Rank the saved stocks

MACD_Rank_and_Filter_Stocks.py

Compare_Stocks = pd.read_csv("<Your Path>\\SMA_Analysis\\All_Stocks.csv") # Read in the All_Stocks data to a dataframe
# Delete companies that don't have enough crosses observed. I am using 50 crosses as my cuttoff:
Not_Enough_Records = []  
Row = 0
while Row < (len(Compare_Stocks)):
    if Compare_Stocks.iloc[Row, 2] < 50:
        Not_Enough_Records.append(Row)  
    Row += 1
Compare_Stocks = Compare_Stocks.drop(Not_Enough_Records)  # Remove records that do not have enough crosses for us to observe
Avg_Accuracy = []  # List to hold the accuracy of each stock
i=0
while i < (len(Compare_Stocks)):
    Avg_Accuracy.append(Compare_Stocks.iloc[i,9])
    i += 1
# Create a dataframe from Compare_Stocks
df = Compare_Stocks[['Company','Days_Observed', 'Crosses', 'True_Positive', 'False_Positive', 'True_Negative', 'False_Negative', 'Sensitivity', 'Specificity', 'TPR', 'FPR', 'Accuracy']]
df["Companies_Ranked"] = df["Accuracy"].rank(ascending = False)  # Rank the stocks by their Accuracy
df.sort_values("Accuracy", inplace = True, ascending = False)  # Sort the ranked stocks
df.to_csv("<Your Path>\\SMA_Analysis\\5_Day_Avg_26_12_MACD.csv", index = False)  # Save the dataframe to a csv
# We now have a list of stocks ranked by how well the MACD indicator predicts their price change.
print("The average accuracy of all stocks observed: " + str(mean(Avg_Accuracy)))  # The overall accuracy of the MACD.