app.py

import gradio as gr
import pandas as pd
import requests
from prophet import Prophet
import plotly.graph_objs as go
import math
import numpy as np

# Constants for API endpoints
OKX_TICKERS_ENDPOINT = "https://www.okx.com/api/v5/market/tickers?instType=SPOT"
OKX_CANDLE_ENDPOINT = "https://www.okx.com/api/v5/market/candles"

TIMEFRAME_MAPPING = {
    "1m": "1m",
    "5m": "5m",
    "15m": "15m",
    "30m": "30m",
    "1h": "1H",
    "2h": "2H",
    "4h": "4H",
    "6h": "6H",
    "12h": "12H",
    "1d": "1D",
    "1w": "1W",
}

# Function to calculate technical indicators
def calculate_technical_indicators(df):
    # RSI Calculation
    delta = df['close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
    rs = gain / loss
    df['RSI'] = 100 - (100 / (1 + rs))
    
    # MACD Calculation
    exp1 = df['close'].ewm(span=12, adjust=False).mean()
    exp2 = df['close'].ewm(span=26, adjust=False).mean()
    df['MACD'] = exp1 - exp2
    df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()
    
    # Bollinger Bands Calculation
    df['MA20'] = df['close'].rolling(window=20).mean()
    df['BB_upper'] = df['MA20'] + 2 * df['close'].rolling(window=20).std()
    df['BB_lower'] = df['MA20'] - 2 * df['close'].rolling(window=20).std()
    
    return df

# Function to create technical analysis charts
def create_technical_charts(df):
    # Price and Bollinger Bands Chart
    fig1 = go.Figure()
    fig1.add_trace(go.Candlestick(
        x=df['timestamp'],
        open=df['open'],
        high=df['high'],
        low=df['low'],
        close=df['close'],
        name='Price'
    ))
    fig1.add_trace(go.Scatter(x=df['timestamp'], y=df['BB_upper'], name='Upper BB', line=dict(color='gray', dash='dash')))
    fig1.add_trace(go.Scatter(x=df['timestamp'], y=df['BB_lower'], name='Lower BB', line=dict(color='gray', dash='dash')))
    fig1.update_layout(title='Price and Bollinger Bands', xaxis_title='Date', yaxis_title='Price')

    # RSI Chart
    fig2 = go.Figure()
    fig2.add_trace(go.Scatter(x=df['timestamp'], y=df['RSI'], name='RSI'))
    fig2.add_hline(y=70, line_dash="dash", line_color="red")
    fig2.add_hline(y=30, line_dash="dash", line_color="green")
    fig2.update_layout(title='RSI Indicator', xaxis_title='Date', yaxis_title='RSI')

    # MACD Chart
    fig3 = go.Figure()
    fig3.add_trace(go.Scatter(x=df['timestamp'], y=df['MACD'], name='MACD'))
    fig3.add_trace(go.Scatter(x=df['timestamp'], y=df['Signal_Line'], name='Signal Line'))
    fig3.update_layout(title='MACD', xaxis_title='Date', yaxis_title='Value')

    return fig1, fig2, fig3

# Fetch available symbols from OKX API
def fetch_okx_symbols():
    try:
        resp = requests.get(OKX_TICKERS_ENDPOINT)
        data = resp.json().get("data", [])
        symbols = [item["instId"] for item in data if item.get("instType") == "SPOT"]
        return ["BTC-USDT"] + symbols if symbols else ["BTC-USDT"]
    except Exception as e:
        print(f"Error fetching symbols: {e}")
        return ["BTC-USDT"]

# Fetch historical candle data from OKX API
def fetch_okx_candles(symbol, timeframe="1H", total=2000):
    calls_needed = math.ceil(total / 300)
    all_data = []
    
    for _ in range(calls_needed):
        params = {"instId": symbol, "bar": timeframe, "limit": 300}
        try:
            resp = requests.get(OKX_CANDLE_ENDPOINT, params=params)
            resp.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
            data = resp.json().get("data", [])
        except requests.exceptions.RequestException as e:
            print(f"Error fetching candles: {e}")
            return pd.DataFrame()
        except (ValueError, KeyError) as e:
            print(f"Error parsing candle data: {e}")
            return pd.DataFrame()
        
        if not data:
            break
        
        columns = ["ts", "o", "h", "l", "c"]
        df_chunk = pd.DataFrame(data, columns=columns)
        df_chunk.rename(columns={"ts": "timestamp", "o": "open", 
                                  "h": "high", "l": "low", 
                                  "c": "close"}, inplace=True)
        all_data.append(df_chunk)

        if len(data) < 300:
            break
    
    if not all_data:
        return pd.DataFrame()

    df_all = pd.concat(all_data)
    
