├── .idea ├── MarketLearning.iml ├── dictionaries │ └── davidzhang.xml ├── inspectionProfiles │ └── Project_Default.xml ├── misc.xml ├── modules.xml ├── vcs.xml └── workspace.xml ├── LICENSE.md ├── README.md ├── cache └── .placeholder ├── input ├── complete.txt ├── custom.txt ├── indices.txt ├── single.txt ├── test.csv └── train.csv ├── logs └── .placeholder └── src ├── __pycache__ ├── data.cpython-36.pyc ├── stock.cpython-36.pyc └── visualize.cpython-36.pyc ├── data.py ├── gen.py ├── mean_reversion ├── __pycache__ │ ├── api.cpython-36.pyc │ └── backtest.cpython-36.pyc ├── api.py └── backtest.py ├── purchase.py ├── stock.py ├── train.py ├── trend_following ├── __pycache__ │ └── backtest.cpython-36.pyc ├── api.py └── backtest.py └── visualize.py /.idea/MarketLearning.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 13 | -------------------------------------------------------------------------------- /.idea/dictionaries/davidzhang.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | backtest 5 | backtested 6 | bollinger 7 | buyables 8 | dataframes 9 | ichart 10 | recommender 11 | todos 12 | 13 | 14 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 12 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /.idea/workspace.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 59 | 60 | 61 | 62 | chtr 63 | moneys 64 | ebay 65 | 66 | 67 | money 68 | 69 | 70 | 71 | 73 | 74 | 114 | 115 | 116 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 144 | 145 | 148 | 149 | 150 | 151 | 154 | 155 | 158 | 159 | 162 | 163 | 164 | 165 | 166 | 167 | 170 | 171 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 184 | 185 | 188 | 189 | 190 | 191 | 194 | 195 | 198 | 199 | 202 | 203 | 204 | 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Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # AlgoBot 2 | 3 | AlgoBot is an algorithmic trading robot written in Python. It currently analyzes patterns in the stock market to automate or suggest trades, using strategies such as mean reversion or trend following. 4 | 5 | **Disclaimer:** This program is not a platform for guaranteed success. AlgoBot is not responsible for any financial losses that may occur. 6 | 7 | 8 | 9 | ## Installation and Usage 10 | 11 | **Requirements:** 12 | 13 | - Python 3.5+ 14 | - Numpy 15 | - Pandas 16 | - Matplotlib 17 | - Flask (API only) 18 | - Flask-RESTful (API only) 19 | 20 | In addition to the dependencies listed above, you will need an internet connection to receive data from Google Finance, unless the most recent data is already cached offline in your computer. 21 | 22 | **Usage:** 23 | 24 | The backtest.py executables in both the mean reversion and trend following strategies test the respective algorithm on historical data. The user can specify the amount of historical days used by modifying the constant **time_span** found in the source code. In mean reversion, the user can also specify the Bollinger band constant, which is represented by the constant **k**. However, the default Bollinger band constant of 1.20 is recommended. Modifying this constant in the trend following algorithm will have no effect. 25 | 26 | Make sure to run all Python executables from the project's **root** directory! 27 | 28 | **API:** 29 | 30 | The API is designed for optimal use through PyCharm and under an NGROK port tunnel. You can also host it locally by running the api.py file under either of the src directories. 