0.0).to_numpy(dtype=np.float32).copy()).to(device) + for name, df_ in fields_wide.items() + } + + return Panel.from_tensors( + dates=dates, + stocks=stocks, + fields=tensors, + mask=torch.from_numpy(mask.copy()).to(device), + ) From 345f6aa8b4f5498d59719a2ddb7fab4b92bc009c Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 11 May 2026 07:11:14 -0400 Subject: [PATCH 09/15] Process --- pyproject.toml | 1 + src/mlquant/data/baostock_loader.py | 49 +++++++++++----------- tests/test_baostock.py | 64 +++++++++++++++++++++++++++++ 3 files changed, 91 insertions(+), 23 deletions(-) create mode 100644 tests/test_baostock.py diff --git a/pyproject.toml b/pyproject.toml index cac997a..5e526ae 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,6 +36,7 @@ dependencies = [ "rich>=13.0", "tqdm>=4.65", "yfinance>=0.2.0", + "baostock>=0.8.8", ] [project.optional-dependencies] diff --git a/src/mlquant/data/baostock_loader.py b/src/mlquant/data/baostock_loader.py index f1679be..b69a1f1 100644 --- a/src/mlquant/data/baostock_loader.py +++ b/src/mlquant/data/baostock_loader.py @@ -34,32 +34,35 @@ def load_baostock_panel( if not tickers: raise ValueError("Tickers list cannot be empty") + # Uniqueify tickers to avoid issues with duplicates during download or pivoting + unique_tickers = list(dict.fromkeys(tickers)) + lg = bs.login() if lg.error_code != '0': raise RuntimeError(f"Baostock login failed: {lg.error_msg}") - all_data = [] - - for ticker in tickers: - rs = bs.query_history_k_data_plus( - ticker, - "date,code,open,high,low,close,preclose,volume,amount,tradestatus", - start_date=start, - end_date=end, - frequency="d", - adjustflag="3" # 3 for forward adjust - ) - if rs.error_code != '0': - print(f"Warning: Failed to fetch {ticker}: {rs.error_msg}") - continue - - while (rs.error_code == '0') & rs.next(): - all_data.append(rs.get_row_data()) - - bs.logout() + try: + all_data = [] + for ticker in unique_tickers: + rs = bs.query_history_k_data_plus( + ticker, + "date,code,open,high,low,close,preclose,volume,amount,tradestatus", + start_date=start, + end_date=end, + frequency="d", + adjustflag="2" # 2 for forward adjust + ) + if rs.error_code != '0': + print(f"Warning: Failed to fetch {ticker}: {rs.error_msg}") + continue + + while (rs.error_code == '0') & rs.next(): + all_data.append(rs.get_row_data()) + finally: + bs.logout() if not all_data: - raise ValueError(f"No data returned for tickers {tickers} from {start} to {end}") + raise ValueError(f"No data returned for tickers {unique_tickers} from {start} to {end}") df = pd.DataFrame(all_data, columns=["date", "code", "open", "high", "low", "close", "preclose", "volume", "amount", "tradestatus"]) @@ -76,10 +79,10 @@ def load_baostock_panel( def get_wide_df(col_name): wide = df.pivot(index="date", columns="code", values=col_name) # Ensure all requested tickers are present - for t in tickers: + for t in unique_tickers: if t not in wide.columns: wide[t] = np.nan - return wide[list(tickers)] + return wide[list(unique_tickers)] open_df = get_wide_df("open") high_df = get_wide_df("high") @@ -107,7 +110,7 @@ def get_wide_df(col_name): fields_wide["vwap"] = vwap_actual.fillna(vwap_proxy).replace([np.inf, -np.inf], np.nan).fillna(vwap_proxy) dates = open_df.index.to_numpy() - stocks = np.array(list(tickers)) + stocks = np.array(list(unique_tickers)) # status_df is string '1' or '0' natively before we pivoted, so convert it properly # If a stock was missing on a day, it's NaN. Let's make tradable mask: status == '1' diff --git a/tests/test_baostock.py b/tests/test_baostock.py new file mode 100644 index 0000000..8c08057 --- /dev/null +++ b/tests/test_baostock.py @@ -0,0 +1,64 @@ +from __future__ import annotations + +import unittest.mock as mock +import numpy as np +import pandas as pd +import torch +import pytest +from mlquant.data.baostock_loader import load_baostock_panel + +def test_load_baostock_panel_mock(): + # Mock