├── tests ├── __init__.py ├── test_variables.py ├── test_utils.py └── test_datasets.py ├── requirements.txt ├── LICENSE.txt ├── stock_prediction.py ├── download_historical_prices.py ├── utils.py ├── backtesting.py ├── current_data.py ├── parsing_keystats.py ├── README.md └── forward_sample.csv /tests/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | requests>=2.20.0 2 | tqdm==4.19.5 3 | pytest==3.4.1 4 | fix_yahoo_finance==0.0.21 5 | pandas_datareader==0.5.0 6 | numpy==1.12.1 7 | pandas==0.22.0 8 | scikit_learn==0.19.1 9 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Robert Andrew Martin 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /tests/test_variables.py: -------------------------------------------------------------------------------- 1 | import os 2 | import parsing_keystats 3 | import current_data 4 | import stock_prediction 5 | 6 | 7 | def test_statspath(): 8 | # Check that the statspath exists and is a directory 9 | assert os.path.exists(current_data.statspath) 10 | assert os.path.isdir(current_data.statspath) 11 | 12 | assert parsing_keystats.statspath == current_data.statspath 13 | 14 | 15 | def test_features_same(): 16 | # There are only four differences (intentionally) 17 | assert set(parsing_keystats.features) - set(current_data.features) == {'Net Income Avl to Common', 'Qtrly Earnings Growth', 18 | 'Qtrly Revenue Growth', 'Shares Short (as of', 19 | 'Shares Short (prior month)'} 20 | assert set(current_data.features) - set(parsing_keystats.features) == {'Net Income Avi to Common', 'Quarterly Earnings Growth', 21 | 'Shares Short', 'Quarterly Revenue Growth', 22 | 'Shares Short (prior month'} 23 | 24 | 25 | def test_outperformance(): 26 | assert stock_prediction.OUTPERFORMANCE >= 0 27 | -------------------------------------------------------------------------------- /tests/test_utils.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import utils 3 | 4 | 5 | def test_status_calc(): 6 | """ 7 | Test the status_calc function which generates training labels 8 | """ 9 | assert utils.status_calc(50, 20, 12.2) == 1 10 | assert utils.status_calc(12.003, 10, 15) == 0 11 | assert utils.status_calc(-10, -30, 5) == 1 12 | assert utils.status_calc(-31, -30, 15) == 0 13 | assert utils.status_calc(15, 5, 10) == 1 14 | 15 | with pytest.raises(ValueError): 16 | utils.status_calc(12, 10, -3) 17 | 18 | 19 | def test_data_string_to_float(): 20 | """ 21 | data_string_to_float() is a function that needs to meet lots of empirical requirements 22 | owing to the idiosyncrasies of Yahoo Finance's HTML. The main jobs are parsing negatives and 23 | abbreviations of big numbers. 24 | """ 25 | assert utils.data_string_to_float("asdfNaN") == "N/A" 26 | assert utils.data_string_to_float(">N/A\n0") == 0 28 | assert utils.data_string_to_float("-3") == -3 29 | assert utils.data_string_to_float("4K") == 4000 30 | assert utils.data_string_to_float("2M") == 2000000 31 | assert utils.data_string_to_float("0.07B") == 70000000 32 | assert utils.data_string_to_float("-100.1K") == -100100 33 | assert utils.data_string_to_float("-0.1M") == -100000 34 | assert utils.data_string_to_float("-0.02B") == -20000000 35 | assert utils.data_string_to_float("-0.00") == 0 36 | assert utils.data_string_to_float("0.00") == 0 37 | assert utils.data_string_to_float("0M") == 0 38 | assert utils.data_string_to_float("010K") == 10000 39 | 40 | with pytest.raises(ValueError): 41 | utils.data_string_to_float(">0x") 42 | with pytest.raises(ValueError): 43 | utils.data_string_to_float("10k") 44 | with pytest.raises(ValueError): 45 | utils.data_string_to_float("2KB") 46 | -------------------------------------------------------------------------------- /stock_prediction.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from sklearn.ensemble import RandomForestClassifier 3 | from utils import data_string_to_float, status_calc 4 | 5 | 6 | # The percentage by which a stock has to beat the S&P500 to be considered a 'buy' 7 | OUTPERFORMANCE = 10 8 | 9 | 10 | def build_data_set(): 11 | """ 12 | Reads the keystats.csv file and prepares it for scikit-learn 13 | :return: X_train and y_train numpy arrays 14 | """ 15 | training_data = pd.read_csv("keystats.csv", index_col="Date") 16 | training_data.dropna(axis=0, how="any", inplace=True) 17 | features = training_data.columns[6:] 18 | 19 | X_train = training_data[features].values 20 | # Generate the labels: '1' if a stock beats the S&P500 by more than 10%, else '0'. 21 | y_train = list( 22 | status_calc( 23 | training_data["stock_p_change"], 24 | training_data["SP500_p_change"], 25 | OUTPERFORMANCE, 26 | ) 27 | ) 28 | 29 | return X_train, y_train 30 | 31 | 32 | def predict_stocks(): 33 | X_train, y_train = build_data_set() 34 | # Remove the random_state parameter to generate actual predictions 35 | clf = RandomForestClassifier(n_estimators=100, random_state=0) 36 | clf.fit(X_train, y_train) 37 | 38 | # Now we get the actual data from which we want to generate predictions. 39 | data = pd.read_csv("forward_sample.csv", index_col="Date") 40 | data.dropna(axis=0, how="any", inplace=True) 41 | features = data.columns[6:] 42 | X_test = data[features].values 43 | z = data["Ticker"].values 44 | 45 | # Get the predicted tickers 46 | y_pred = clf.predict(X_test) 47 | if sum(y_pred) == 0: 48 | print("No stocks predicted!") 49 | else: 50 | invest_list = z[y_pred].tolist() 51 | print( 52 | f"{len(invest_list)} stocks predicted to outperform the S&P500 by more than {OUTPERFORMANCE}%:" 53 | ) 54 | print(" ".join(invest_list)) 55 | return invest_list 56 | 57 | 58 | if __name__ == "__main__": 59 | print("Building dataset and predicting stocks...") 60 | predict_stocks() 61 | -------------------------------------------------------------------------------- /download_historical_prices.py: -------------------------------------------------------------------------------- 1 | import os 2 | from pandas_datareader import data as pdr 3 | import pandas as pd 4 | import fix_yahoo_finance as yf 5 | 6 | yf.pdr_override() 7 | 8 | START_DATE = "2003-08-01" 9 | END_DATE = "2015-01-01" 10 | 11 | 12 | def build_stock_dataset(start=START_DATE, end=END_DATE): 13 | """ 14 | Creates the dataset containing all stock prices 15 | :returns: stock_prices.csv 16 | """ 17 | 18 | statspath = "intraQuarter/_KeyStats/" 19 | ticker_list = os.listdir(statspath) 20 | 21 | # Required on macOS 22 | if ".DS_Store" in ticker_list: 23 | os.remove(f"{statspath}/.DS_Store") 24 | ticker_list.remove(".DS_Store") 25 | 26 | # Get all Adjusted Close prices for all the tickers in our list, 27 | # between START_DATE and END_DATE 28 | all_data = pdr.get_data_yahoo(ticker_list, start, end) 29 | stock_data = all_data["Adj Close"] 30 | 31 | # Remove any columns that hold no data, and print their tickers. 