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def backtestStockAndPredict(stock, n_days_lst, training_days_lst, mov_avg_lst, 
                            forecast_period_lst, fbp_intervals,stop_early=True, visualize=True):
    
    # Printing the stock
    print(f"\nBacktesting {stock}. . .")

    # Tuning parameters for stock
    results = parameterTuning(
        stock, 
        n_days_lst, 

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def parameterTuning(stock, n_days_lst, training_days_lst, mov_avg_lst, 
                    forecast_period_lst, fbp_intervals, stop_early=True):
    """
    Given a list of parameters for a specific stock. Iterates through
    different combination of parameters until a successful backtest
    performance is found.
    
    Optional stop_early variable for stopping tuning immediately 
    when a positive backtest result is found
    """

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def backtestStock(stock, pred_df, prices, price_history, interval_width):
    
    # Adding positions to the forecast DF
    positions = pred_df

    # Getting forecast prophet positions 
    positions['fbp_positions'] = positions.apply(
        lambda x: fbpPositions(x, short=True), 
        axis=1
    )

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def riskAnalysis(performance, prices, price_history, interval_width):
    """
    Analyzes the performance DataFrame to calculate various
    evaluation metrics on the backtest to determine if
    the backtest performance was favorable.
    """
    ### Hypothesis testing average returns
    
    # Weekly returns for fb prophet
    rets = performance['fbp_positions'].pct_change(5).dropna()

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def getStockPrices(stock, n_days, training_days, mov_avg):
    """
    Gets stock prices from now to N days ago and training amount will be in addition 
    to the number of days to train.
    """
    
    # Designating the Ticker
    ticker = yf.Ticker(stock)

    # Getting all price history

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def sentimentPositions(val, thresh=0.1, short=True):
    """
    Returns position as 1, -1, or 0 for Buy, Sell, 
    and Do Nothing respectively based on the given 
    sentiment value and threshold.
    """
    if val > thresh:
        return 1
    elif val < -thresh and short:
        return -1

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def fbpTrainPredict(df, forecast_period):
    """
    Uses FB Prophet and fits to a appropriately formatted DF. Makes a prediction N days into 
    the future based on given forecast period. Returns predicted values as a DF.
    """
    # Setting up prophet
    m = Prophet(
        daily_seasonality=True, 
        yearly_seasonality=True, 
        weekly_seasonality=True

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def getPrices(ticker, training_days, tweet_df, mov_avg, forecast_period):
    """
    Using a tweet DataFrame to find the first day and training data from 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 tweet DF as well 
    as the moving average and forecast period in order to accurately line up
    with the tweet DF.
    

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def getTweets(search_term, until, since, limit=20):
    """
    Configures Twint and returns a dataframe of tweets for a specific day.
    """
    # Configuring Twint for search
    c = twint.Config()

    # The limit of tweets to retrieve
    c.Limit = limit

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def backtestPerformanceVis(ticker, n_hours, training_hours, mov_avg, forecast_hours):
    """
    Consolidates the previous functions that support the backtesting process.
    """
    # Getting Price data
    print("Getting price data...")
    prices = getIntradayPrices(
        crypto=ticker, 
        n_hours=n_hours, 
        training_hours=training_hours,