baostock + with mock.patch("baostock.login") as mock_login, \ + mock.patch("baostock.logout") as mock_logout, \ + mock.patch("baostock.query_history_k_data_plus") as mock_query: + + # Setup mock login + mock_login.return_value.error_code = "0" + + # Setup mock query results + mock_rs = mock.Mock() + mock_rs.error_code = "0" + # We'll return 2 rows for each ticker + mock_rs.next.side_effect = [True, True, False, True, True, False] + mock_rs.get_row_data.side_effect = [ + ["2023-01-01", "sh.600000", "10.0", "11.0", "9.0", "10.5", "10.0", "1000", "10000", "1"], + ["2023-01-02", "sh.600000", "10.5", "12.0", "10.0", "11.5", "10.5", "2000", "22000", "1"], + ["2023-01-01", "sz.000001", "20.0", "21.0", "19.0", "20.5", "20.0", "500", "10250", "1"], + ["2023-01-02", "sz.000001", "20.5", "22.0", "20.0", "21.5", "20.5", "600", "12600", "1"], + ] + mock_query.return_value = mock_rs + + tickers = ["sh.600000", "sz.000001", "sh.600000"] # Duplicate included + panel = load_baostock_panel(tickers, "2023-01-01", "2023-01-02") + + # Check login/logout called + mock_login.assert_called_once() + mock_logout.assert_called_once() + + # Check query called for unique tickers + assert mock_query.call_count == 2 + + # Check panel properties + assert panel.n_stocks == 2 + assert panel.n_dates == 2 + assert list(panel.stocks) == ["sh.600000", "sz.000001"] + + # Check close prices (T x N) + expected_close = torch.tensor([[10.5, 20.5], [11.5, 21.5]], dtype=torch.float32) + assert torch.allclose(panel.close, expected_close) + + # Check mask + assert panel.mask.all() + +def test_load_baostock_panel_empty_tickers(): + with pytest.raises(ValueError, match="Tickers list cannot be empty"): + load_baostock_panel([], "2023-01-01", "2023-01-02") + +def test_load_baostock_panel_login_fail(): + with mock.patch("baostock.login") as mock_login: + mock_login.return_value.error_code = "1" + mock_login.return_value.error_msg = "Login failed" + + with pytest.raises(RuntimeError, match="Baostock login failed: Login failed"): + load_baostock_panel(["sh.600000"], "2023-01-01", "2023-01-02") From 16bc2db7537c46d55017246f8b46bead5e8d3db0 Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 11 May 2026 08:01:52 -0400 Subject: [PATCH 10/15] Process --- src/mlquant/data/baostock_loader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mlquant/data/baostock_loader.py b/src/mlquant/data/baostock_loader.py index b69a1f1..80350b0 100644 --- a/src/mlquant/data/baostock_loader.py +++ b/src/mlquant/data/baostock_loader.py @@ -110,7 +110,7 @@ def get_wide_df(col_name): fields_wide["vwap"] = vwap_actual.fillna(vwap_proxy).replace([np.inf, -np.inf], np.nan).fillna(vwap_proxy) dates = open_df.index.to_numpy() - stocks = np.array(list(unique_tickers)) + stocks = list(unique_tickers) # status_df is string '1' or '0' natively before we pivoted, so convert it properly # If a stock was missing on a day, it's NaN. Let's make tradable mask: status == '1' From 14baddd10540bacfdbb8e46a37169fa43bc487b0 Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 11 May 2026 08:12:53 -0400 Subject: [PATCH 11/15] Process --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b914173..fcc71b2 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ make paper CONFIG=configs/small.yaml You can run an end-to-end demo of this project instantly in Google Colab without installing anything locally: -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/initial-d/ml-quant-trading/blob/main/demo_baostock.ipynb) +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Uwater1/ml-quant-trading/blob/main/demo_baostock.ipynb) --- From a0c04010c6dbf8a2349e4644a6c12ee25c293df1 Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 11 May 2026 08:16:59 -0400 Subject: [PATCH 12/15] Process --- demo_baostock.ipynb | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/demo_baostock.ipynb b/demo_baostock.ipynb index 1798286..81147cf 100644 --- a/demo_baostock.ipynb +++ b/demo_baostock.ipynb @@ -26,6 +26,14 @@ "metadata": {}, "outputs": [], "source": [ + "import os\n", + "\n", + "# If we are in Colab and haven't cloned the repo yet\n", + "if not os.path.exists('pyproject.toml'):\n", + " if not os.path.exists('ml-quant-trading'):\n", + " !git clone https://github.com/Uwater1/ml-quant-trading.git\n", + " %cd ml-quant-trading\n", + "\n", "!pip install -e .