32 | stock_data.dropna(how="all", axis=1, inplace=True) 33 | missing_tickers = [ 34 | ticker for ticker in ticker_list if ticker.upper() not in stock_data.columns 35 | ] 36 | print(f"{len(missing_tickers)} tickers are missing: \n {missing_tickers} ") 37 | # If there are only some missing datapoints, forward fill. 38 | stock_data.ffill(inplace=True) 39 | stock_data.to_csv("stock_prices.csv") 40 | 41 | 42 | def build_sp500_dataset(start=START_DATE, end=END_DATE): 43 | """ 44 | Creates the dataset containing S&P500 prices 45 | :returns: sp500_index.csv 46 | """ 47 | index_data = pdr.get_data_yahoo("SPY", start=START_DATE, end=END_DATE) 48 | index_data.to_csv("sp500_index.csv") 49 | 50 | 51 | def build_dataset_iteratively( 52 | idx_start, idx_end, date_start=START_DATE, date_end=END_DATE 53 | ): 54 | """ 55 | This is an alternative iterative solution to building the stock dataset, which may be necessary if the 56 | tickerlist is too big. 57 | Instead of downloading all at once, we download ticker by ticker and append to a dataframe. 58 | This will download data for tickerlist[idx_start:idx_end], which makes this method suitable 59 | for chunking data. 60 | 61 | :param idx_start: (int) the starting index of the tickerlist 62 | :param idx_end: (int) the end index of the tickerlist 63 | """ 64 | 65 | statspath = "intraQuarter/_KeyStats/" 66 | ticker_list = os.listdir(statspath) 67 | 68 | df = pd.DataFrame() 69 | 70 | for ticker in ticker_list: 71 | ticker = ticker.upper() 72 | 73 | stock_ohlc = pdr.get_data_yahoo(ticker, start=date_start, end=date_end) 74 | if stock_ohlc.empty: 75 | print(f"No data for {ticker}") 76 | continue 77 | adj_close = stock_ohlc["Adj Close"].rename(ticker) 78 | df = pd.concat([df, adj_close], axis=1) 79 | df.to_csv("stock_prices.csv") 80 | 81 | 82 | if __name__ == "__main__": 83 | build_stock_dataset() 84 | build_sp500_dataset() 85 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | def data_string_to_float(number_string): 2 | """ 3 | The result of our regex search is a number stored as a string, but we need a float. 4 | - Some of these strings say things like '25M' instead of 25000000. 5 | - Some have 'N/A' in them. 6 | - Some are negative (have '-' in front of the numbers). 7 | - As an artifact of our regex, some values which were meant to be zero are instead '>0'. 8 | We must process all of these cases accordingly. 9 | :param number_string: the string output of our regex, which needs to be converted to a float. 10 | :return: a float representation of the string, taking into account minus sign, unit, etc. 11 | """ 12 | # Deal with zeroes and the sign 13 | if ("N/A" in number_string) or ("NaN" in number_string): 14 | return "N/A" 15 | elif number_string == ">0": 16 | return 0 17 | elif "B" in number_string: 18 | return float(number_string.replace("B", "")) * 1000000000 19 | elif "M" in number_string: 20 | return float(number_string.replace("M", "")) * 1000000 21 | elif "K" in number_string: 22 | return float(number_string.replace("K", "")) * 1000 23 | else: 24 | return float(number_string) 25 | 26 | 27 | def duplicate_error_check(df): 28 | """ 29 | A common symptom of failed parsing is when there are consecutive duplicate values. This function was used 30 | to find the duplicates and tweak the regex. Any remaining duplicates are probably coincidences. 31 | :param df: the dataframe to be checked 32 | :return: Prints out a list of the rows containing duplicates, as well as the duplicated values. 33 | """ 34 | # Some columns often (correctly) have the same value as other columns. Remove these. 35 | df.drop( 36 | [ 37 | "Unix", 38 | "Price", 39 | "stock_p_change", 40 | "SP500", 41 | "SP500_p_change", 42 | "Float", 43 | "200-Day Moving Average", 44 | "Short Ratio", 45 | "Operating Margin", 46 | ], 47 | axis=1, 48 | inplace=True, 49 | ) 50 | 51 | for i in range(len(df)): 52 | # Check if there are any duplicates. 53 | if pd.Series(df.iloc[i] == df.iloc[i].shift()).any(): 54 | duplicates = set( 55 | [x for x in list(df.iloc[i]) if list(df.iloc[i]).count(x) > 1] 56 | ) 57 | # A duplicate value of zero is quite common. We want other duplicates. 58 | if duplicates != {0}: 59 | print(i, df.iloc[i], duplicates, sep="\n") 60 | 61 | 62 | def status_calc(stock, sp500, outperformance=10): 63 | """A simple function to classify whether a stock outperformed the S&P500 64 | :param stock: stock price 65 | :param sp500: S&P500 price 66 | :param outperformance: stock is classified 1 if stock price > S&P500 price + outperformance 67 | :return: true/false 68 | """ 69 | if outperformance < 0: 70 | raise ValueError("outperformance must be positive") 71 | return stock - sp500 >= outperformance 72 | -------------------------------------------------------------------------------- /backtesting.py: -------------------------------------------------------------------------------- 1 | # Preprocessing 2 | import numpy as np 3 | import pandas as pd 4 | from sklearn.model_selection import train_test_split 5 | from sklearn.ensemble import RandomForestClassifier 6 | from sklearn.metrics import precision_score 7 | from utils import status_calc 8 | 9 | 10 | def backtest(): 11 | """ 12 | A simple backtest, which splits the dataset into a train set and test set, 13 | then fits a Random Forest classifier to the train set. We print the precision and accuracy 14 | of the classifier on the test set, then run a backtest comparing this strategy's performance 15 | to passive investment in the S&P500. 16 | Please note that there is a methodological flaw in this backtest which will give deceptively 17 | good results, so the results here should not encourage you to live trade. 18 | """ 19 | # Build the dataset, and drop any rows with missing values 20 | data_df = pd.read_csv("keystats.csv", index_col="Date") 21 | data_df.dropna(axis=0, how="any", inplace=True) 22 | 23 | features = data_df.columns[6:] 24 | X = data_df[features].values 25 | 26 | # The labels are generated by applying the status_calc to the dataframe. 27 | # '1' if a stock beats the S&P500 by more than x%, else '0'. Here x is the 28 | # outperformance parameter, which is set to 10 by default but can be redefined. 29 | y = list( 30 | status_calc( 31 | data_df["stock_p_change"], data_df["SP500_p_change"], outperformance=10 32 | ) 33 | ) 34 | 35 | # z is required for us to track returns 36 | z = np.array(data_df[["stock_p_change", "SP500_p_change"]]) 37 | 38 | # Generate the train set and test set by randomly splitting the dataset 39 | X_train, X_test, y_train, y_test, z_train, z_test = train_test_split( 40 | X, y, z, test_size=0.2 41 | ) 42 | 43 | # Instantiate a RandomForestClassifier with 100 trees, then fit it to the training data 44 | clf = RandomForestClassifier(n_estimators=100, random_state=0) 45 | clf.fit(X_train, y_train) 46 | 47 | # Generate the predictions, then print test set accuracy and precision 48 | y_pred = clf.predict(X_test) 49 | print("Classifier performance\n", "=" * 20) 50 | print(f"Accuracy score: {clf.score(X_test, y_test): .2f}") 51 | print(f"Precision score: {precision_score(y_test, y_pred): .2f}") 52 | 53 | # Because y_pred is an array of 1s and 0s, the number of positive predictions 54 | # is equal to the sum of the array 55 | num_positive_predictions = sum(y_pred) 56 | if num_positive_predictions < 0: 57 | print("No stocks predicted!") 