31 | 32 | ## License 33 | 34 | AlgoBot - An algorithmic trading bot written in Python 35 | 36 | This program is free software: you can redistribute it and/or modify 37 | it under the terms of the GNU General Public License as published by 38 | the Free Software Foundation, either version 3 of the License, or 39 | (at your option) any later version. 40 | 41 | This program is distributed in the hope that it will be useful, 42 | but WITHOUT ANY WARRANTY; without even the implied warranty of 43 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 44 | GNU General Public License for more details. 45 | 46 | You should have received a copy of the GNU General Public License 47 | along with this program. If not, see . 48 | 49 | For more information, see [LICENSE.md](https://github.com/Davarco/AlgoBot/blob/master/LICENSE.md). 50 | -------------------------------------------------------------------------------- /cache/.placeholder: -------------------------------------------------------------------------------- 1 | This makes sure that the cache stays alive. -------------------------------------------------------------------------------- /input/complete.txt: -------------------------------------------------------------------------------- 1 | ATVI 2 | ADBE 3 | AKAM 4 | ALXN 5 | GOOG 6 | GOOGL 7 | AMZN 8 | AAL 9 | AMGN 10 | ADI 11 | AAPL 12 | AMAT 13 | ADSK 14 | ADP 15 | BIDU 16 | BIIB 17 | BMRN 18 | AVGO 19 | CA 20 | CELG 21 | CERN 22 | CHKP 23 | CTAS 24 | CSCO 25 | CTXS 26 | CTSH 27 | CMCSA 28 | COST 29 | CSX 30 | CTRP 31 | XRAY 32 | DISCA 33 | DISCK 34 | DISH 35 | DLTR 36 | EBAY 37 | EA 38 | EXPE 39 | ESRX 40 | FB 41 | FAST 42 | FISV 43 | GILD 44 | HAS 45 | HSIC 46 | HOLX 47 | IDXX 48 | ILMN 49 | INCY 50 | INTC 51 | INTU 52 | ISRG 53 | JBHT 54 | JD 55 | KLAC 56 | LRCX 57 | LBTYA 58 | LBTYK 59 | LILA 60 | LILAK 61 | QVCA 62 | MAR 63 | MAT 64 | MXIM 65 | MCHP 66 | MU 67 | MSFT 68 | MDLZ 69 | MNST 70 | MYL 71 | NTES 72 | NFLX 73 | NCLH 74 | NVDA 75 | ORLY 76 | PCAR 77 | PAYX 78 | PYPL 79 | QCOM 80 | REGN 81 | ROST 82 | STX 83 | SHPG 84 | SIRI 85 | SWKS 86 | SBUX 87 | SYMC 88 | TMUS 89 | TSLA 90 | TXN 91 | KHC 92 | PCLN 93 | TSCO 94 | FOX 95 | FOXA 96 | ULTA 97 | VRSK 98 | VRTX 99 | VIAB 100 | VOD 101 | WBA 102 | WDC 103 | WYNN 104 | XLNX 105 | YHOO 106 | -------------------------------------------------------------------------------- /input/custom.txt: -------------------------------------------------------------------------------- 1 | AAPL 2 | MSFT 3 | GOOG 4 | CSCO 5 | ORCL 6 | INTC 7 | QCOM 8 | AMZN 9 | EBAY 10 | COST 11 | NVDA 12 | SPLS 13 | UAL 14 | -------------------------------------------------------------------------------- /input/indices.txt: -------------------------------------------------------------------------------- 1 | INDEXNASDAQ -------------------------------------------------------------------------------- /input/single.txt: -------------------------------------------------------------------------------- 1 | VRTX -------------------------------------------------------------------------------- /logs/.placeholder: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Davarco/AlgoBot/2ad8ce28563186806c43f918614cc57c799391a1/logs/.placeholder -------------------------------------------------------------------------------- /src/__pycache__/data.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Davarco/AlgoBot/2ad8ce28563186806c43f918614cc57c799391a1/src/__pycache__/data.cpython-36.pyc -------------------------------------------------------------------------------- /src/__pycache__/stock.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Davarco/AlgoBot/2ad8ce28563186806c43f918614cc57c799391a1/src/__pycache__/stock.cpython-36.pyc -------------------------------------------------------------------------------- /src/__pycache__/visualize.