Testing raised standards




Now that we've changed our standards, we're curious to see what suggestions we get.

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, preprocessing
import pandas as pd
from matplotlib import style
import statistics

style.use("ggplot")


FEATURES =  ['DE Ratio',
             'Trailing P/E',
             'Price/Sales',
             'Price/Book',
             'Profit Margin',
             'Operating Margin',
             'Return on Assets',
             'Return on Equity',
             'Revenue Per Share',
             'Market Cap',
             'Enterprise Value',
             'Forward P/E',
             'PEG Ratio',
             'Enterprise Value/Revenue',
             'Enterprise Value/EBITDA',
             'Revenue',
             'Gross Profit',
             'EBITDA',
             'Net Income Avl to Common ',
             'Diluted EPS',
             'Earnings Growth',
             'Revenue Growth',
             'Total Cash',
             'Total Cash Per Share',
             'Total Debt',
             'Current Ratio',
             'Book Value Per Share',
             'Cash Flow',
             'Beta',
             'Held by Insiders',
             'Held by Institutions',
             'Shares Short (as of',
             'Short Ratio',
             'Short % of Float',
             'Shares Short (prior ']


def Build_Data_Set():
    data_df = pd.DataFrame.from_csv("key_stats_acc_perf_WITH_NA.csv")

    #data_df = data_df[:100]
    data_df = data_df.reindex(np.random.permutation(data_df.index))
    data_df = data_df.replace("NaN",0).replace("N/A",0)
    

    X = np.array(data_df[FEATURES].values)#.tolist())

    y = (data_df["Status"]
         .replace("underperform",0)
         .replace("outperform",1)
         .values.tolist())

    X = preprocessing.scale(X)

    Z = np.array(data_df[["stock_p_change","sp500_p_change"]])


    return X,y,Z


def Analysis():

    test_size = 1

    invest_amount = 10000
    total_invests = 0

    
    if_market = 0
    if_strat = 0



    
    X, y, Z = Build_Data_Set()
    print(len(X))

    
    clf = svm.SVC(kernel="linear", C= 1.0)
    clf.fit(X[:-test_size],y[:-test_size])

    correct_count = 0

    for x in range(1, test_size+1):
        if clf.predict(X[-x])[0] == y[-x]:
            correct_count += 1

        if clf.predict(X[-x])[0] == 1:
            invest_return = invest_amount + (invest_amount * (Z[-x][0]/100))
            market_return = invest_amount + (invest_amount * (Z[-x][1]/100))
            total_invests += 1
            if_market += market_return
            if_strat += invest_return
            
##
##    print("Accuracy:", (correct_count/test_size) * 100.00)
##
##    print("Total Trades:", total_invests)
##    print("Ending with Strategy:",if_strat)
##    print("Ending with Market:",if_market)

##    compared = ((if_strat - if_market) / if_market) * 100.0
##    do_nothing = total_invests * invest_amount
##
##    avg_market = ((if_market - do_nothing) / do_nothing) * 100.0
##    avg_strat = ((if_strat - do_nothing) / do_nothing) * 100.0


    
##    print("Compared to market, we earn",str(compared)+"% more")
##    print("Average investment return:", str(avg_strat)+"%")
##    print("Average market return:", str(avg_market)+"%")


    data_df = pd.DataFrame.from_csv("forward_sample_WITH_NA.csv")

    data_df = data_df.replace("N/A",0).replace("NaN",0)

    X = np.array(data_df[FEATURES].values)

    X = preprocessing.scale(X)

    Z = data_df["Ticker"].values.tolist()

    invest_list = []

    for i in range(len(X)):
        p = clf.predict(X[i])[0]
        if p == 1:
            print(Z[i])
            invest_list.append(Z[i])

    print(len(invest_list))
    print(invest_list)
    
    
    
    

    

    
Analysis()

The next tutorial:





  • Intro to Machine Learning with Scikit Learn and Python

  • Simple Support Vector Machine (SVM) example with character recognition

  • Our Method and where we will be getting our Data

  • Parsing data

  • More Parsing

  • Structuring data with Pandas

  • Getting more data and meshing data sets

  • Labeling of data part 1

  • Labeling data part 2

  • Finally finishing up the labeling

  • Linear SVC Machine learning SVM example with Python

  • Getting more features from our data

  • Linear SVC machine learning and testing our data

  • Scaling, Normalizing, and machine learning with many features

  • Shuffling our data to solve a learning issue

  • Using Quandl for more data

  • Improving our Analysis with a more accurate measure of performance in relation to fundamentals

  • Learning and Testing our Machine learning algorithm

  • More testing, this time including N/A data

  • Back-testing the strategy

  • Pulling current data from Yahoo

  • Building our New Data-set

  • Searching for investment suggestions

  • Raising investment requirement standards

  • Testing raised standards
  • Streamlining the changing of standards