Code to conduct an OBV analysis on our stored historical data sets.

DSR_OBV_Analysis.py

# OBV Analysis, feel free to replace this section with your own analysis -------------------------------------------------------------------------
list_files = (glob.glob("<Your Path>\\Daily_Stock_Report\\Stocks\\*.csv")) # Creates a list of all csv filenames in the stocks folder
new_data = [] #  This will be a 2D array to hold our stock name and OBV score
interval = 0  # Used for iteration
while interval < len(list_files):
    Data = pd.read_csv(list_files[interval]).tail(10)  # Gets the last 10 days of trading for the current stock in iteration
    pos_move = []  # List of days that the stock price increased
    neg_move = []  # List of days that the stock price increased
    OBV_Value = 0  # Sets the initial OBV_Value to zero
    count = 0
    while (count < 10):  # 10 because we are looking at the last 10 trading days
        if Data.iloc[count,1] < Data.iloc[count,4]:  # True if the stock increased in price
            pos_move.append(count)  # Add the day to the pos_move list
        elif Data.iloc[count,1] > Data.iloc[count,4]:  # True if the stock decreased in price
            neg_move.append(count)  # Add the day to the neg_move list
        count += 1
    count2 = 0
    for i in pos_move:  # Adds the volumes of positive days to OBV_Value, divide by opening price to normalize across all stocks
        OBV_Value = round(OBV_Value + (Data.iloc[i,5]/Data.iloc[i,1]))
    for i in neg_move:  # Subtracts the volumes of negative days from OBV_Value, divide by opening price to normalize across all stocks
        OBV_Value = round(OBV_Value - (Data.iloc[i,5]/Data.iloc[i,1]))
    Stock_Name = ((os.path.basename(list_files[interval])).split(".csv")[0])  # Get the name of the current stock we are analyzing
    new_data.append([Stock_Name, OBV_Value])  # Add the stock name and OBV value to the new_data list
    interval += 1
df = pd.DataFrame(new_data, columns = ['Stock', 'OBV_Value'])  # Creates a new dataframe from the new_data list
df["Stocks_Ranked"] = df["OBV_Value"].rank(ascending = False)  # Rank the stocks by their OBV_Values
df.sort_values("OBV_Value", inplace = True, ascending = False)  # Sort the ranked stocks
df.to_csv("<Your Path>\\Daily_Stock_Report\\OBV_Ranked.csv", index = False)  # Save the dataframe to a csv without the index column