Upload app_4.py

app_4.py

@@ -0,0 +1,234 @@
+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-%m"),
+            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, 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)
+        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_output, error_output, precision_recall_plot, 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(list(AVAILABLE_MODELS.keys()), 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_output = 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_output, future_forecast_output, future_forecast_table,
+                    r2_output, error_output, precision_recall_plot, 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
+                ]
+            )
+    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()