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Davarco/AlgoBot/2ad8ce28563186806c43f918614cc57c799391a1/src/__pycache__/visualize.cpython-36.pyc -------------------------------------------------------------------------------- /src/data.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import pandas 3 | import time 4 | import io 5 | import os 6 | 7 | 8 | # Returns a single piece of data (pandas dataframes) 9 | def retrieve_single(ticker): 10 | 11 | # Constants for downloading data 12 | cache = "cache" 13 | start_month = "Jan" 14 | start_day = "01" 15 | start_year = "2010" 16 | title_date = start_month + "-" + start_day + "-" + start_year 17 | 18 | # Get the stock data from google finance 19 | print("Getting stock ticker list...") 20 | ticker = ticker.strip() 21 | path = cache + "/" + ticker + "_" + title_date + "_" + time.strftime("%d-%m-%Y") + ".csv" 22 | if os.path.exists(path): 23 | data = pandas.read_csv(path) 24 | else: 25 | url = "https://www.google.com/finance/historical?q=NASDAQ:" + ticker + \ 26 | "&startdate=" + start_month + "+" + start_day + "%2C+" + start_year + "&output=csv" 27 | raw_data = requests.get(url).content 28 | with open(path, 'wb') as f: 29 | f.write(raw_data) 30 | data = pandas.read_csv(io.StringIO(raw_data.decode('utf-8'))) 31 | 32 | return data 33 | 34 | 35 | # Returns list of data (pandas dataframes) 36 | def retrieve_list(path): 37 | 38 | # Constants for downloading data 39 | cache = "cache" 40 | start_month = "Jan" 41 | start_day = "01" 42 | start_year = "2010" 43 | title_date = start_month + "-" + start_day + "-" + start_year 44 | 45 | # List of companies 46 | company_data_list = {} 47 | company_list = open(path) 48 | print("Getting stock ticker list...") 49 | 50 | for name in company_list: 51 | 52 | # Get data from google finance url 53 | name = name.strip() 54 | path = cache + "/" + name + "_" + title_date + "_" + time.strftime("%d-%m-%Y") + ".csv" 55 | if os.path.exists(path): 56 | data = pandas.read_csv(path) 57 | else: 58 | url = "https://www.google.com/finance/historical?q=NASDAQ:" + name + \ 59 | "&startdate=" + start_month + "+" + start_day + "%2C+" + start_year + "&output=csv" 60 | raw_data = requests.get(url).content 61 | with open(path, 'wb') as f: 62 | f.write(raw_data) 63 | data = pandas.read_csv(io.StringIO(raw_data.decode('utf-8'))) 64 | 65 | # Create dictionary pair with the data 66 | company_data_list[name] = data 67 | 68 | return company_data_list 69 | 70 | -------------------------------------------------------------------------------- /src/gen.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import pandas 3 | import numpy 4 | import time 5 | import io 6 | import os 7 | 8 | 9 | # Generate data for training 10 | def gen(): 11 | 12 | # Constants for downloading data 13 | cache = "cache" 14 | start_month = "Jan" 15 | start_day = "01" 16 | start_year = "2010" 17 | future_interval = 100 18 | percent_test = 0.3 19 | title_date = start_month + "-" + start_day + "-" + start_year 20 | train_path = "input/train.csv" 21 | test_path = "input/test.csv" 22 | train_csv = open(train_path, "w+") 23 | test_csv = open(test_path, "w+") 24 | 25 | # List of companies 26 | company_list = open("input/complete.txt") 27 | print("Getting stock ticker list...") 28 | 29 | for name in company_list: 30 | 31 | # Get data from google finance url 32 | name = name.strip() 33 | path = cache + "/" + name + "_" + title_date + "_" + time.strftime("%d-%m-%Y") + ".csv" 34 | if os.path.exists(path): 