import gradio as gr import pandas as pd import numpy as np import os from datetime import datetime, timedelta import logging from core.data import load_data, add_technical_indicators, add_sentiment, sentiment_analysis from core.model_runner import get_model from core.plot import plot_forecast, plot_metrics_r2, plot_metrics_errors, plot_metrics_precision_recall, plot_metrics_risk, plot_loss_curve, plot_model_architecture, plot_future_forecast, plot_indicators, plot_signals, plot_backtest import plotly.io as pio from core.signals import generate_signals from config import AVAILABLE_MODELS, DEFAULT_TICKERS, AVAILABLE_TIMEFRAMES, AVAILABLE_INDICATORS from newsapi import NewsApiClient from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer log_path = "/tmp/app_log.txt" os.makedirs("/tmp", exist_ok=True) logging.basicConfig( level=logging.DEBUG, handlers=[ logging.FileHandler(log_path), logging.StreamHandler() ], format='%(asctime)s - %(levelname)s - %(message)s' ) def update_horizon_label(timeframe): units = {'1m': 'minutes', '5m': 'minutes', '15m': 'minutes', '30m': 'minutes', '1h': 'hours', '4h': 'hours', '1d': 'days', '1wk': 'weeks'} return gr.update(label=f"Horizon ({units.get(timeframe, 'days')})") def run_dashboard(data_src, ticker, file_upload, timeframe, start_date, end_date, horizon, indicators, include_sentiment, news_api_key, alpha_api_key, finnhub_api_key, twelvedata_api_key, account_size, risk_percent, model, hidden_units, n_layers, epochs, learning_rate, beta1, beta2, weight_decay, dropout, window_size, test_split, rsi_mid, macd_sens, adx_thr, sent_thr, vote_buy, vote_sell, feat_selector, feat_threshold): try: logging.info(f"Running dashboard for {ticker}, timeframe: {timeframe}, model: {model}") start_date = pd.to_datetime(start_date).strftime("%Y-%m-%d") end_date = pd.to_datetime(end_date).strftime("%Y-%m-%d") df = load_data(data_src=data_src, ticker=ticker, start=start_date, end=end_date, interval=timeframe, file_upload=file_upload, alpha_api_key=alpha_api_key, finnhub_api_key=finnhub_api_key, twelvedata_api_key=twelvedata_api_key) if df.empty: logging.error("Failed to load data") return [None] * 12 + ["Failed to load data", None, None, None, None, "Failed to load data"] df, valid_indicators = add_technical_indicators(df, indicators) sentiment_text = "Sentiment analysis not included." if include_sentiment and news_api_key: df, sentiment_text = add_sentiment(df, ticker, news_api_key, start_date, end_date) features = valid_indicators # Use valid_indicators for feature target = 'value' result = get_model( df=df, features=features, target=target, model_name=model, horizon=horizon, hidden_units=hidden_units, n_layers=n_layers, epochs=epochs, learning_rate=learning_rate, beta1=beta1, beta2=beta2, weight_decay=weight_decay, dropout=dropout, window_size=window_size, test_split=test_split, selector_method=feat_selector, importance_threshold=feat_threshold ) if isinstance(result, dict) and result.get("error"): logging.error(f"Model training failed: {result['error']})") return [None] * 12 + [f"Model training failed: {result['error']}", None, None, None, None, f"Model training failed: {result['error']}"] signals_df, trades_df, equity_df = generate_signals(df, result) if signals_df.empty: logging.error("Failed to generate signals") return [None] * 12 + ["Failed to generate signals", None, None, None, None, "Failed to generate signals"] chart_plot = plot_indicators(df, ticker) signals_plot = plot_signals(signals_df, ticker) backtest_plot = plot_backtest(equity_df, trades_df, ticker) future_plot = plot_future_forecast(df, result, indicators) future_table = pd.DataFrame({ "Date": [df.index[-1] + timedelta(days=i+1) for i in range(horizon)], "Prediction": result["latest_prediction"] }) signals_table = signals_df.reset_index()[["Date", "Price", "Signal", "Position_Size", "Stop_Loss", "Take_Profit", "Equity"]] r2_output = plot_metrics_r2(result) error_output = plot_metrics_errors(result) precision_recall_output = plot_metrics_precision_recall(result) risk_output = plot_metrics_risk(result) loss_output = plot_loss_curve(result) architecture_output = plot_model_architecture(result) signals_csv = f"signals_{ticker}.csv" signals_df.to_csv(signals_csv) predictions_csv = f"predictions_{ticker}.csv" pd.DataFrame({ "Actual": result["actual"], "Forecast": result["forecast"] }).to_csv(predictions_csv) chart_png = f"chart_{ticker}.png" pio.write_image(chart_plot, chart_png, format='png') with open(log_path, 'r') as log_file: log_output = log_file.read() logging.info("Dashboard run completed successfully") return [ chart_plot, sentiment_text, signals_table, backtest_plot, future_plot, future_table, r2_output, error_output, precision_recall_output, risk_output, loss_output, architecture_output, "Dashboard generated successfully", chart_png, signals_csv, predictions_csv, signals_plot, log_output ] except Exception as e: logging.error(f"Dashboard error: {str(e)}") return [None] * 12 + [f"Error: {str(e)}", None, None, None, None, f"Error: {str(e)}"] def main_interface(): try: with gr.Blocks(title="Market Prediction Pro", theme=gr.themes.Default()) as app: gr.Markdown("# Market Prediction Pro") with gr.Row(): with gr.Column(scale=1): data_src = gr.Dropdown(["yahoo", "csv", "alpha_vantage", "finnhub", "twelvedata"], label="Data Source", value="yahoo") ticker = gr.Dropdown(DEFAULT_TICKERS, label="Ticker", value="AAPL") file_upload = gr.File(label="Upload CSV", visible=False) timeframe = gr.Dropdown(AVAILABLE_TIMEFRAMES, label="Timeframe", value="1d") start_date = gr.Textbox("2020-01-01", label="Start Date (YYYY-MM-DD)") end_date = gr.Textbox("2023-01-01", label="End Date (YYYY-MM-DD)") horizon = gr.Slider(1, 30, step=1, label="Horizon (days)", value=1) indicators = gr.CheckboxGroup(AVAILABLE_INDICATORS, label="Technical Indicators", value=["rsi", "macd", "bbands"]) include_sentiment = gr.Checkbox(label="Include Sentiment Analysis", value=False) news_api_key = gr.Textbox("a1018b32215d4c29bdfa1beae97e1f5c", label="News API Key (Optional)", type="password") alpha_api_key = gr.Textbox("IUCYQSZIPF3QIB3Q", label="Alpha Vantage API Key (Optional)", type="password") finnhub_api_key = gr.Textbox(label="Finnhub API Key (Optional)", type="password") twelvedata_api_key = gr.Textbox(label="Twelve Data API Key (Optional)", type="password") gr.Markdown("### Model Parameters") model = gr.Dropdown(AVAILABLE_MODELS, label="Model", value="LSTM") hidden_units = gr.Slider(10, 200, step=10, label="Hidden Units", value=50) n_layers = gr.Slider(1, 5, step=1, label="Number of Layers", value=2) epochs = gr.Slider(1, 100, step=1, label="Epochs", value=10) learning_rate = gr.Slider(0.0001, 0.1, step=0.0001, label="Learning Rate", value=0.001) beta1 = gr.Slider(0.1, 0.999, step=0.001, label="Adam Beta1", value=0.9) beta2 = gr.Slider(0.1, 0.999, step=0.001, label="Adam Beta2", value=0.999) weight_decay = gr.Slider(0.0, 0.1, step=0.001, label="Weight Decay", value=0.0001) dropout = gr.Slider(0.0, 0.5, step=0.05, label="Dropout", value=0.2) window_size = gr.Slider(5, 50, step=1, label="Window Size", value=20) test_split = gr.Slider(0.1, 0.5, step=0.05, label="Test Split Ratio", value=0.2) gr.Markdown("### Signal Generation Parameters") rsi_mid = gr.Slider(30, 70, step=1, label="RSI Midpoint", value=50) macd_sens = gr.Slider(5, 30, step=1, label="MACD Sensitivity", value=12) adx_thr = gr.Slider(10, 50, step=1, label="ADX Threshold", value=25) sent_thr = gr.Slider(-1.0, 1.0, step=0.01, label="Sentiment Threshold", value=0.05) vote_buy = gr.Slider(1, 5, step=1, label="Votes to Buy", value=3) vote_sell = gr.Slider(1, 5, step=1, label="Votes to Sell", value=3) gr.Markdown("### Feature Selection Parameters") feat_selector = gr.Dropdown(["none", "rfe", "sfm"], label="Feature Selector", value="none") feat_threshold = gr.Slider(0.0, 1.0, step=0.01, label="Feature Importance Threshold", value=0.01) run_button = gr.Button("Run Dashboard") with gr.Column(scale=2): output_text = gr.Textbox(label="Status", interactive=False) chart_output = gr.Plot(label="Price Chart with Indicators") sentiment_output = gr.Textbox(label="Sentiment Analysis", interactive=False) signals_output = gr.DataFrame(label="Generated Signals") backtest_plot = gr.Plot(label="Backtesting Results") future_forecast_output = gr.Plot(label="Future Forecast") future_forecast_table = gr.DataFrame(label="Future Forecast Data") r2_output = gr.Plot(label="R2 Score") error_output = gr.Plot(label="Prediction Errors") precision_recall_output = gr.Plot(label="Precision-Recall Curve") risk_output = gr.Plot(label="Risk Metrics") loss_output = gr.Plot(label="Loss Curve") architecture_output = gr.Plot(label="Model Architecture") log_output = gr.Textbox(label="Application Log", interactive=False, lines=10) data_src.change(lambda x: gr.update(visible=x=="csv"), inputs=data_src, outputs=file_upload) timeframe.change(update_horizon_label, inputs=timeframe, outputs=horizon) run_button.click( run_dashboard, inputs=[ data_src, ticker, file_upload, timeframe, start_date, end_date, horizon, indicators, include_sentiment, news_api_key, alpha_api_key, finnhub_api_key, twelvedata_api_key, gr.Number(value=10000, visible=False), gr.Number(value=0.01, visible=False), model, hidden_units, n_layers, epochs, learning_rate, beta1, beta2, weight_decay, dropout, window_size, test_split, rsi_mid, macd_sens, adx_thr, sent_thr, vote_buy, vote_sell, feat_selector, feat_threshold ], outputs=[ chart_output, sentiment_output, signals_output, backtest_plot, future_forecast_output, future_forecast_table, r2_output, error_output, precision_recall_output, risk_output, loss_output, architecture_output, output_text, gr.File(label="Chart PNG"), gr.File(label="Signals CSV"), gr.File(label="Predictions CSV"), gr.Plot(label="Signals Plot"), log_output ] ) return app except Exception as e: logging.error(f"Main interface creation failed: {str(e)}") return gr.Blocks().queue().launch() if __name__ == "__main__": main_interface().queue().launch()