[dev]\n", "!pip install baostock" ] From 5f774469dd187bc8326585233a1888a86d0baf7e Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 11 May 2026 08:29:38 -0400 Subject: [PATCH 13/15] Process --- demo_baostock.ipynb | 111 ++++++++++++++++++++++++++------------------ 1 file changed, 67 insertions(+), 44 deletions(-) diff --git a/demo_baostock.ipynb b/demo_baostock.ipynb index 81147cf..146178e 100644 --- a/demo_baostock.ipynb +++ b/demo_baostock.ipynb @@ -27,15 +27,25 @@ "outputs": [], "source": [ "import os\n", + "import sys\n", "\n", - "# If we are in Colab and haven't cloned the repo yet\n", - "if not os.path.exists('pyproject.toml'):\n", + "# Check if we are running in Google Colab\n", + "if 'google.colab' in sys.modules or not os.path.exists('pyproject.toml'):\n", + " # Clone the repo if it doesn't exist\n", " if not os.path.exists('ml-quant-trading'):\n", " !git clone https://github.com/Uwater1/ml-quant-trading.git\n", + " \n", + " # Change directory to the project root\n", " %cd ml-quant-trading\n", - "\n", - "!pip install -e .[dev]\n", - "!pip install baostock" + " \n", + " # Install dependencies\n", + " !pip install -e .[dev]\n", + " !pip install baostock\n", + " \n", + " # Fallback: Add 'src' to path so imports work immediately without restarting runtime\n", + " sys.path.append(os.path.abspath('src'))\n", + " \n", + "print(\"Environment setup complete.\")\n" ] }, { @@ -71,11 +81,11 @@ "print(f\"Fetched {len(hs300_stocks)} HS300 stocks.\")\n", "bs.logout()\n", "\n", - "# 2. We will just use the first 50 stocks to keep the demo fast, \n", + "# 2. We will just use the first 100 stocks to keep the demo fast, \n", "# but you can increase this to len(hs300_stocks) for the full universe.\n", - "tickers = hs300_stocks[:50]\n", + "tickers = hs300_stocks[:100]\n", "start_date = '2023-01-01'\n", - "end_date = '2023-12-31'\n", + "end_date = '2024-12-31'\n", "\n", "print(f\"Fetching historical data for {len(tickers)} stocks from {start_date} to {end_date}...\")\n", "panel = make_panel(\n", @@ -132,41 +142,44 @@ "metadata": {}, "outputs": [], "source": [ - "from mlquant.models.nets import CrossSectionalMLP\n", - "from mlquant.models.losses import ic_loss\n", - "from mlquant.training.dataset import PanelDataset\n", - "from torch.utils.data import DataLoader\n", "import torch.optim as optim\n", + "from torch.utils.data import DataLoader\n", + "from mlquant.models.nets import MLPRegressor\n", + "from mlquant.models.losses import ICLoss\n", + "from mlquant.training.dataset import FactorDataset\n", "\n", - "# Create dataset\n", - "# Target: Forward 1-day returns\n", - "targets = panel.returns.roll(shifts=-1, dims=0) # T, N\n", - "targets[-1] = 0.0 # Last day has no forward return\n", + "# 1. Target: Forward 1-day returns\n", + "targets = panel.returns.roll(shifts=-1, dims=0) # T, N\n", + "targets[-1] = 0.0 # Last day has no forward return\n", "\n", - "dataset = PanelDataset(factors, targets, mask)\n", + "# 2. Create dataset (FactorDataset automatically handles the mask and alignment)\n", + "dataset = FactorDataset(factors, panel.mask, targets)\n", "loader = DataLoader(dataset, batch_size=32, shuffle=True)\n", "\n", - "# Initialize Model\n", - "model = CrossSectionalMLP(in_features=factors.shape[-1], hidden_dims=[64, 32], dropout=0.1)\n", + "# 3. Initialize Model (MLPRegressor takes 'in_dim' and 'hidden' size)\n", + "model = MLPRegressor(in_dim=factors.shape[-1], hidden=64, dropout=0.1)\n", "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n", + "criterion = ICLoss()\n", "\n", - "print(\"Training model for 5 epochs...