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 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' ) analyzer = SentimentIntensityAnalyzer() def sentiment_analysis(ticker, start_date, end_date, api_key): try: if not api_key: return "No API key provided", None newsapi = NewsApiClient(api_key=api_key) start = pd.to_datetime(start_date) end = pd.to_datetime(end_date) articles = newsapi.get_everything( q=ticker, from_param=start.strftime("%Y-%m-%d"), to=end.strftime("%Y-%m-%d"), language='en', sort_by='relevancy' ) sentiments = [analyzer.polarity_scores(article["title"])["compound"] for article in articles["articles"]] avg_sentiment = np.mean(sentiments) if sentiments else 0.0 sentiment_text = f"Average sentiment for {ticker}: {avg_sentiment:.2f}" return sentiment_text, avg_sentiment except Exception as e: logging.error(f"Sentiment analysis failed: {str(e)}") return f"Sentiment analysis failed: {str(e)}", None 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, 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) 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) if include_sentiment and news_api_key: df = add_sentiment(df, ticker, news_api_key, start_date, end_date) sentiment_text, sentiment_score = sentiment_analysis(ticker, start_date, end_date, news_api_key) 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_plot = plot_metrics_r2(result) error_plot = plot_metrics_errors(result) precision_recall_plot = plot_metrics_precision_recall(result) risk_plot = plot_metrics_risk(result) loss_plot = plot_loss_curve(result) architecture_plot = 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_plot, error_plot, precision_recall_plot, risk_plot, loss_plot, architecture_plot, "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"], 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(label="News API Key", visible=False, type="password") alpha_api_key = gr.Textbox(label="Alpha Vantage API Key", type="password") account_size = gr.Slider(1000, 100000, step=1000, label="Account Size ($)", value=10000) risk_percent = gr.Slider(0.01, 0.1, step=0.01, label="Risk per Trade (%)", value=0.01) with gr.Column(scale=1): model = gr.Dropdown(AVAILABLE_MODELS, label="Model", value="LSTM") hidden_units = gr.Slider(16, 256, step=16, label="Hidden Units", value=64) n_layers = gr.Slider(1, 4, step=1, label="Layers", value=1) epochs = gr.Slider(10, 100, step=10, label="Epochs", value=50) learning_rate = gr.Slider(0.0001, 0.01, step=0.0001, label="Learning Rate", value=0.001) beta1 = gr.Slider(0.8, 0.99, step=0.01, label="Beta 1", value=0.9) beta2 = gr.Slider(0.9, 0.999, step=0.001, label="Beta 2", value=0.999) weight_decay = gr.Slider(0.0, 0.1, step=0.01, label="Weight Decay", value=0.01) dropout = gr.Slider(0.0, 0.5, step=0.05, label="Dropout", value=0.2) window_size = gr.Slider(5, 60, step=5, label="Window Size", value=30) test_split = gr.Slider(0.1, 0.5, step=0.05, label="Test Split", value=0.2) rsi_mid = gr.Slider(30, 70, step=5, label="RSI Middle", value=50) macd_sens = gr.Slider(0.0, 0.5, step=0.05, label="MACD Sensitivity", value=0.0) adx_thr = gr.Slider(10, 50, step=5, label="ADX Threshold", value=20) sent_thr = gr.Slider(0.0, 0.5, step=0.05, label="Sentiment Threshold", value=0.1) vote_buy = gr.Slider(1, 5, step=1, label="Vote Buy Threshold", value=2) vote_sell = gr.Slider(-5, -1, step=1, label="Vote Sell Threshold", value=-2) feat_selector = gr.Dropdown(["RandomForest", "PCA"], label="Feature Selector", value="RandomForest") feat_threshold = gr.Slider(0.0, 0.5, step=0.05, label="Feature Importance Threshold", value=0.0) run_btn = gr.Button("Run Analysis") with gr.Tabs(): with gr.TabItem("Price & Indicators"): chart_plot = gr.Plot(label="📈 Price and Indicators") with gr.TabItem("Signals"): signals_plot = gr.Plot(label="📈 Trading Signals") signals_table = gr.DataFrame(label="📅 Signals") with gr.TabItem("Sentiment"): sentiment_text = gr.Textbox(label="Sentiment Analysis") with gr.TabItem("Forecast"): backtest_plot = gr.Plot(label="📈 Backtest Results") future_plot = gr.Plot(label="📈 Future Forecast") future_table = gr.DataFrame(label="📅 Future Predictions") with gr.TabItem("Metrics"): r2_plot = gr.Plot(label="📊 R² & MAPE") error_plot = gr.Plot(label="📊 Error Metrics") precision_recall_plot = gr.Plot(label="📊 Precision & Recall") risk_plot = gr.Plot(label="📊 Risk Metrics") loss_plot = gr.Plot(label="📈 Training Loss Curve") architecture_plot = gr.Plot(label="🧠 Model Architecture") with gr.TabItem("Export"): status = gr.Textbox(label="Status") export_chart = gr.File(label="Export Chart (PNG)") export_signals = gr.File(label="Export Signals (CSV)") export_predictions = gr.File(label="Export Predictions (CSV)") log_output = gr.Textbox(label="Debug Logs", lines=10) data_src.change(fn=lambda src: gr.update(visible=(src == "csv")), inputs=[data_src], outputs=[file_upload]) include_sentiment.change(fn=lambda sent: gr.update(visible=sent), inputs=[include_sentiment], outputs=[news_api_key]) timeframe.change(fn=update_horizon_label, inputs=[timeframe], outputs=[horizon]) run_btn.click( fn=run_dashboard, inputs=[data_src, ticker, file_upload, timeframe, start_date, end_date, horizon, indicators, include_sentiment, news_api_key, alpha_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], outputs=[chart_plot, sentiment_text, signals_table, backtest_plot, future_plot, future_table, r2_plot, error_plot, precision_recall_plot, risk_plot, loss_plot, architecture_plot, status, export_chart, export_signals, export_predictions, signals_plot, log_output] ) except Exception as e: logging.error(f"Error in main_interface: {str(e)}") raise return app if __name__ == "__main__": main_interface().launch(server_name="0.0.0.0", server_port=7860, share=False)