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import yfinance as yf |
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import pandas as pd |
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from sklearn.model_selection import train_test_split |
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from sklearn.linear_model import LogisticRegression |
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from sklearn.metrics import accuracy_score |
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import gradio as gr |
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def fetch_stock_data(tickers, start_date, end_date): |
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data = {} |
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for ticker in tickers.split(','): |
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stock_data = yf.download(ticker.strip(), start=start_date, end=end_date) |
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data[ticker.strip()] = stock_data |
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return data |
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def preprocess_data(data, window_size=5): |
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processed_data = {} |
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feature_names = [f'Lag_{i}' for i in range(1, window_size + 1)] |
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for ticker, df in data.items(): |
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df['Return'] = df['Adj Close'].pct_change() |
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df['Future Return'] = df['Return'].shift(-1) |
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df['Target'] = df['Future Return'].apply(lambda x: 1 if x > 0 else 0) |
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for i in range(1, window_size + 1): |
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df[f'Lag_{i}'] = df['Return'].shift(i) |
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df = df.dropna() |
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processed_data[ticker] = (df[feature_names], df['Target']) |
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return processed_data, feature_names |
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def train_model(processed_data): |
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models = {} |
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for ticker, (X, y) in processed_data.items(): |
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) |
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model = LogisticRegression() |
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model.fit(X_train, y_train) |
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y_pred = model.predict(X_test) |
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accuracy = accuracy_score(y_test, y_pred) |
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models[ticker] = model |
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print(f'Model for {ticker} trained with accuracy: {accuracy:.2f}') |
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return models |
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def predict(models, data, feature_names, window_size=5): |
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predictions = {} |
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for ticker, model in models.items(): |
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df = data[ticker] |
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latest_data = df['Adj Close'].pct_change().dropna().iloc[-window_size:].values.reshape(1, -1) |
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if latest_data.shape[1] == window_size: |
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latest_data_df = pd.DataFrame(latest_data, columns=feature_names) |
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prediction = model.predict(latest_data_df) |
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predictions[ticker] = 'Buy' if prediction[0] == 1 else 'Sell' |
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else: |
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predictions[ticker] = 'Insufficient data' |
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return predictions |
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def stock_prediction_app(tickers, start_date, end_date): |
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stock_data = fetch_stock_data(tickers, start_date, end_date) |
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processed_data, feature_names = preprocess_data(stock_data) |
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models = train_model(processed_data) |
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predictions = predict(models, stock_data, feature_names) |
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return predictions |
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inputs = [ |
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gr.Textbox(lines=2, placeholder="Enter tickers separated by commas (e.g., AAPL, MSFT, GOOGL)", label="Stock Tickers"), |
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gr.Textbox(placeholder="Enter start date (YYYY-MM-DD)", label="Start Date"), |
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gr.Textbox(placeholder="Enter end date (YYYY-MM-DD)", label="End Date") |
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] |
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outputs = gr.JSON() |
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interface = gr.Interface( |
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fn=stock_prediction_app, |
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inputs=inputs, |
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outputs=outputs, |
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title="Stock Prediction App", |
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description="Enter stock tickers, start date, and end date to get buy/sell predictions." |
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) |
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interface.launch() |
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