58 | 59 | # Recall that z_test stores the change in stock price in column 0, and the 60 | # change in S&P500 price in column 1. 61 | # Whenever a stock is predicted to outperform (y_pred = 1), we 'buy' that stock 62 | # and simultaneously `buy` the index for comparison. 63 | stock_returns = 1 + z_test[y_pred, 0] / 100 64 | market_returns = 1 + z_test[y_pred, 1] / 100 65 | 66 | # Calculate the average growth for each stock we predicted 'buy' 67 | # and the corresponding index growth 68 | avg_predicted_stock_growth = sum(stock_returns) / num_positive_predictions 69 | index_growth = sum(market_returns) / num_positive_predictions 70 | percentage_stock_returns = 100 * (avg_predicted_stock_growth - 1) 71 | percentage_market_returns = 100 * (index_growth - 1) 72 | total_outperformance = percentage_stock_returns - percentage_market_returns 73 | 74 | print("\n Stock prediction performance report \n", "=" * 40) 75 | print(f"Total Trades:", num_positive_predictions) 76 | print(f"Average return for stock predictions: {percentage_stock_returns: .1f} %") 77 | print( 78 | f"Average market return in the same period: {percentage_market_returns: .1f}% " 79 | ) 80 | print( 81 | f"Compared to the index, our strategy earns {total_outperformance: .1f} percentage points more" 82 | ) 83 | 84 | 85 | if __name__ == "__main__": 86 | backtest() 87 | -------------------------------------------------------------------------------- /tests/test_datasets.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pandas as pd 3 | 4 | import parsing_keystats 5 | import stock_prediction 6 | import current_data 7 | 8 | 9 | def test_forward_sample_dimensions(): 10 | """ 11 | Check that the forward sample has been built correctly 12 | """ 13 | # Number of features + ['Date', 'Unix', 'Ticker', 'Price', 'stock_p_change', 'SP500', 'SP500_p_change'] 14 | df = pd.read_csv('forward_sample.csv') 15 | indexing_columns = ['Date', 'Unix', 'Ticker', 'Price', 16 | 'stock_p_change', 'SP500', 'SP500_p_change'] 17 | n_cols = len(df.columns) 18 | assert n_cols == len(current_data.features) + len(indexing_columns) 19 | assert len(df) == len(os.listdir('forward/')) 20 | indexing_columns.remove('Ticker') 21 | # Make sure that all of the indexing columns only contain zeroes 22 | assert df[indexing_columns].sum().sum() == 0 23 | 24 | 25 | def test_forward_sample_data(): 26 | """ 27 | Some quick checks on the forward sample data 28 | """ 29 | df = pd.read_csv('forward_sample.csv') 30 | # For these tests we need to fill in nan values with zero 31 | df.fillna(0, inplace=True) 32 | 33 | # Make sure that these features have positive values 34 | positive_features = ['Market Cap', 'Price/Sales', 'Revenue', 'Revenue Per Share', 'Total Cash', 35 | 'Total Cash Per Share', 'Total Debt', '50-Day Moving Average', '200-Day Moving Average', 36 | 'Avg Vol (3 month)', 'Shares Outstanding', 'Float', 37 | '% Held by Insiders', '% Held by Institutions', 'Shares Short', 38 | 'Short Ratio', 'Short % of Float', 'Shares Short (prior month'] 39 | assert all(df[positive_features] >= 0) 40 | 41 | # Make sure that these features have values less than 100 (the above checks that they are +ve) 42 | fractional_features = ['% Held by Insiders', '% Held by Institutions', 43 | 'Short Ratio', 'Short % of Float'] 44 | assert all(df[fractional_features] <= 100) 45 | 46 | 47 | def test_stock_prices_dataset(): 48 | """ 49 | Check that data from pandas-datareader has been downloaded correctly 50 | """ 51 | 52 | df = pd.read_csv("stock_prices.csv", index_col='Date', parse_dates=True) 53 | assert type(df.index) == pd.core.indexes.datetimes.DatetimeIndex 54 | # Make sure that all columns have some price data 55 | assert all(df.isnull().sum() < len(df)) 56 | # After this, we fill in missing values with zero for test purposes 57 | df.fillna(0, inplace=True) 58 | assert all(df >= 0) 59 | 60 | # Index prices 61 | index_df = pd.read_csv( 62 | "sp500_index.csv", index_col='Date', parse_dates=True) 63 | assert type(df.index) == pd.core.indexes.datetimes.DatetimeIndex 64 | assert len(index_df.columns) == 6 65 | assert index_df.shape[0] == df.shape[0] 66 | assert index_df.isnull().sum().sum() == 0 67 | 68 | 69 | def def_keystats_dimensions(): 70 | """ 71 | This tests that the keystats csv has been built correctly 72 | """ 73 | df = pd.read_csv("keystats.csv", index_col='Date') 74 | 75 | indexing_columns = ['Unix', 'Ticker', 'Price', 76 | 'stock_p_change', 'SP500', 'SP500_p_change'] 77 | n_cols = len(df.columns) 78 | assert n_cols == len(parsing_keystats.features) + len(indexing_columns) 79 | 80 | # No missing data in the index columns 81 | assert df[indexing_columns].isnull().sum().sum() == 0 82 | 83 | 84 | def test_stock_prediction_dataset(): 85 | """ 86 | This tests that the dataset on which we are training our algorithm has been correctly built 87 | """ 88 | df = pd.read_csv("keystats.csv", index_col='Date') 89 | num_rows_with_nan = sum(df.isnull().sum(axis=1) > 0) 90 | 91 | X, y = stock_prediction.build_data_set() 92 | assert X.shape[0] == df.shape[0] - num_rows_with_nan 93 | assert len(y) == df.shape[0] - num_rows_with_nan 94 | assert X.shape[1] == len(parsing_keystats.features) 95 | -------------------------------------------------------------------------------- /current_data.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import os 3 | import re 4 | import time 5 | import requests 6 | import numpy as np 7 | from tqdm import tqdm 8 | from utils import data_string_to_float 9 | 10 | # The path to your fundamental data 11 | statspath = "intraQuarter/_KeyStats/" 12 | 13 | # These are the features that will be parsed 14 | features = [ # Valuation measures 15 | "Market Cap", 16 | "Enterprise Value", 17 | "Trailing P/E", 18 | "Forward P/E", 19 | "PEG Ratio", 20 | "Price/Sales", 21 | "Price/Book", 22 | "Enterprise Value/Revenue", 23 | "Enterprise Value/EBITDA", 24 | # Financials 25 | "Profit Margin", 26 | "Operating Margin", 27 | "Return on Assets", 28 | "Return on Equity", 29 | "Revenue", 30 | "Revenue Per Share", 31 | "Quarterly Revenue Growth", 32 | "Gross Profit", 33 | "EBITDA", 34 | "Net Income Avi to Common", 35 | "Diluted EPS", 36 | "Quarterly Earnings Growth", 37 | "Total Cash", 38 | "Total Cash Per Share", 39 | "Total Debt", 40 | "Total Debt/Equity", 41 | "Current Ratio", 42 | "Book Value Per Share", 43 | "Operating Cash Flow", 44 | "Levered Free Cash Flow", 45 | # Trading information 46 | "Beta", 47 | "50-Day Moving Average", 48 | "200-Day Moving Average", 49 | "Avg Vol (3 month)", 50 | "Shares Outstanding", 51 | "Float", 52 | "% Held by Insiders", 53 | "% Held by Institutions", 54 | "Shares Short", 55 | "Short Ratio", 56 | "Short % of Float", 57 | "Shares Short (prior month", 58 | ] 59 | 60 | 61 | def check_yahoo(): 62 | """ 63 | Retrieves the stock ticker from the _KeyStats directory, then downloads the html file from yahoo finance. 