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def getData(ticker, window, ma_period):
    """
    Grabs price data from a given ticker. Retrieves prices based on the given time window; from now
    to N days ago.  Sets the moving average period for prediction. Returns a preprocessed DF
    formatted for FB Prophet.
    """
    # Time periods
    now = datetime.now()

    # How far back to retrieve tweets

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# Initiating options
options = Options()

# Performing without GUI
options.headless = True
options.add_argument("--window-size=1920,1200")

# Accepting downloads without GUI
options.add_experimental_option("prefs", {
  "download.default_directory": r"/Users/marcosantos/Downloads",

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# Adding adblocker extension
options = Options()

options.add_extension(
    "/Users/marcosantos/Downloads/extension_1_37_2_0.crx"
)

# Opening the browser
driver = webdriver.Chrome(
    executable_path="/Users/marcosantos/Downloads/chromedriver",

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# Adding adblocker extension
options = Options()

options.add_extension(
    "/Users/marcosantos/Downloads/extension_1_37_2_0.crx"
)

# Opening the browser
driver = webdriver.Chrome(
    executable_path="/Users/marcosantos/Downloads/chromedriver",

marcosan93

def vectBacktest(df, thres=.01, short=True):
    """
    Accepts a dataframe of sentiment and log returns. Returns a series of the
    portfolio performance from the backtest.
    """
    
    # Getting the initial positions
    df['positions'] = df['sentiment'].apply(lambda x: getPositions(x,
                                                                   thres=thres,
                                                                   short=short))

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def getPositions(val, thres=.01, short=True):
    """
    Provided a specific sentiment value, will return 1, 0, or -1 representing
    buy, hold, or sell respectively. The threshold will determined how high or low the score 
    needs to be to trigger a buy or sell, otherwise it will be a hold.
    """
    
    # Sentiment score threshold
    thres = .01
    

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def sentimentAndPrice(ticker, start, end, numtweets=20):
    """
    Visually compares sentiment with the closing price of a given stock ticker.
    """
    # Creating a DF that contains daily tweets between two dates
    df = tweetByDay(start, end, pd.DataFrame(), search="$"+ticker, limit=numtweets)
        
    # Analyzing the sentiment of each tweet
    sent_df = getSentiment(
        df, 

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def getStockPrices(ticker, start, end):
    """
    Gets the historical daily prices between two dates. Applies the logarithmic function
    to return a dataframe of log returns each day.
    """
    # Setting the stock
    stock = yf.Ticker(ticker)

    # Getting historical prices
    stock_df = stock.history(start=end, end=start, interval="1d")[['Close']]

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def sentimentAndPrice(ticker, start, end, numtweets=20):
    """
    Visually compares sentiment with the closing price of a given stock ticker.
    """
    # Creating a DF that contains daily tweets between two dates
    df = tweetByDay(start, end, pd.DataFrame(), search="$"+ticker, limit=numtweets)
        
    # Analyzing the sentiment of each tweet
    sent_df = getSentiment(
        df, 

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def getStockPrices(ticker, start, end):
    """
    Gets the historical daily prices between two dates. Scaling the prices based on a
    given sentiment dataframe.
    """
    # Setting the stock
    stock = yf.Ticker(ticker)

    # Getting historical prices
    stock_df = stock.history(start=end, end=start, interval="1d")[['Close']]