get_price_fbp_sent.py

def getPrices(ticker, training_days, sent_df, mov_avg):
    """
    Using a sentiment DataFrame to find the first day and trains with the previous
    N days to make predictions that coincide with the sentiment date range.
    
    In order to properly backtest with sentiment, a certain amount of training 
    days need to come before the initial date from the sentiment DF.
    
    Also prepares the price data for FBProphet.
    """
    # The first day from the sentiment DF minus the amount of days to train with
    ago = datetime.strptime(
        sent_df.index[0], "%Y-%m-%d"
    ) - timedelta(
        days=training_days+mov_avg
    )
    
    # Getting prices
    prices = pd.DataFrame(
        client.get_prices_eod(
            ticker+"-USD.CC", 
            from_=ago.strftime("%Y-%m-%d")
        )
    )

    # Set index
    prices = prices.set_index('date', drop=True)
    
    # Getting the N Day Moving Average and rounding the values for some light data preprocessing
    prices['MA'] = prices[['open']].rolling(
        window=mov_avg
    ).mean().apply(lambda x: round(x, 2))

    # Dropping Nans
    prices.dropna(inplace=True)

    # Resetting format for FBP
    prices = prices.reset_index().rename(
        columns={"date": "ds", "MA": "y"}
    )
    
    return prices