64 | :return: a directory named `forward/` filled with the html files for each ticker 65 | """ 66 | # Create the directory where we will store the current data 67 | if not os.path.exists("forward/"): 68 | os.makedirs("forward/") 69 | 70 | # Retrieve a list of tickers from the fundamental data folder 71 | ticker_list = os.listdir(statspath) 72 | 73 | # Required in macOS to remove the hidden index file. 74 | if ".DS_Store" in ticker_list: 75 | ticker_list.remove(".DS_Store") 76 | 77 | for ticker in tqdm(ticker_list, desc="Download progress:", unit="tickers"): 78 | try: 79 | link = f"http://finance.yahoo.com/quote/{ticker.upper()}/key-statistics" 80 | resp = requests.get(link) 81 | 82 | # Write results to forward/ 83 | save = f"forward/{ticker}.html" 84 | with open(save, "w") as file: 85 | file.write(resp.text) 86 | 87 | except Exception as e: 88 | print(f"{ticker}: {str(e)}\n") 89 | time.sleep(2) 90 | 91 | 92 | def forward(): 93 | """ 94 | Creates the forward sample by parsing the current data html files that we downloaded in check_yahoo(). 95 | :return: a pandas dataframe containing all of the current data for each ticker. 96 | """ 97 | # Creating an empty dataframe which we will later fill. In addition to the features, we need some index variables 98 | # (date, unix timestamp, ticker), and of course the dependent variables (prices). 99 | df_columns = [ 100 | "Date", 101 | "Unix", 102 | "Ticker", 103 | "Price", 104 | "stock_p_change", 105 | "SP500", 106 | "SP500_p_change", 107 | ] + features 108 | 109 | df = pd.DataFrame(columns=df_columns) 110 | 111 | tickerfile_list = os.listdir("forward/") 112 | 113 | # Required in macOS to remove the hidden index file. 114 | if ".DS_Store" in tickerfile_list: 115 | tickerfile_list.remove(".DS_Store") 116 | 117 | # This is the actual parsing. This needs to be fixed every time yahoo changes their UI. 118 | for tickerfile in tqdm(tickerfile_list, desc="Parsing progress:", unit="tickers"): 119 | ticker = tickerfile.split(".html")[0].upper() 120 | source = open(f"forward/{tickerfile}").read() 121 | # Remove commas from the html to make parsing easier. 122 | source = source.replace(",", "") 123 | 124 | # Regex search for the different variables in the html file, then append to value_list 125 | value_list = [] 126 | for variable in features: 127 | try: 128 | # Basically, look for the first number present after we an occurence of the variable 129 | regex = ( 130 | r">" 131 | + re.escape(variable) 132 | + r".*?(\-?\d+\.*\d*K?M?B?|N/A[\\n|\s]*|>0|NaN)%?" 133 | r"(|)" 134 | ) 135 | value = re.search(regex, source, flags=re.DOTALL).group(1) 136 | 137 | # Dealing with number formatting 138 | value_list.append(data_string_to_float(value)) 139 | 140 | # The data may not be present. Process accordingly. 141 | except AttributeError: 142 | value_list.append("N/A") 143 | # print(ticker, variable) 144 | 145 | # Append the ticker and the features to the dataframe 146 | new_df_row = [0, 0, ticker, 0, 0, 0, 0] + value_list 147 | 148 | df = df.append(dict(zip(df_columns, new_df_row)), ignore_index=True) 149 | 150 | return df.replace("N/A", np.nan) 151 | 152 | 153 | if __name__ == "__main__": 154 | check_yahoo() 155 | current_df = forward() 156 | current_df.to_csv("forward_sample.csv", index=False) 157 | -------------------------------------------------------------------------------- /parsing_keystats.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import os 3 | import time 4 | import re 5 | from datetime import datetime 6 | from utils import data_string_to_float 7 | from tqdm import tqdm 8 | 9 | 10 | # The directory where individual html files are stored 11 | statspath = "intraQuarter/_KeyStats/" 12 | 13 | # The list of features to parse from the html files 14 | features = [ # Valuation measures 15 | "Market Cap", 16 | "Enterprise Value", 17 | "Trailing P/E", 18 | "Forward P/E", 19 | "PEG Ratio", 20 | "Price/Sales", 21 | "Price/Book", 22 | "Enterprise Value/Revenue", 23 | "Enterprise Value/EBITDA", 24 | # Financial highlights 25 | "Profit Margin", 26 | "Operating Margin", 27 | "Return on Assets", 28 | "Return on Equity", 29 | "Revenue", 30 | "Revenue Per Share", 31 | "Qtrly Revenue Growth", 32 | "Gross Profit", 33 | "EBITDA", 34 | "Net Income Avl to Common", 35 | "Diluted EPS", 36 | "Qtrly Earnings Growth", 37 | "Total Cash", 38 | "Total Cash Per Share", 39 | "Total Debt", 40 | "Total Debt/Equity", 41 | "Current Ratio", 42 | "Book Value Per Share", 43 | "Operating Cash Flow", 44 | "Levered Free Cash Flow", 45 | # Trading information 46 | "Beta", 47 | "50-Day Moving Average", 48 | "200-Day Moving Average", 49 | "Avg Vol (3 month)", 50 | "Shares Outstanding", 51 | "Float", 52 | "% Held by Insiders", 53 | "% Held by Institutions", 54 | "Shares Short (as of", 55 | "Short Ratio", 56 | "Short % of Float", 57 | "Shares Short (prior month", 58 | ] 59 | 60 | 61 | def preprocess_price_data(): 62 | """ 63 | Currently, the sp500 and stock price datasets we downloaded do not have any data for 64 | days when the market was closed (weekends and public holidays). We need to amend this so that 65 | all rows are included. Doing this now saves a lot of effort when we actually create the 66 | keystats dataset, which requires that we have stock data every day. 67 | :return: SP500 and stock dataframes, with no missing rows. 68 | """ 69 | # Read in SP500 data and stock data, parsing the dates. 70 | sp500_raw_data = pd.read_csv("sp500_index.csv", index_col="Date", parse_dates=True) 71 | stock_raw_data = pd.read_csv("stock_prices.csv", index_col="Date", parse_dates=True) 72 | 73 | # We will reindex to include the weekends. 74 | start_date = str(stock_raw_data.index[0]) 75 | end_date = str(stock_raw_data.index[-1]) 76 | idx = pd.date_range(start_date, end_date) 77 | sp500_raw_data = sp500_raw_data.reindex(idx) 78 | stock_raw_data = stock_raw_data.reindex(idx) 79 | 80 | # Now the weekends are NaN, so we fill forward these NaNs 81 | # (i.e weekends take the value of Friday's adjusted close). 82 | sp500_raw_data.ffill(inplace=True) 83 | stock_raw_data.ffill(inplace=True) 84 | 85 | return sp500_raw_data, stock_raw_data 86 | 87 | 88 | def parse_keystats(sp500_df, stock_df): 89 | """ 90 | We have downloaded a large number of html files, which are snapshots of a ticker at different times, 91 | containing the fundamental data (our features). To extract the key statistics, we use regex. 92 | For supervised machine learning, we also need the data that will form our dependent variable, 93 | the performance of the stock compared to the SP500. 94 | :sp500_df: dataframe containing SP500 prices 95 | :stock_df: dataframe containing stock prices 96 | :return: a dataframe of training data (i.e features and the components of our dependent variable) 97 | """ 98 | # The tickers whose data is to be parsed. 