app_3.py

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()