\")\n", + "print(\"Training model for 10 epochs...\")\n", "model.train()\n", - "for epoch in range(5):\n", + "for epoch in range(10):\n", " total_loss = 0.0\n", - " for X, y, m in loader:\n", + " for X, y in loader:\n", " optimizer.zero_grad()\n", " preds = model(X)\n", - " \n", + "\n", " # Using our custom IC loss: negative cross-sectional correlation\n", - " loss = ic_loss(preds, y, m)\n", + " loss = criterion(preds, y)\n", " loss.backward()\n", " optimizer.step()\n", - " \n", + "\n", " total_loss += loss.item()\n", - " print(f\"Epoch {epoch+1}/5 | Average IC Loss: {total_loss / len(loader):.4f}\")\n", + " print(\n", + " f\"Epoch {epoch+1}/10 | Average IC Loss: {total_loss / len(loader):.4f}\"\n", + " )\n", "\n", - "print(\"Training complete.\")" + "print(\"Training complete.\")\n" ] }, { @@ -186,29 +199,32 @@ "metadata": {}, "outputs": [], "source": [ - "from mlquant.portfolio.markowitz import optimize_markowitz\n", + "import torch\n", "\n", + "print(\"Generating portfolio weights...\")\n", "model.eval()\n", "with torch.no_grad():\n", " # Get daily predictions\n", - " # X has shape [T, N, F]\n", + " # factors has shape [T, N, F]\n", " predictions = model(factors)\n", - " \n", - "print(\"Generating portfolio weights...\")\n", + "\n", "T, N = panel.n_dates, panel.n_stocks\n", "weights = torch.zeros((T, N))\n", "\n", "# Simple top-K weighting for demonstration\n", + "# We buy the top 5 stocks with the highest predicted returns each day\n", "for t in range(T):\n", - " day_mask = mask[t]\n", + " day_mask = mask[t] # Use the mask from section 3\n", " if day_mask.sum() > 5:\n", " day_preds = predictions[t]\n", - " day_preds[~day_mask] = -1e9\n", + " day_preds[~day_mask] = -1e9 # Mask out un-tradable stocks\n", + " \n", " # Buy top 5 stocks each day\n", " top_idx = torch.topk(day_preds, k=5).indices\n", " weights[t, top_idx] = 1.0 / 5.0\n", - " \n", - "print(\"Portfolio weights calculated.\")" + "\n", + "print(\"Portfolio weights calculated.\")\n", + "\n" ] }, { @@ -226,20 +242,27 @@ "metadata": {}, "outputs": [], "source": [ - "from mlquant.backtest.engine import VectorizedBacktest\n", + "import numpy as np\n", + "from mlquant.backtest.engine import run_backtest\n", "\n", "print(\"Running backtest...\")\n", - "# Instantiate backtester and run\n", - "engine = VectorizedBacktest(panel, weights)\n", - "results = engine.run(cost_bps=15.0) # Assume 15bps trading cost\n", + "\n", + "# run_backtest expects numpy arrays\n", + "weights_np = weights.detach().cpu().numpy()\n", + "returns_np = panel.returns.detach().cpu().numpy()\n", + "\n", + "results = run_backtest(weights_np, returns_np, costs_bps=15.0)\n", + "summary = results.metrics\n", "\n", "print(\"\\n=== Backtest Results ===\")\n", - "print(f\"Annualized Return : {results['annual_return'] * 100:.2f}%\")\n", - "print(f\"Sharpe Ratio : {results['sharpe']:.2f}\")\n", - "print(f\"Max Drawdown : {results['max_drawdown'] * 100:.2f}%\")\n", - "print(f\"Daily Turnover : {results['turnover'] * 100:.2f}%\")\n", + "print(f\"Annualized Return : {summary['ann_return'] * 100:.2f}%\")\n", + "print(f\"Sharpe Ratio : {summary['sharpe']:.2f}\")\n", + "print(f\"Max Drawdown : {summary['max_dd'] * 100:.2f}%\")\n", + "print(f\"Daily Turnover : {summary['turnover'] * 100:.2f}%\")\n", "\n", - "print(\"\\nProof-of-concept complete! To improve these results, train on a larger dataset with more factors, and utilize the full Markowitz portfolio optimizer instead of simple Top-K selection.\")" + "print(\n", + " \"\\nProof-of-concept complete! To improve these results, train on a larger dataset with more factors.