import pandas as pd
import numpy as np
import os


def add_sentiment(stock_data: pd.DataFrame, file_name) -> pd.DataFrame:
    file_path = os.path.join(os.path.dirname(__file__), file_name)
    sentiment_data = pd.read_csv(file_path, index_col='date', parse_dates=['date'])
    merged_df = pd.merge(stock_data, sentiment_data, left_index=True, right_index=True, how='left')
    return merged_df


def calc_rsi(over: pd.Series, fn_roll: callable, scale_down: bool = True) -> pd.Series:
    # Get the difference in price from previous step
    delta = over.diff()
    # Get rid of the first row, which is NaN since it did not have a previous row to calculate the differences
    delta = delta[1:]

    # Make the positive gains (up) and negative gains (down) Series
    up, down = delta.clip(lower=0), delta.clip(upper=0).abs()

    roll_up, roll_down = fn_roll(up), fn_roll(down)
    rs = roll_up / roll_down
    rsi = 100.0 - (100.0 / (1.0 + rs))

    # Avoid division-by-zero if `roll_down` is zero
    # This prevents inf and/or nan values.
    rsi[:] = np.select([roll_down == 0, roll_up == 0, True], [100, 0, rsi])
    # rsi = rsi.case_when([((roll_down == 0), 100), ((roll_up == 0), 0)])
    # This alternative to np.select works only for pd.__version__ >= 2.2.0.
    rsi.name = 'rsi'

    # Assert range
    valid_rsi = rsi[13:]
    assert ((0 <= valid_rsi) & (valid_rsi <= 100)).all()
    # Note: rsi[:length - 1] is excluded from above assertion because it is NaN for SMA.
    rsi = rsi.reindex(over.index)
    if scale_down:
        rsi = rsi / 100
    return rsi


def scale_stock_data(data: pd.DataFrame, scale_volume: float, scale_price: float) -> pd.DataFrame:
    data['volume'] = data['volume'] / scale_volume
    data['open'] = data['open'] / scale_price
    data['high'] = data['high'] / scale_price
    data['low'] = data['low'] / scale_price
    data['close'] = data['close'] / scale_price
    data['adj_close'] = data['adj_close'] / scale_price
    return data


def calculate_indicators(prices_dataframe: pd.DataFrame, use_regular_close=False) -> pd.DataFrame:
    if use_regular_close:
        col = 'close'
        prices_dataframe = prices_dataframe.drop(columns=['adj_close'])
    else:
        col = 'adj_close'
    prices_dataframe['return'] = prices_dataframe[col].pct_change(1).iloc[1:]
    prices_dataframe['log1p_return'] = np.log1p(prices_dataframe['return'])

    prices_dataframe['rsi_ema'] = calc_rsi(prices_dataframe[col], lambda s: s.ewm(span=14).mean())
    # prices_dataframe['rsi_sma'] = calc_rsi(prices_dataframe[col], lambda s: s.rolling(14).mean())

    # prices_dataframe['smstd_20'] = prices_dataframe[col].rolling(window=20).std()
    # prices_dataframe['sma_20'] = prices_dataframe[col].rolling(window=20).mean()
    prices_dataframe['ewma_20'] = prices_dataframe[col].ewm(span=20).mean()
    prices_dataframe['ewma_60'] = prices_dataframe[col].ewm(span=60).mean()
    prices_dataframe['ewmstd_20'] = prices_dataframe[col].ewm(span=20).std()
    prices_dataframe['macd'] = prices_dataframe[col].ewm(span=12).mean() - prices_dataframe[col].ewm(span=26).mean()
    # prices_dataframe['upper_band_sma'] = prices_dataframe.ewma_20 + (2 * prices_dataframe.ewmstd_20)
    # prices_dataframe['lower_band_sma'] = prices_dataframe.sma_20 - (2 * prices_dataframe.ewmstd_20)

    # prices_dataframe['upper_band_ewma'] = prices_dataframe.ewma_20 + (2 * prices_dataframe.ewmstd_20)
    # prices_dataframe['lower_band_ewma'] = prices_dataframe.ewma_20 - (2 * prices_dataframe.ewmstd_20)

    return prices_dataframe