99 | stock_list = [x[0] for x in os.walk(statspath)] 100 | stock_list = stock_list[1:] 101 | 102 | # Creating a new dataframe which we will later fill. 103 | df_columns = [ 104 | "Date", 105 | "Unix", 106 | "Ticker", 107 | "Price", 108 | "stock_p_change", 109 | "SP500", 110 | "SP500_p_change", 111 | ] + features 112 | 113 | df = pd.DataFrame(columns=df_columns) 114 | 115 | # tqdm is a simple progress bar 116 | for stock_directory in tqdm(stock_list, desc="Parsing progress:", unit="tickers"): 117 | keystats_html_files = os.listdir(stock_directory) 118 | 119 | # Snippet to get rid of the .DS_Store file in macOS 120 | if ".DS_Store" in keystats_html_files: 121 | keystats_html_files.remove(".DS_Store") 122 | 123 | ticker = stock_directory.split(statspath)[1] 124 | 125 | for file in keystats_html_files: 126 | # Convert the datetime format of our file to unix time 127 | date_stamp = datetime.strptime(file, "%Y%m%d%H%M%S.html") 128 | unix_time = time.mktime(date_stamp.timetuple()) 129 | 130 | # Read in the html file as a string. 131 | full_file_path = stock_directory + "/" + file 132 | 133 | # This will store the parsed values 134 | value_list = [] 135 | 136 | with open(full_file_path, "r") as source: 137 | source = source.read() 138 | # Remove commas from the html to make parsing easier. 139 | source = source.replace(",", "") 140 | 141 | # Regex search for the different variables in the html file, then append to value_list 142 | for variable in features: 143 | # Search for the table entry adjacent to the variable name. 144 | try: 145 | regex = ( 146 | r">" 147 | + re.escape(variable) 148 | + r".*?(\-?\d+\.*\d*K?M?B?|N/A[\\n|\s]*|>0|NaN)%?" 149 | r"(|)" 150 | ) 151 | value = re.search(regex, source, flags=re.DOTALL).group(1) 152 | 153 | # Dealing with number formatting 154 | value_list.append(data_string_to_float(value)) 155 | 156 | # The data may not be present. Process accordingly 157 | except AttributeError: 158 | # In the past, 'Avg Vol' was instead named 'Average Volume' 159 | # If 'Avg Vol' fails, search for 'Average Volume'. 160 | if variable == "Avg Vol (3 month)": 161 | try: 162 | new_variable = ">Average Volume (3 month)" 163 | regex = ( 164 | re.escape(new_variable) 165 | + r".*?(\-?\d+\.*\d*K?M?B?|N/A[\\n|\s]*|>0)%?" 166 | r"(|)" 167 | ) 168 | value = re.search(regex, source, flags=re.DOTALL).group( 169 | 1 170 | ) 171 | value_list.append(data_string_to_float(value)) 172 | except AttributeError: 173 | value_list.append("N/A") 174 | else: 175 | value_list.append("N/A") 176 | 177 | # We need the stock price and SP500 price now and one year from now. 178 | # Convert from unix time to YYYY-MM-DD, so we can look for the price in the dataframe 179 | # then calculate the percentage change. 180 | current_date = datetime.fromtimestamp(unix_time).strftime("%Y-%m-%d") 181 | one_year_later = datetime.fromtimestamp(unix_time + 31536000).strftime( 182 | "%Y-%m-%d" 183 | ) 184 | 185 | # SP500 prices now and one year later, and the percentage change 186 | sp500_price = float(sp500_df.loc[current_date, "Adj Close"]) 187 | sp500_1y_price = float(sp500_df.loc[one_year_later, "Adj Close"]) 188 | sp500_p_change = round( 189 | ((sp500_1y_price - sp500_price) / sp500_price * 100), 2 190 | ) 191 | 192 | # Stock prices now and one year later. We need a try/except because some data is missing 193 | stock_price, stock_1y_price = "N/A", "N/A" 194 | try: 195 | stock_price = float(stock_df.loc[current_date, ticker.upper()]) 196 | stock_1y_price = float(stock_df.loc[one_year_later, ticker.upper()]) 197 | except KeyError: 198 | # If stock data is missing, we must skip this datapoint 199 | # print(f"PRICE RETRIEVAL ERROR for {ticker}") 200 | continue 201 | 202 | stock_p_change = round( 203 | ((stock_1y_price - stock_price) / stock_price * 100), 2 204 | ) 205 | 206 | # Append all our data to the dataframe. 207 | new_df_row = [ 208 | date_stamp, 209 | unix_time, 210 | ticker, 211 | stock_price, 212 | stock_p_change, 213 | sp500_price, 214 | sp500_p_change, 215 | ] + value_list 216 | 217 | df = df.append(dict(zip(df_columns, new_df_row)), ignore_index=True) 218 | 219 | # Remove rows with missing stock price data 220 | df.dropna(axis=0, subset=["Price", "stock_p_change"], inplace=True) 221 | # Output the CSV 222 | df.to_csv("keystats.csv", index=False) 223 | 224 | 225 | if __name__ == "__main__": 226 | sp500_df, stock_df = preprocess_price_data() 227 | parse_keystats(sp500_df, stock_df) 228 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MachineLearningStocks in python: a starter project and guide 2 | 3 | [![forthebadge made-with-python](https://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/) 4 | 5 | [![GitHub license](https://img.shields.io/badge/License-MIT-brightgreen.svg?style=flat-square)](https://github.com/surelyourejoking/MachineLearningStocks/blob/master/LICENSE.txt) (https://github.com/surelyourejoking/MachineLearningStocks/graphs/commit-activity) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) 6 | 7 | MachineLearningStocks is designed to be an **intuitive** and **highly extensible** template project applying machine learning to making stock predictions. My hope is that this project will help you understand the overall workflow of using machine learning to predict stock movements and also appreciate some of its subtleties. And of course, after following this guide and playing around with the project, you should definitely **make your own improvements** – if you're struggling to think of what to do, at the end of this readme I've included a long list of possiblilities: take your pick. 8 | 9 | Concretely, we will be cleaning and preparing a dataset of historical stock prices and fundamentals using `pandas`, after which we will apply a `scikit-learn` classifier to discover the relationship between stock fundamentals (e.g PE ratio, debt/equity, float, etc) and the subsequent annual price change (compared with the an index). We then conduct a simple backtest, before generating predictions on current data. 10 | 11 | While I would not live trade based off of the predictions from this exact code, I do believe that you can use this project as starting point for a profitable trading system – I have actually used code based on this project to live trade, with pretty decent results (around 20% returns on backtest and 10-15% on live trading). 12 | 13 | This project has quite a lot of personal significance for me. It was my first proper python project, one of my first real encounters with ML, and the first time I used git. At the start, my code was rife with bad practice and inefficiency: I have since tried to amend most of this, but please be warned that some minor issues may remain (feel free to raise an issue, or fork and submit a PR). Both the project and myself as a programmer have evolved a lot since the first iteration, but there is always room to improve. 14 | 15 | *As a disclaimer, this is a purely educational project. Be aware that backtested performance may often be deceptive – trade at your own risk!* 16 | 17 | *MachineLearningStocks predicts which stocks will outperform. But it does not suggest how best to combine them into a portfolio. I have just released [PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt), a portfolio optimisation library which uses 18 | classical efficient frontier techniques (with modern improvements) in order to generate risk-efficient portfolios. Generating optimal allocations from the predicted outperformers might be a great way to improve risk-adjusted returns.