initial commit

main.py

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+# ============================================
+# 1. Imports
+# ============================================
+import numpy as np
+import pandas as pd
+import yfinance as yf
+
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.preprocessing import StandardScaler
+
+import matplotlib.pyplot as plt
+
+# ============================================
+# 2. Parameters
+# ============================================
+TICKERS = ["AAPL", "MSFT", "GOOGL", "AMZN", "META"]
+START_DATE = "2015-01-01"
+END_DATE = "2024-01-01"
+TRAIN_END = "2020-12-31"
+
+TRANSACTION_COST = 0.001  # 0.1%
+
+# ============================================
+# 3. Download Data
+# ============================================
+def download_data(tickers):
+    data = yf.download(tickers, start=START_DATE, end=END_DATE, group_by="ticker")
+    
+    dfs = []
+    for ticker in tickers:
+        df = data[ticker].copy()
+        df["ticker"] = ticker
+        dfs.append(df)
+    
+    df = pd.concat(dfs)
+    df.index.name = "date"
+    return df.reset_index()
+
+df = download_data(TICKERS)
+
+# ============================================
+# 4. Sort (IMPORTANT)
+# ============================================
+df = df.sort_values(["ticker", "date"])
+
+# ============================================
+# 5. Feature Engineering (NO APPLY)
+# ============================================
+df["return_1d"] = df.groupby("ticker")["Close"].pct_change()
+df["return_5d"] = df.groupby("ticker")["Close"].pct_change(5)
+
+df["ma_5"] = df.groupby("ticker")["Close"].transform(lambda x: x.rolling(5).mean())
+df["ma_10"] = df.groupby("ticker")["Close"].transform(lambda x: x.rolling(10).mean())
+
+df["volatility_5d"] = (
+    df.groupby("ticker")["return_1d"]
+    .transform(lambda x: x.rolling(5).std())
+)
+
+df["volume_change"] = df.groupby("ticker")["Volume"].pct_change()
+
+df["price_ma5_ratio"] = df["Close"] / df["ma_5"]
+
+# ============================================
+# 6. Labels (SAFE)
+# ============================================
+df["future_return"] = df.groupby("ticker")["Close"].pct_change().shift(-1)
+df["target"] = (df["future_return"] > 0).astype(int)
+
+# ============================================
+# 7. Clean Data
+# ============================================
+df = df.dropna().reset_index(drop=True)
+
+# ============================================
+# 8. Train/Test Split
+# ============================================
+train = df[df["date"] <= TRAIN_END]
+test = df[df["date"] > TRAIN_END]
+
+FEATURES = [
+    "return_1d",
+    "return_5d",
+    "ma_5",
+    "ma_10",
+    "volatility_5d",
+    "volume_change",
+    "price_ma5_ratio"
+]
+
+X_train = train[FEATURES]
+y_train = train["target"]
+
+X_test = test[FEATURES]
+y_test = test["target"]
+
+# ============================================
+# 9. Scaling
+# ============================================
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# ============================================
+# 10. Train Model
+# ============================================
+model = RandomForestClassifier(
+    n_estimators=100,
+    max_depth=5,
+    random_state=42
+)
+
+model.fit(X_train, y_train)
+
+# ============================================
+# 11. Predictions
+# ============================================
+preds = model.predict(X_test)
+
+accuracy = accuracy_score(y_test, preds)
+print(f"Test Accuracy: {accuracy:.4f}")
+
+# ============================================
+# 12. Backtest
+# ============================================
+test = test.copy()
+test["prediction"] = preds
+
+# 🚨 Avoid lookahead bias
+test["prediction"] = test.groupby("ticker")["prediction"].shift(1)
+
+# Strategy returns
+test["strategy_return"] = test["future_return"] * test["prediction"]
+
+# Transaction costs
+test["position_change"] = (
+    test.groupby("ticker")["prediction"].diff().abs()
+)
+test["transaction_cost"] = test["position_change"] * TRANSACTION_COST
+
+test["strategy_return"] -= test["transaction_cost"]
+
+# Drop NaNs from shifting
+test = test.dropna()
+
+# ============================================
+# 13. Performance
+# ============================================
+test["cum_market"] = (1 + test["future_return"]).cumprod()
+test["cum_strategy"] = (1 + test["strategy_return"]).cumprod()
+
+sharpe = np.sqrt(252) * test["strategy_return"].mean() / test["strategy_return"].std()
+print(f"Sharpe Ratio: {sharpe:.2f}")
+
+# ============================================
+# 14. Plot
+# ============================================
+plt.figure(figsize=(10,6))
+plt.plot(test["date"], test["cum_market"], label="Market")
+plt.plot(test["date"], test["cum_strategy"], label="Strategy")
+plt.legend()
+plt.title("Strategy vs Market")
+plt.xlabel("Date")
+plt.ylabel("Cumulative Return")
+plt.grid()
+#plt.show()
+plt.savefig("strategy.png", dpi=150)
+print("Plot saved as strategy.png")

requirements.txt

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+beautifulsoup4==4.14.3
+certifi==2026.2.25
+cffi==2.0.0
+charset-normalizer==3.4.6
+contourpy==1.3.3
+curl_cffi==0.13.0
+cycler==0.12.1
+fonttools==4.62.1
+frozendict==2.4.7
+idna==3.11
+joblib==1.5.3
+kiwisolver==1.5.0
+lxml==6.0.2
+matplotlib==3.10.8
+multitasking==0.0.12
+numpy==2.4.3
+packaging==26.0
+pandas==3.0.1
+peewee==4.0.2
+pillow==12.1.1
+platformdirs==4.9.4
+protobuf==7.34.1
+pycparser==3.0
+pyparsing==3.3.2
+python-dateutil==2.9.0.post0
+pytz==2026.1.post1
+requests==2.32.5
+scikit-learn==1.8.0
+scipy==1.17.1
+six==1.17.0
+soupsieve==2.8.3
+threadpoolctl==3.6.0
+typing_extensions==4.15.0
+urllib3==2.6.3
+websockets==16.0
+yfinance==1.2.0