    # Convert timestamps to datetime and calculate indicators
    df_all["timestamp"] = pd.to_datetime(df_all["timestamp"], unit="ms")
    numeric_cols = ["open", "high", "low", "close"]
    df_all[numeric_cols] = df_all[numeric_cols].astype(float)
    df_all = calculate_technical_indicators(df_all)
    
    return df_all

# Prepare data for Prophet forecasting
def prepare_data_for_prophet(df):
    if df.empty:
        return pd.DataFrame(columns=["ds", "y"])
    df_prophet = df.rename(columns={"timestamp": "ds", "close": "y"})
    return df_prophet[["ds", "y"]]

# Perform forecasting using Prophet
def prophet_forecast(df_prophet, periods=10, freq="h", daily_seasonality=False, weekly_seasonality=False, yearly_seasonality=False, seasonality_mode="additive", changepoint_prior_scale=0.05):
    if df_prophet.empty:
        return pd.DataFrame(), "No data for Prophet."
    
    try:
        model = Prophet(
            daily_seasonality=daily_seasonality,
            weekly_seasonality=weekly_seasonality,
            yearly_seasonality=yearly_seasonality,
            seasonality_mode=seasonality_mode,
            changepoint_prior_scale=changepoint_prior_scale,
        )
        model.fit(df_prophet)
        future = model.make_future_dataframe(periods=periods, freq=freq)
        forecast = model.predict(future)
        return forecast, ""
    except Exception as e:
        return pd.DataFrame(), f"Forecast error: {e}"

# Wrapper function for forecasting
def prophet_wrapper(df_prophet, forecast_steps, freq, daily_seasonality, weekly_seasonality, yearly_seasonality, seasonality_mode, changepoint_prior_scale):
    if len(df_prophet) < 10:
        return pd.DataFrame(), "Not enough data for forecasting (need >=10 rows)."

    full_forecast, err = prophet_forecast(
        df_prophet,
        periods=forecast_steps,
        freq=freq,
        daily_seasonality=daily_seasonality,
        weekly_seasonality=weekly_seasonality,
        yearly_seasonality=yearly_seasonality,
        seasonality_mode=seasonality_mode,
        changepoint_prior_scale=changepoint_prior_scale,
    )
    if err:
        return pd.DataFrame(), err

    future_only = full_forecast.loc[len(df_prophet):, ["ds", "yhat", "yhat_lower", "yhat_upper"]]
    return future_only, ""

# Create forecast plot
def create_forecast_plot(forecast_df):
    if forecast_df.empty:
        return go.Figure()

    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=forecast_df["ds"],
        y=forecast_df["yhat"],
        mode="lines",
        name="Forecast",
        line=dict(color="blue", width=2)
    ))

    fig.add_trace(go.Scatter(
        x=forecast_df["ds"],
        y=forecast_df["yhat_lower"],
        fill=None,
        mode="lines",
        line=dict(width=0),
        showlegend=True,
        name="Lower Bound"
    ))

    fig.add_trace(go.Scatter(
        x=forecast_df["ds"],
        y=forecast_df["yhat_upper"],
        fill="tonexty",
        mode="lines",
        line=dict(width=0),
        name="Upper Bound"
    ))

    fig.update_layout(
        title="Price Forecast",
        xaxis_title="Time",
        yaxis_title="Price",
        hovermode="x unified",
        template="plotly_white",
    )
    return fig

# Function to display forecast and technical analysis charts
def display_forecast(symbol, timeframe, forecast_steps, total_candles, daily_seasonality, weekly_seasonality, yearly_seasonality, seasonality_mode, changepoint_prior_scale):
    df_raw, forecast_df, error = predict(
        symbol=symbol,
        timeframe=timeframe,
        forecast_steps=forecast_steps,
        total_candles=total_candles,
        daily_seasonality=daily_seasonality,
        weekly_seasonality=weekly_seasonality,
        yearly_seasonality=yearly_seasonality,
        seasonality_mode=seasonality_mode,
        changepoint_prior_scale=changepoint_prior_scale
    )

    if error:
        return None, None, None, None, pd.DataFrame() # Return empty dataframe for forecast_df

    forecast_plot = create_forecast_plot(forecast_df)
    tech_plot, rsi_plot, macd_plot = create_technical_charts(df_raw)
    