* 19 | 20 | *This guide has been cross-posted at my academic blog, [reasonabledeviations.com](https://reasonabledeviations.com/)* 21 | 22 | ## Contents 23 | 24 | - [Contents](#contents) 25 | - [Overview](#overview) 26 | - [EDIT as of 24/5/18](#edit-as-of-24518) 27 | - [EDIT as of October 2019](#edit-as-of-october-2019) 28 | - [Quickstart](#quickstart) 29 | - [Preliminaries](#preliminaries) 30 | - [Historical data](#historical-data) 31 | - [Historical stock fundamentals](#historical-stock-fundamentals) 32 | - [Historical price data](#historical-price-data) 33 | - [Creating the training dataset](#creating-the-training-dataset) 34 | - [Preprocessing historical price data](#preprocessing-historical-price-data) 35 | - [Features](#features) 36 | - [Valuation measures](#valuation-measures) 37 | - [Financials](#financials) 38 | - [Trading information](#trading-information) 39 | - [Parsing](#parsing) 40 | - [Backtesting](#backtesting) 41 | - [Current fundamental data](#current-fundamental-data) 42 | - [Stock prediction](#stock-prediction) 43 | - [Unit testing](#unit-testing) 44 | - [Where to go from here](#where-to-go-from-here) 45 | - [Data acquisition](#data-acquisition) 46 | - [Data preprocessing](#data-preprocessing) 47 | - [Machine learning](#machine-learning) 48 | - [Contributing](#contributing) 49 | 50 | ## Overview 51 | 52 | The overall workflow to use machine learning to make stocks prediction is as follows: 53 | 54 | 1. Acquire historical fundamental data – these are the *features* or *predictors* 55 | 2. Acquire historical stock price data – this is will make up the dependent variable, or label (what we are trying to predict). 56 | 3. Preprocess data 57 | 4. Use a machine learning model to learn from the data 58 | 5. Backtest the performance of the machine learning model 59 | 6. Acquire current fundamental data 60 | 7. Generate predictions from current fundamental data 61 | 62 | This is a very generalised overview, but in principle this is all you need to build a fundamentals-based ML stock predictor. 63 | 64 | ### EDIT as of 24/5/18 65 | 66 | This project uses pandas-datareader to download historical price data from Yahoo Finance. However, in the past few weeks this has become extremely inconsistent – it seems like Yahoo have added some measures to prevent the bulk download of their data. I will try to add a fix, but for now, take note that `download_historical_prices.py` may be deprecated. 67 | 68 | As a temporary solution, I've uploaded `stock_prices.csv` and `sp500_index.csv`, so the rest of the project can still function. 69 | 70 | ### EDIT as of October 2019 71 | 72 | I expect that after so much time there will be many data issues. To that end, I have decided to upload the other CSV files: `keystats.csv` (the output of `parsing_keystats.py`) and `forward_sample.csv` (the output of `current_data.py`). 73 | 74 | ## Quickstart 75 | 76 | If you want to throw away the instruction manual and play immediately, clone this project, then download and unzip the [data file](https://pythonprogramming.net/data-acquisition-machine-learning/) into the same directory. Then, open an instance of terminal and cd to the project's file path, e.g 77 | 78 | ```bash 79 | cd Users/User/Desktop/MachineLearningStocks 80 | ``` 81 | 82 | Then, run the following in terminal: 83 | 84 | ```bash 85 | pip install -r requirements.txt 86 | python download_historical_prices.py 87 | python parsing_keystats.py 88 | python backtesting.py 89 | python current_data.py 90 | pytest -v 91 | python stock_prediction.py 92 | ``` 93 | 94 | Otherwise, follow the step-by-step guide below. 95 | 96 | ## Preliminaries 97 | 98 | This project uses python 3.6, and the common data science libraries `pandas` and `scikit-learn`. If you are on python 3.x less than 3.6, you will find some syntax errors wherever f-strings have been used for string formatting. These are fortunately very easy to fix (just rebuild the string using your preferred method), but I do encourage you to upgrade to 3.6 to enjoy the elegance of f-strings. A full list of requirements is included in the `requirements.txt` file. To install all of the requirements at once, run the following code in terminal: 99 | 100 | ```bash 101 | pip install -r requirements.txt 102 | ``` 103 | 104 | To get started, clone this project and unzip it. This folder will become our working directory, so make sure you `cd` your terminal instance into this directory. 105 | 106 | ## Historical data 107 | 108 | Data acquisition and preprocessing is probably the hardest part of most machine learning projects. But it is a necessary evil, so it's best to not fret and just carry on. 109 | 110 | For this project, we need three datasets: 111 | 112 | 1. Historical stock fundamentals 113 | 2. Historical stock prices 114 | 3. Historical S&P500 prices 115 | 116 | We need the S&P500 index prices as a benchmark: a 5% stock growth does not mean much if the S&P500 grew 10% in that time period, so all stock returns must be compared to those of the index. 117 | 118 | ### Historical stock fundamentals 119 | 120 | Historical fundamental data is actually very difficult to find (for free, at least). Although sites like [Quandl](https://www.quandl.com/) do have datasets available, you often have to pay a pretty steep fee. 121 | 122 | It turns out that there is a way to parse this data, for free, from [Yahoo Finance](https://finance.yahoo.com/). I will not go into details, because [Sentdex has done it for us](https://pythonprogramming.net/data-acquisition-machine-learning/). On his page you will be able to find a file called `intraQuarter.zip`, which you should download, unzip, and place in your working directory. Relevant to this project is the subfolder called `_KeyStats`, which contains html files that hold stock fundamentals for all stocks in the S&P500 between 2003 and 2013, sorted by stock. However, at this stage, the data is unusable – we will have to parse it into a nice csv file before we can do any ML. 123 | 124 | ### Historical price data 125 | 126 | In the first iteration of the project, I used `pandas-datareader`, an extremely convenient library which can load stock data straight into `pandas`. However, after Yahoo Finance changed their UI, `datareader` no longer worked, so I switched to [Quandl](https://www.quandl.com/), which has free stock price data for a few tickers, and a python API. However, as `pandas-datareader` has been [fixed](https://github.com/ranaroussi/fix-yahoo-finance), we will use that instead. 127 | 128 | Likewise, we can easily use `pandas-datareader` to access data for the SPY ticker. Failing that, one could manually download it from [yahoo finance](https://finance.yahoo.com/quote/%5EGSPC/history?p=%5EGSPC), place it into the project directory and rename it `sp500_index.csv`. 129 | 130 | The code for downloading historical price data can be run by entering the following into terminal: 131 | 132 | ```bash 133 | python download_historical_prices.py 134 | ``` 135 | 136 | ## Creating the training dataset 137 | 138 | Our ultimate goal for the training data is to have a 'snapshot' of a particular stock's fundamentals at a particular time, and the corresponding subsequent annual performance of the stock. 