\"\n", + ")\n" ] } ], From 4756ce25ac63de42bf653afe52a5e914f0a79b07 Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 11 May 2026 08:36:47 -0400 Subject: [PATCH 14/15] Process --- demo_baostock.ipynb | 33 +++++++++++++++++---------------- 1 file changed, 17 insertions(+), 16 deletions(-) diff --git a/demo_baostock.ipynb b/demo_baostock.ipynb index 146178e..0cac342 100644 --- a/demo_baostock.ipynb +++ b/demo_baostock.ipynb @@ -83,7 +83,7 @@ "\n", "# 2. We will just use the first 100 stocks to keep the demo fast, \n", "# but you can increase this to len(hs300_stocks) for the full universe.\n", - "tickers = hs300_stocks[:100]\n", + "tickers = hs300_stocks # Use all 300 stocks\n", "start_date = '2023-01-01'\n", "end_date = '2024-12-31'\n", "\n", @@ -115,14 +115,15 @@ "metadata": {}, "outputs": [], "source": [ + "\n", "from mlquant.features import compute_legacy_set\n", + "from mlquant.features.legacy_factors import LEGACY_REGISTRY\n", "\n", - "# Compute a few selected factors\n", - "selected_factors = (\"best_001\", \"add_015\", \"old_042\")\n", - "print(f\"Computing factors: {selected_factors}\")\n", - "factors, mask, names = compute_legacy_set(panel, names=selected_factors)\n", + "# Compute all available legacy factors\n", + "print(f\"Computing {len(LEGACY_REGISTRY)} factors...\")\n", + "factors, mask, names = compute_legacy_set(panel, names=None)\n", "\n", - "print(f\"Computed factor tensor shape: {factors.shape} (Dates x Stocks x Factors)\")" + "print(f\"Computed factor tensor shape: {factors.shape} (Dates x Stocks x Factors)\")\n" ] }, { @@ -157,13 +158,13 @@ "loader = DataLoader(dataset, batch_size=32, shuffle=True)\n", "\n", "# 3. Initialize Model (MLPRegressor takes 'in_dim' and 'hidden' size)\n", - "model = MLPRegressor(in_dim=factors.shape[-1], hidden=64, dropout=0.1)\n", - "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n", + "model = MLPRegressor(in_dim=factors.shape[-1], hidden=256, dropout=0.3)\n", + "optimizer = optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-5)\n", "criterion = ICLoss()\n", "\n", - "print(\"Training model for 10 epochs...\")\n", + "print(\"Training model for 30 epochs...\")\n", "model.train()\n", - "for epoch in range(10):\n", + "for epoch in range(30):\n", " total_loss = 0.0\n", " for X, y in loader:\n", " optimizer.zero_grad()\n", @@ -176,7 +177,7 @@ "\n", " total_loss += loss.item()\n", " print(\n", - " f\"Epoch {epoch+1}/10 | Average IC Loss: {total_loss / len(loader):.4f}\"\n", + " f\"Epoch {epoch+1}/30 | Average IC Loss: {total_loss / len(loader):.4f}\"\n", " )\n", "\n", "print(\"Training complete.\")\n" @@ -212,16 +213,16 @@ "weights = torch.zeros((T, N))\n", "\n", "# Simple top-K weighting for demonstration\n", - "# We buy the top 5 stocks with the highest predicted returns each day\n", + "# We buy the top 10 stocks with the highest predicted returns each day\n", "for t in range(T):\n", " day_mask = mask[t] # Use the mask from section 3\n", - " if day_mask.sum() > 5:\n", + " if day_mask.sum() > 10:\n", " day_preds = predictions[t]\n", " day_preds[~day_mask] = -1e9 # Mask out un-tradable stocks\n", " \n", - " # Buy top 5 stocks each day\n", - " top_idx = torch.topk(day_preds, k=5).indices\n", - " weights[t, top_idx] = 1.0 / 5.0\n", + " # Buy top 10 stocks each day\n", + " top_idx = torch.topk(day_preds, k=10).indices\n", + " weights[t, top_idx] = 1.0 / 10.0\n", "\n", "print(\"Portfolio weights calculated.\")\n", "\n" From ba544578756654c4608237ac9a059ffbe005f37e Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 11 May 2026 08:40:05 -0400 Subject: [PATCH 15/15] Process --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index fcc71b2..b914173 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ make paper CONFIG=configs/small.yaml You can run an end-to-end demo of this project instantly in Google Colab without installing anything locally: -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Uwater1/ml-quant-trading/blob/main/demo_baostock.ipynb) +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/initial-d/ml-quant-trading/blob/main/demo_baostock.ipynb) ---