import pandas as pd
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestRegressor
import time
from datetime import datetime, timezone, timedelta
import calendar

rf_reg_grid = {
    'bootstrap': [True],
    'max_depth': [5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None],
    'max_features': ['auto', 'sqrt', 'log2'],
    'min_samples_leaf': [1, 2, 4, 6],
    'min_samples_split': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12],
    'n_estimators': [80, 90, 100, 120, 150, 200]
}

best_params = {
    'bootstrap': [True], 'max_depth': [90], 'max_features': ['auto'], 'min_samples_leaf': [4], 'min_samples_split': [10], 'n_estimators': [100]
}
def get_data_and_store_csv(symbol, interval, start, end = None, limit=1000):
    """
    start and end must be isoformat YYYY-MM-DD
    We are using utc time zone
    the maximum records is 1000 per each Binance API call
    """
    # Source: https://stackoverflow.com/questions/66295187/how-do-i-get-all-the-prices-history-with-binance-api-for-a-crypto-using-python
    df = pd.DataFrame()

    if start is None:
        print('start time must not be None')
        exit(0)
    start = calendar.timegm(datetime.fromisoformat(start).timetuple()) * 1000

    if end is None:
        dt = datetime.now(timezone.utc)
        utc_time = dt.replace(tzinfo=timezone.utc)
        end = int(utc_time.timestamp()) * 1000
        return
    else:
        end = calendar.timegm(datetime.fromisoformat(end).timetuple()) * 1000
    last_time = None

    while len(df) == 0 or (last_time is not None and last_time < end):
        url = 'https://api.binance.com/api/v3/klines?symbol=' + \
              symbol + '&interval=' + interval + '&limit=1000'
        if(len(df) == 0):
            url += '&startTime=' + str(start)
        else:
            url += '&startTime=' + str(last_time)
        url += '&endTime=' + str(end)

        df2 = pd.read_json(url)
        df2.columns = ['Opentime', 'Open', 'High', 'Low', 'Close', 'Volume', 'Closetime',
                       'Quote asset volume', 'Number of trades', 'Taker by base', 'Taker buy quote', 'Ignore']

        dftmp = pd.DataFrame()
        dftmp = pd.concat([df2, dftmp], axis=0, ignore_index=True, keys=None)
        dftmp.Opentime = pd.to_datetime(dftmp.Opentime, unit='ms')
        dftmp = dftmp.drop(['Quote asset volume', 'Closetime',
                      'Number of trades', 'Taker by base', 'Taker buy quote', 'Ignore'], axis=1)
        column_names = ["Opentime", "Open", "High", "Low", "Close", "Volume"]

        dftmp.reset_index(drop=True, inplace=True)
        dftmp = dftmp.reindex(columns=column_names)

        last_time = (dftmp['Opentime'][len(dftmp) - 1] - datetime(1970, 1, 1)) // timedelta(milliseconds=1)

        df = pd.concat([df, dftmp], axis=0, ignore_index=True, keys=None)

    df.to_csv(f'data/{symbol}--interval-{interval}--start-{start}--end-{end}.csv',  index=False)
    return df

def get_stored_data(symbol, interval, start, end):
    start = calendar.timegm(datetime.fromisoformat(start).timetuple()) * 1000
    end = calendar.timegm(datetime.fromisoformat(end).timetuple()) * 1000
    df = pd.read_csv(
        #f'/crypto_prediction/data/{symbol}--interval-{interval}--start-{start}--end-{end}.csv',
        f'data/{symbol}--interval-{interval}--start-{start}--end-{end}.csv',
        parse_dates=['Opentime']
    )
    return df

def process_data(df):
    df["sale_year"] = df.Opentime.dt.year    
    df["sale_month"] = df.Opentime.dt.month
    df["sale_day"] = df.Opentime.dt.day
    df["sale_hour"] = df.Opentime.dt.hour
    df["minute"] = df.Opentime.dt.minute
    df["sale_day_of_week"] = df.Opentime.dt.dayofweek
    df["sale_day_of_year"] = df.Opentime.dt.dayofyear
    df["quarter"] =  df.Opentime.dt.quarter
    df["price"] = df.Close

    df.drop("Opentime", axis=1, inplace=True)
    df.drop("Open", axis=1, inplace=True)
    df.drop("High", axis=1, inplace=True)
    df.drop("Low", axis=1, inplace=True)
    df.drop("Close", axis=1, inplace=True)
    df.drop("Volume", axis=1, inplace=True)

    return df

def split_data(df, split_value):
    df_split = int(len(df) * split_value)
    
    df_train = df[:df_split]
    df_val = df[df_split:]

    X_train, y_train = df_train.drop("price", axis=1), df_train["price"]
    X_test, y_test = df_val.drop("price", axis = 1), df_val["price"]

    return X_train, X_test, y_train, y_test

def find_best_hyperparameters_and_train(X_train, y_train):
    global rf_reg_grid

    gs_rf_reg = GridSearchCV(
        RandomForestRegressor(),
        param_grid=best_params,
        cv=5,
        refit=True,
        n_jobs=-1
    )

    gs_rf_reg.fit(X_train, y_train)

    print("Best hyperparameters: ", gs_rf_reg.best_params_)

    return gs_rf_reg

def main():
    global models
    # Read data into panda dataframe
    #df = get_data_and_store_csv('XMRUSDT', '1m', '2020-01-01', '2022-09-04')
    df = get_stored_data('XMRUSDT', '1m', '2020-01-01', '2022-09-04')
    # Prepare data for moddeling
    df = process_data(df=df)
    # Split data into train and test sets
    X_train, X_test, y_train, y_test = split_data(df=df, split_value=0.9999) 

    # Model the data
    model = find_best_hyperparameters_and_train(
         X_train=X_train,
         y_train=y_train
    )

    # Score our modello
    print("Test data:\n", X_test)
    print("Prediction results:\n", model.predict(X_test))
    print("Correct values:\n", y_test)
    print('Model scored:\n', model.score(X_test, y_test))


if __name__ == "__main__":
    start_time = time.time()
    main()
    end_time = time.time() - start_time
    print(f'\n\nCompleted...\t\t\t\t\t{end_time // 3600} h {(end_time // 60) % 60} m {int(end_time % 60)}s')