app.py

import gradio as gr
import pandas as pd
import requests
from prophet import Prophet
import logging

logging.basicConfig(level=logging.INFO)

########################################
# OKX endpoints & utility
########################################

# 1) GET symbols (spot tickers)
OKX_TICKERS_ENDPOINT = "https://www.okx.com/api/v5/market/tickers?instType=SPOT"

# 2) GET historical candles for a symbol
#    e.g. https://www.okx.com/api/v5/market/candles?instId=BTC-USDT&bar=1H&limit=100
OKX_CANDLE_ENDPOINT = "https://www.okx.com/api/v5/market/candles"

# You can extend or modify this to match more of OKX's `bar` intervals
TIMEFRAME_MAPPING = {
    "1m": "1m",
    "5m": "5m",
    "15m": "15m",
    "30m": "30m",
    "1h": "1H",
    "2h": "2H",
    "4h": "4H",
    "6h": "6H",
    "12h": "12H",
    "1d": "1D",
    "1w": "1W",  # OKX supports 1W, etc.
}

def fetch_okx_symbols():
    """
    Fetch the list of symbols (instId) from OKX Spot tickers.
    """
    logging.info("Fetching symbols from OKX Spot tickers...")
    try:
        resp = requests.get(OKX_TICKERS_ENDPOINT, timeout=30)
        resp.raise_for_status()
        json_data = resp.json()

        if json_data.get("code") != "0":
            logging.error(f"Non-zero code returned: {json_data}")
            return ["Error: Could not fetch OKX symbols"]

        data = json_data.get("data", [])
        # Example item in data: { "instId": "ETH-USDT", "instType": "SPOT", ... }
        symbols = [item["instId"] for item in data if item.get("instType") == "SPOT"]
        if not symbols:
            logging.warning("No spot symbols found.")
            return ["Error: No spot symbols found."]
        
        logging.info(f"Fetched {len(symbols)} OKX spot symbols.")
        return sorted(symbols)

    except Exception as e:
        logging.error(f"Error fetching OKX symbols: {e}")
        return [f"Error: {str(e)}"]

def fetch_okx_candles(symbol, timeframe="1H", limit=100):
    """
    Fetch historical candle data for a symbol from OKX.
    timeframe must match OKX's `bar` (e.g. "1H", "4H", "1D").
    Returns (DataFrame, error_message) or (DataFrame, "").
    """
    logging.info(f"Fetching {limit} candles for {symbol} @ {timeframe} from OKX...")
    params = {
        "instId": symbol,
        "bar": timeframe,
        "limit": limit
    }

    try:
        resp = requests.get(OKX_CANDLE_ENDPOINT, params=params, timeout=30)
        resp.raise_for_status()
        json_data = resp.json()

        if json_data.get("code") != "0":
            msg = f"OKX returned code={json_data.get('code')}, msg={json_data.get('msg')}"
            logging.error(msg)
            return pd.DataFrame(), msg

        # Data looks like: ["1673684400000", "20923.7", "20952.5", "20881.3", "20945.8", "927.879", "19412314.5671"]
        # Let's parse columns: [0] ts, [1] open, [2] high, [3] low, [4] close, [5] volume, [6] ??? quoteVol
        items = json_data.get("data", [])
        if not items:
            warning_msg = f"No candle data returned for {symbol}."
            logging.warning(warning_msg)
            return pd.DataFrame(), warning_msg
        
        # items is a list of lists, each is a candle. Reverse if needed to go old->new:
        # OKX returns the most recent data first, so we invert it for chronological order
        items.reverse()

        df = pd.DataFrame(items, columns=[
            "timestamp", "open", "high", "low", "close", "volume", "quoteVolume"
        ])
        df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
        df[["open", "high", "low", "close", "volume", "quoteVolume"]] = df[
            ["open", "high", "low", "close", "volume", "quoteVolume"]
        ].astype(float)

        logging.info(f"Fetched {len(df)} rows for {symbol}.")
        return df, ""
    except Exception as e:
        err_msg = f"Error fetching candles for {symbol}: {e}"
        logging.error(err_msg)
        return pd.DataFrame(), err_msg

