import ccxt
import pandas as pd
import streamlit as st
import xgboost as xgb
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split

exchange = ccxt.mexc({
    'apiKey': 'mx0vglbkoCOwmqV5tn',
    'secret': 'c6d8cc8953fd405787ed54e3488ae0db',
    'enableRateLimit': True,
})

def fetch_ohlcv_data(symbol, timeframe, limit):
    return exchange.fetch_ohlcv(symbol, timeframe, since=None, limit=limit)

def data_to_dataframe(data):
    df = pd.DataFrame(data, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
    df.set_index('timestamp', inplace=True)
    return df

def prepare_dataset(df, lags):
    X = pd.DataFrame()
    y = pd.DataFrame()

    for lag in range(1, lags + 1):
        shifted_close = df['close'].shift(lag)
        X[f'close_lag_{lag}'] = shifted_close

    y = df['close'].shift(-1)
    return X, y

def train_xgboost_model(X_train, y_train):
    dtrain = xgb.DMatrix(X_train, label=y_train)
    params = {
        'objective': 'reg:squarederror',
        'eval_metric': 'rmse',
    }

    model = xgb.train(params, dtrain)
    return model

def predict_next_hour_price(df, model, lags):
    X_test = pd.DataFrame()

    for lag in range(1, lags + 1):
        shifted_close = df['close'].shift(lag)
        X_test[f'close_lag_{lag}'] = shifted_close

    X_test = X_test.tail(1)
    dtest = xgb.DMatrix(X_test)
    next_hour_price = model.predict(dtest)
    return next_hour_price

def evaluate_prediction_accuracy(y_test, y_pred):
    accuracy = r2_score(y_test, y_pred)
    return accuracy

def execute_trade(exchange, symbol, side, amount, leverage):
    order = exchange.create_market_order(symbol, side, amount)

    # Set leverage for the order
    if leverage is not None and leverage != 1:
        position = exchange.fetch_position(symbol)
        if position is not None:
            current_leverage = position['info']['leverage']
            if current_leverage != leverage:
                # Update leverage for the position
                exchange.set_leverage(leverage, symbol)

    return order

symbol = 'BTC/USDT'
timeframe = '1h'
limit = 100
lags = 12
test_size = 0.3
leverage = 5

ohlcv_data = fetch_ohlcv_data(symbol, timeframe, limit)
df = data_to_dataframe(ohlcv_data)

if st.button("hoo"):
    st.write(df)


X, y = prepare_dataset(df, lags)

# Replace missing values with the mean of the column
X = X.fillna(X.mean())
y = y.fillna(y.mean())

#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)

#model, scaler = train_xgboost_model(X_train, y_train)

#y_pred = model.predict(X_test)

# Inverse transform the predicted values to get the original scale
#y_pred = scaler.inverse_transform(y_pred.reshape(-1, 1))

#accuracy = evaluate_prediction_accuracy(y_test, y_pred)

#X, y = prepare_dataset(df, lags)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)

model = train_xgboost_model(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = evaluate_prediction_accuracy(y_test, y_pred)

if accuracy > 0.9:
    latest_data = df.tail(lags)
    predicted_price = predict_next_hour_price(latest_data, model, lags)
    current_price = latest_data['close'].iloc[-1]

    if predicted_price > current_price:
        # Buy BTC
        amount = 0.001  # Set the amount you want to trade
        order = execute_trade(exchange, symbol, 'buy', amount, leverage)
        print(f"Bought {amount} BTC at {current_price} with {leverage}x leverage")
    elif predicted_price < current_price:
        # Sell BTC
        amount = 0.001  # Set the amount you want to trade
        order = execute_trade(exchange, symbol, 'sell', amount, leverage)
        print(f"Sold {amount} BTC at {current_price} with {leverage}x leverage")
else:
    print("Accuracy is low, not placing order.")