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README (1).md

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+---
+title: AI Market Prediction Dashboard
+emoji: 📈
+colorFrom: red
+colorTo: green
+sdk: gradio
+sdk_version: 5.22.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app (1).py

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+
+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)
+
+

config (1).py

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+# config.py
+DEFAULT_TICKERS = ["BTC-USD", "ETH-USD", "AAPL", "MSFT", "TSLA", "GOOG", "META", "AMZN", "XAUUSD=X", "EURUSD=X"]
+AVAILABLE_MODELS = ["LSTM", "GRU", "BiLSTM", "CNN", "Transformer", "Hybrid", "MLP"]
+AVAILABLE_TIMEFRAMES = ["1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h", "1d", "5d", "1wk", "1mo", "3mo"]
+AVAILABLE_INDICATORS = [
+    "adx", "ao", "apo", "aroon", "atr", "bbands", "cci", "cg", "cmo", "copp",
+    "dema", "dma", "donchian", "dpo", "efi", "ema", "eom", "fama", "fisher",
+    "hma", "ichimoku", "kama", "kc", "kdj", "kst", "macd", "mama", "mfi", "mom",
+    "natr", "obv", "ppo", "psar", "roc", "rsi", "rvi", "sma", "stoch", "stochrsi",
+    "supertrend", "tema", "trix", "tsi", "uo", "vortex", "vp", "vwap", "willr", "wma", "zlema"
+]
+DEFAULT_PARAMS = {
+    "epochs": 100,
+    "hidden_units": 256,
+    "layers": 3,
+    "learning_rate": 0.001,
+    "window_size": 30,
+    "test_split": 0.2
+}

requirements (1).txt

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+gradio
+pandas
+yfinance
+TA-Lib
+vaderSentiment
+newsapi-python
+plotly
+kaleido==0.2.1
+torch
+torchvision
+torchaudio
+torchsummary
+numpy
+scikit-learn
+matplotlib
+alpha_vantage
+textblob
+xgboost
+torchviz 
+graphviz