139 | 140 | For example, if our 'snapshot' consists of all of the fundamental data for AAPL on the date 28/1/2005, then we also need to know the percentage price change of AAPL between 28/1/05 and 28/1/06. Thus our algorithm can learn how the fundamentals impact the annual change in the stock price. 141 | 142 | In fact, this is a slight oversimplification. In fact, what the algorithm will eventually learn is how fundamentals impact the *outperformance of a stock relative to the S&P500 index*. This is why we also need index data. 143 | 144 | ### Preprocessing historical price data 145 | 146 | When `pandas-datareader` downloads stock price data, it does not include rows for weekends and public holidays (when the market is closed). 147 | 148 | However, referring to the example of AAPL above, if our snapshot includes fundamental data for 28/1/05 and we want to see the change in price a year later, we will get the nasty surprise that 28/1/2006 is a Saturday. Does this mean that we have to discard this snapshot? 149 | 150 | By no means – data is too valuable to callously toss away. As a workaround, I instead decided to 'fill forward' the missing data, i.e we will assume that the stock price on Saturday 28/1/2006 is equal to the stock price on Friday 27/1/2006. 151 | 152 | ### Features 153 | 154 | Below is a list of some of the interesting variables that are available on Yahoo Finance. 155 | 156 | #### Valuation measures 157 | 158 | - 'Market Cap' 159 | - Enterprise Value 160 | - Trailing P/E 161 | - Forward P/E 162 | - PEG Ratio 163 | - Price/Sales 164 | - Price/Book 165 | - Enterprise Value/Revenue 166 | - Enterprise Value/EBITDA 167 | 168 | #### Financials 169 | 170 | - Profit Margin 171 | - Operating Margin 172 | - Return on Assets 173 | - Return on Equity 174 | - Revenue 175 | - Revenue Per Share 176 | - Quarterly Revenue Growth 177 | - Gross Profit 178 | - EBITDA 179 | - Net Income Avi to Common 180 | - Diluted EPS 181 | - Quarterly Earnings Growth 182 | - Total Cash 183 | - Total Cash Per Share 184 | - Total Debt 185 | - Total Debt/Equity 186 | - Current Ratio 187 | - Book Value Per Share 188 | - Operating Cash Flow 189 | - Levered Free Cash Flow 190 | 191 | #### Trading information 192 | 193 | - Beta 194 | - 50-Day Moving Average 195 | - 200-Day Moving Average 196 | - Avg Vol (3 month) 197 | - Shares Outstanding 198 | - Float 199 | - % Held by Insiders 200 | - % Held by Institutions 201 | - Shares Short 202 | - Short Ratio 203 | - Short % of Float 204 | - Shares Short (prior month) 205 | 206 | ### Parsing 207 | 208 | However, all of this data is locked up in HTML files. Thus, we need to build a parser. In this project, I did the parsing with regex, but please note that generally it is [really not recommended](https://stackoverflow.com/questions/1732348/regex-match-open-tags-except-xhtml-self-contained-tags) to use regex to parse HTML. However, I think regex probably wins out for ease of understanding (this project being educational in nature), and from experience regex works fine in this case. 209 | 210 | This is the exact regex used: 211 | 212 | ```python 213 | r'>' + re.escape(variable) + r'.*?(\-?\d+\.*\d*K?M?B?|N/A[\\n|\s]*|>0|NaN)%?(|)' 214 | ``` 215 | 216 | While it looks pretty arcane, all it is doing is searching for the first occurence of the feature (e.g "Market Cap"), then it looks forward until it finds a number immediately followed by a `` or `` (signifying the end of a table entry). The complexity of the expression above accounts for some subtleties in the parsing: 217 | 218 | - the numbers could be preceeded by a minus sign 219 | - Yahoo Finance sometimes uses K, M, and B as abbreviations for thousand, million and billion respectively. 220 | - some data are given as percentages 221 | - some datapoints are missing, so instead of a number we have to look for "N/A" or "NaN. 222 | 223 | Both the preprocessing of price data and the parsing of keystats are included in `parsing_keystats.py`. Run the following in your terminal: 224 | 225 | ```bash 226 | python parsing_keystats.py 227 | ``` 228 | 229 | You should see the file `keystats.csv` appear in your working directory. Now that we have the training data ready, we are ready to actually do some machine learning. 230 | 231 | ## Backtesting 232 | 233 | Backtesting is arguably the most important part of any quantitative strategy: you must have some way of testing the performance of your algorithm before you live trade it. 234 | 235 | Despite its importance, I originally did not want to include backtesting code in this repository. The reasons were as follows: 236 | 237 | - Backtesting is messy and empirical. The code is not very pleasant to use, and in practice requires a lot of manual interaction. 238 | - Backtesting is very difficult to get right, and if you do it wrong, you will be deceiving yourself with high returns. 239 | - Developing and working with your backtest is probably the best way to learn about machine learning and stocks – you'll see what works, what doesn't, and what you don't understand. 240 | 241 | Nevertheless, because of the importance of backtesting, I decided that I can't really call this a 'template machine learning stocks project' without backtesting. Thus, I have included a simplistic backtesting script. Please note that there is a fatal flaw with this backtesting implementation that will result in *much* higher backtesting returns. It is quite a subtle point, but I will let you figure that out :) 242 | 243 | Run the following in terminal: 244 | 245 | ```bash 246 | python backtesting.py 247 | ``` 248 | 249 | You should get something like this: 250 | 251 | ```txt 252 | Classifier performance 253 | ====================== 254 | Accuracy score: 0.81 255 | Precision score: 0.75 256 | 257 | Stock prediction performance report 258 | =================================== 259 | Total Trades: 177 260 | Average return for stock predictions: 37.8 % 261 | Average market return in the same period: 9.2% 262 | Compared to the index, our strategy earns 28.6 percentage points more 263 | ``` 264 | 265 | Again, the performance looks too good to be true and almost certainly is. 266 | 267 | ## Current fundamental data 268 | 269 | Now that we have trained and backtested a model on our data, we would like to generate actual predictions on current data. 270 | 271 | As always, we can scrape the data from good old Yahoo Finance. My method is to literally just download the statistics page for each stock (here is the [page](https://finance.yahoo.com/quote/AAPL/key-statistics?p=AAPL) for Apple), then to parse it using regex as before. 