    # Prepare forecast data for the Dataframe output
    forecast_df_display = forecast_df.loc[:, ["ds", "yhat", "yhat_lower", "yhat_upper"]].copy()
    forecast_df_display.rename(columns={"ds": "Date", "yhat": "Forecast", "yhat_lower": "Lower Bound", "yhat_upper": "Upper Bound"}, inplace=True)

    return forecast_plot, tech_plot, rsi_plot, macd_plot, forecast_df_display

# Main prediction function
def predict(symbol, timeframe, forecast_steps, total_candles, daily_seasonality, weekly_seasonality, yearly_seasonality, seasonality_mode, changepoint_prior_scale):
    okx_bar = TIMEFRAME_MAPPING.get(timeframe, "1H")
    df_raw = fetch_okx_candles(symbol=symbol, timeframe=okx_bar, total=total_candles)

    if df_raw.empty:
        return pd.DataFrame(), pd.DataFrame(), "No data fetched."

    df_prophet = prepare_data_for_prophet(df_raw)
    freq = "h" if "h" in timeframe.lower() else "d"

    future_df, err2 = prophet_wrapper(
        df_prophet,
        forecast_steps,
        freq,
        daily_seasonality,
        weekly_seasonality,
        yearly_seasonality,
        seasonality_mode,
        changepoint_prior_scale,
    )
    
    if err2:
        return pd.DataFrame(), pd.DataFrame(), err2

    return df_raw, future_df, ""


# Main Gradio app setup
def main():
    symbols = fetch_okx_symbols()
    
    with gr.Blocks(theme=gr.themes.Base()) as demo:
        # Header
        with gr.Row():
            gr.Markdown("# CryptoVision")

        # Market Selection and Forecast Parameters
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("### Market Selection")
                symbol_dd = gr.Dropdown(
                    label="Trading Pair",
                    choices=symbols,
                    value="BTC-USDT"
                )
                timeframe_dd = gr.Dropdown(
                    label="Timeframe",
                    choices=list(TIMEFRAME_MAPPING.keys()),
                    value="1h"
                )
            with gr.Column(scale=1):
                gr.Markdown("### Forecast Parameters")
                forecast_steps_slider = gr.Slider(
                    label="Forecast Steps",
                    minimum=1,
                    maximum=100,
                    value=24,
                    step=1
                )
                total_candles_slider = gr.Slider(
                    label="Historical Candles",
                    minimum=300,
                    maximum=3000,
                    value=2000,
                    step=100
                )

        # Advanced Settings
        with gr.Row():
            with gr.Column():
                gr.Markdown("### Advanced Settings")
                daily_box = gr.Checkbox(label="Daily Seasonality", value=True)
                weekly_box = gr.Checkbox(label="Weekly Seasonality", value=True)
                yearly_box = gr.Checkbox(label="Yearly Seasonality", value=False)
                seasonality_mode_dd = gr.Dropdown(
                    label="Seasonality Mode",
                    choices=["additive", "multiplicative"],
                    value="additive"
                )
                changepoint_scale_slider = gr.Slider(
                    label="Changepoint Prior Scale",
                    minimum=0.01,
                    maximum=1.0,
                    step=0.01,
                    value=0.05
                )

        # Generate Forecast Button
        forecast_btn = gr.Button("Generate Forecast", variant="primary", size="lg")

        # Output Plots
        with gr.Row():
            forecast_plot = gr.Plot(label="Price Forecast")
        
        with gr.Row():
            tech_plot = gr.Plot(label="Technical Analysis")
            rsi_plot = gr.Plot(label="RSI Indicator")
        
        with gr.Row():
            macd_plot = gr.Plot(label="MACD")
        
        # Output Data Table
        forecast_df = gr.Dataframe(
            label="Forecast Data",
            headers=["Date", "Forecast", "Lower Bound", "Upper Bound"]
        )

        # Button click functionality
        forecast_btn.click(
            fn=display_forecast,
            inputs=[
                symbol_dd,
                timeframe_dd,
                forecast_steps_slider,
                total_candles_slider,
                daily_box,
                weekly_box,
                yearly_box,
                seasonality_mode_dd,
                changepoint_scale_slider,
            ],
            outputs=[forecast_plot, tech_plot, rsi_plot, macd_plot, forecast_df]
        )

    return demo

if __name__ == "__main__":
    app = main()
    app.launch()