########################################
# Prophet pipeline
########################################

def prepare_data_for_prophet(df):
    """
    Convert the DataFrame to a Prophet-compatible format.
    """
    if df.empty:
        logging.warning("Empty DataFrame, cannot prepare data for Prophet.")
        return pd.DataFrame(columns=["ds", "y"])

    df_prophet = df.rename(columns={"timestamp": "ds", "close": "y"})
    return df_prophet[["ds", "y"]]

def prophet_forecast(df_prophet, periods=10, freq="H"):
    """
    Train a Prophet model and forecast.
    """
    if df_prophet.empty:
        logging.warning("Prophet input is empty, no forecast can be generated.")
        return pd.DataFrame(), "No data to forecast."

    try:
        model = Prophet()
        model.fit(df_prophet)
        future = model.make_future_dataframe(periods=periods, freq=freq)
        forecast = model.predict(future)
        return forecast, ""
    except Exception as e:
        logging.error(f"Forecast error: {e}")
        return pd.DataFrame(), f"Forecast error: {e}"

def prophet_wrapper(df_prophet, forecast_steps, freq):
    """
    Do the forecast, then slice out the new/future rows.
    """
    if len(df_prophet) < 10:
        return pd.DataFrame(), "Not enough data for forecasting (need >=10 rows)."

    full_forecast, err = prophet_forecast(df_prophet, forecast_steps, freq)
    if err:
        return pd.DataFrame(), err

    # Only keep the newly generated future portion
    future_only = full_forecast.iloc[len(df_prophet):, ["ds", "yhat", "yhat_lower", "yhat_upper"]]
    return future_only, ""

########################################
# Main Gradio logic
########################################

def predict(symbol, timeframe, forecast_steps):
    """
    Orchestrate candle fetch + prophet forecast.
    """
    # Convert user timeframe to OKX bar param
    okx_bar = TIMEFRAME_MAPPING.get(timeframe, "1H")

    # Let’s fetch 500 candles
    df_raw, err = fetch_okx_candles(symbol, timeframe=okx_bar, limit=500)
    if err:
        return pd.DataFrame(), err

    df_prophet = prepare_data_for_prophet(df_raw)
    # We guess frequency from timeframe. If timeframe is "1h", we'll do freq="H" in Prophet, etc.
    # We'll do a simple mapping here:
    freq = "H" if "h" in timeframe.lower() else "D"  # e.g. "1h" -> "H", "1d" -> "D"

    future_df, err2 = prophet_wrapper(df_prophet, forecast_steps, freq)
    if err2:
        return pd.DataFrame(), err2

    return future_df, ""

def display_forecast(symbol, timeframe, forecast_steps):
    """
    For the Gradio UI, returns forecast or error message.
    """
    logging.info(f"User requested: symbol={symbol}, timeframe={timeframe}, steps={forecast_steps}")
    forecast_df, error = predict(symbol, timeframe, forecast_steps)
    if error:
        return f"Error: {error}"
    return forecast_df

def main():
    # Fetch OKX symbols
    symbols = fetch_okx_symbols()
    if not symbols or "Error" in symbols[0]:
        symbols = ["No symbols available"]

    with gr.Blocks() as demo:
        gr.Markdown("# OKX Price Forecasting with Prophet")
        gr.Markdown(
            "This app uses OKX's spot market candles to predict future price movements. "
            "Select a symbol and timeframe, specify forecast steps, then click 'Generate Forecast'. "
            "No proxies or special access required."
        )

        symbol_dd = gr.Dropdown(
            label="Symbol",
            choices=symbols,
            value=symbols[0] if symbols else None
        )
        timeframe_dd = gr.Dropdown(
            label="Timeframe",
            choices=["1m", "5m", "15m", "30m", "1h", "2h", "4h", "6h", "12h", "1d", "1w"],
            value="1h"
        )
        steps_slider = gr.Slider(
            label="Forecast Steps (hours/days depending on timeframe)",
            minimum=1,
            maximum=100,
            value=10
        )
        forecast_btn = gr.Button("Generate Forecast")

        output_df = gr.Dataframe(
            label="Future Forecast Only",
            headers=["ds", "yhat", "yhat_lower", "yhat_upper"]
        )

        forecast_btn.click(
            fn=display_forecast,
            inputs=[symbol_dd, timeframe_dd, steps_slider],
            outputs=output_df
        )

        gr.Markdown(
            "Looking for more automation? Check out this "
            "[crypto trading bot](https://www.gunbot.com)."
        )

    return demo

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