272 | 273 | In fact, the regex should be almost identical, but because Yahoo has changed their UI a couple of times, there are some minor differences. This part of the projet has to be fixed whenever yahoo finance changes their UI, so if you can't get the project to work, the problem is most likely here. 274 | 275 | Run the following in terminal: 276 | 277 | ```bash 278 | python current_data.py 279 | ``` 280 | 281 | The script will then begin downloading the HTML into the `forward/` folder within your working directory, before parsing this data and outputting the file `forward_sample.csv`. You might see a few miscellaneous errors for certain tickers (e.g 'Exceeded 30 redirects.'), but this is to be expected. 282 | 283 | ## Stock prediction 284 | 285 | Now that we have the training data and the current data, we can finally generate actual predictions. This part of the project is very simple: the only thing you have to decide is the value of the `OUTPERFORMANCE` parameter (the percentage by which a stock has to beat the S&P500 to be considered a 'buy'). I have set it to 10 by default, but it can easily be modified by changing the variable at the top of the file. Go ahead and run the script: 286 | 287 | ```bash 288 | python stock_prediction.py 289 | ``` 290 | 291 | You should get something like this: 292 | 293 | ```txt 294 | 21 stocks predicted to outperform the S&P500 by more than 10%: 295 | NOC FL SWK NFX LH NSC SCHL KSU DDS GWW AIZ ORLY R SFLY SHW GME DLX DIS AMP BBBY APD 296 | ``` 297 | 298 | ## Unit testing 299 | 300 | I have included a number of unit tests (in the `tests/` folder) which serve to check that things are working properly. However, due to the nature of the some of this projects functionality (downloading big datasets), you will have to run all the code once before running the tests. Otherwise, the tests themselves would have to download huge datasets (which I don't think is optimal). 301 | 302 | I thus recommend that you run the tests after you have run all the other scripts (except, perhaps, `stock_prediction.py`). 303 | 304 | To run the tests, simply enter the following into a terminal instance in the project directory: 305 | 306 | ```bash 307 | pytest -v 308 | ``` 309 | 310 | Please note that it is not considered best practice to include an `__init__.py` file in the `tests/` directory (see [here](https://docs.pytest.org/en/latest/goodpractices.html) for more), but I have done it anyway because it is uncomplicated and functional. 311 | 312 | ## Where to go from here 313 | 314 | I have stated that this project is extensible, so here are some ideas to get you started and possibly increase returns (no promises). 315 | 316 | ### Data acquisition 317 | 318 | My personal belief is that better quality data is THE factor that will ultimately determine your performance. Here are some ideas: 319 | 320 | - Explore the other subfolders in Sentdex's `intraQuarter.zip`. 321 | - Parse the annual reports that all companies submit to the SEC (have a look at the [Edgar Database](https://www.sec.gov/edgar/searchedgar/companysearch.html)) 322 | - Try to find websites from which you can scrape fundamental data (this has been my solution). 323 | - Ditch US stocks and go global – perhaps better results may be found in markets that are less-liquid. It'd be interesting to see whether the predictive power of features vary based on geography. 324 | - Buy Quandl data, or experiment with alternative data. 325 | 326 | ### Data preprocessing 327 | 328 | - Build a more robust parser using BeautifulSoup 329 | - In this project, I have just ignored any rows with missing data, but this reduces the size of the dataset considerably. Are there any ways you can fill in some of this data? 330 | - hint: if the PE ratio is missing but you know the stock price and the earnings/share... 331 | - hint 2: how different is Apple's book value in March to its book value in June? 332 | - Some form of feature engineering 333 | - e.g, calculate [Graham's number](https://www.investopedia.com/terms/g/graham-number.asp) and use it as a feature 334 | - some of the features are probably redundant. Why not remove them to speed up training? 335 | - Speed up the construction of `keystats.csv`. 336 | - hint: don't keep appending to one growing dataframe! Split it into chunks 337 | 338 | ### Machine learning 339 | 340 | Altering the machine learning stuff is probably the easiest and most fun to do. 341 | 342 | - The most important thing if you're serious about results is to find the problem with the current backtesting setup and fix it. This will likely be quite a sobering experience, but if your backtest is done right, it should mean that any observed outperformance on your test set can be traded on (again, do so at your own discretion). 343 | - Try a different classifier – there is plenty of research that advocates the use of SVMs, for example. Don't forget that other classifiers may require feature scaling etc. 344 | - Hyperparameter tuning: use gridsearch to find the optimal hyperparameters for your classifier. But make sure you don't overfit! 345 | - Make it *deep* – experiment with neural networks (an easy way to start is with `sklearn.neural_network`). 346 | - Change the classification problem into a regression one: will we achieve better results if we try to predict the stock *return* rather than whether it outperformed? 347 | - Run the prediction multiple times (perhaps using different hyperparameters?) and select the *k* most common stocks to invest in. This is especially important if the algorithm is not deterministic (as is the case for Random Forest) 348 | - Experiment with different values of the `outperformance` parameter. 349 | - Should we really be trying to predict raw returns? What happens if a stock achieves a 20% return but does so by being highly volatile? 350 | - Try to plot the importance of different features to 'see what the machine sees'. 351 | 352 | ## Contributing 353 | 354 | Feel free to fork, play around, and submit PRs. I would be very grateful for any bug fixes or more unit tests. 355 | 356 | This project was originally based on Sentdex's excellent [machine learning tutorial](https://www.youtube.com/playlist?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3), but it has since evolved far beyond that and the code is almost completely different. The complete series is also on [his website](https://pythonprogramming.net/machine-learning-python-sklearn-intro/). 357 | 358 | --- 359 | 360 | For more content like this, check out my academic blog at [reasonabledeviations.com/](https://reasonabledeviations.com). 361 | -------------------------------------------------------------------------------- /forward_sample.csv: -------------------------------------------------------------------------------- 1 | Date,Unix,Ticker,Price,stock_p_change,SP500,SP500_p_change,Market Cap,Enterprise Value,Trailing P/E,Forward P/E,PEG Ratio,Price/Sales,Price/Book,Enterprise Value/Revenue,Enterprise Value/EBITDA,Profit Margin,Operating Margin,Return on Assets,Return on Equity,Revenue,Revenue Per Share,Quarterly Revenue Growth,Gross Profit,EBITDA,Net Income Avi to Common,Diluted EPS,Quarterly Earnings Growth,Total Cash,Total Cash Per Share,Total Debt,Total Debt/Equity,Current Ratio,Book Value Per Share,Operating Cash Flow,Levered Free Cash 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