35 | df = pandas.read_csv(path) 36 | else: 37 | url = "https://www.google.com/finance/historical?q=NASDAQ:" + name + \ 38 | "&startdate=" + start_month + "+" + start_day + "%2C+" + start_year + "&output=csv" 39 | raw_data = requests.get(url).content 40 | with open(path, 'wb') as f: 41 | f.write(raw_data) 42 | df = pandas.read_csv(io.StringIO(raw_data.decode('utf-8'))) 43 | df = df.iloc[::-1] 44 | 45 | # Create new csv with the data 46 | prices = numpy.array(df['Close']) 47 | # print(prices) 48 | 49 | # Append to new csv 50 | row = len(prices) 51 | num_test = int(row * percent_test) 52 | for i in range(200, row-future_interval-num_test): 53 | 54 | # Parse data 55 | curr_200 = prices[i]/prices[i-200:i].mean() 56 | curr_100 = prices[i]/prices[i-100:i].mean() 57 | curr_50 = prices[i]/prices[i-50:i].mean() 58 | future = prices[i+future_interval] 59 | 60 | # Write to file 61 | num = 0 62 | if future > prices[i]: 63 | num = 1 64 | 65 | # Write to csv 66 | train_csv.write(str(curr_200) + "," + str(curr_100) + "," + str(curr_50) + "," + str(num) + "\n") 67 | 68 | # Append to test csv as well 69 | for i in range(row-future_interval-num_test, row-future_interval): 70 | 71 | # Parse data 72 | curr_200 = prices[i] / prices[i - 200:i].mean() 73 | curr_100 = prices[i] / prices[i - 100:i].mean() 74 | curr_50 = prices[i] / prices[i - 50:i].mean() 75 | future = prices[i + future_interval] 76 | 77 | # Write to file 78 | num = 0 79 | if future > prices[i]: 80 | num = 1 81 | 82 | # Write to csv 83 | test_csv.write(str(curr_200) + "," + str(curr_100) + "," + str(curr_50) + "," + str(num) + "\n") 84 | 85 | 86 | if __name__ == '__main__': 87 | gen() 88 | -------------------------------------------------------------------------------- /src/mean_reversion/__pycache__/api.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Davarco/AlgoBot/2ad8ce28563186806c43f918614cc57c799391a1/src/mean_reversion/__pycache__/api.cpython-36.pyc -------------------------------------------------------------------------------- /src/mean_reversion/__pycache__/backtest.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Davarco/AlgoBot/2ad8ce28563186806c43f918614cc57c799391a1/src/mean_reversion/__pycache__/backtest.cpython-36.pyc -------------------------------------------------------------------------------- /src/mean_reversion/api.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) 4 | 5 | from flask import Flask 6 | from flask_restful import Resource, Api 7 | from data import retrieve_list 8 | from stock import Stock 9 | from mean_reversion.backtest import k, num_days 10 | 11 | 12 | app = Flask(__name__) 13 | api = Api(app) 14 | 15 | todos = {} 16 | 17 | # Get stock data 18 | stock_data = retrieve_list("input/complete.txt") 19 | stock_dict_list = [] 20 | for key in stock_data: 21 | stock_dict_list.append(Stock(key, stock_data[key], k, 0, num_days)) 22 | 23 | # Sort stock by buy and sell 24 | buy_order = sorted(stock_dict_list, key=lambda stock_sorting: stock_sorting.lower_band_diff, reverse=True) 25 | sell_order = sorted(stock_dict_list, key=lambda stock_sorting: stock_sorting.upper_band_diff, reverse=True) 26 | 27 | buy_json = [] 28 | for stock in buy_order: 29 | buy_json.append(stock.__dict__) 30 | 31 | sell_json = [] 32 | for stock in sell_order: 33 | sell_json.append(stock.__dict__) 34 | 35 | 36 | class BuyOrder(Resource): 37 | def get(self): 38 | # stock = buy_order[0] 39 | # buy_order.remove(stock) 40 | # buy_order.insert(-1, stock) 41 | # return {"data": stock.__dict__} 42 | return {"data_count": len(buy_json), "data": buy_json} 43 | 44 | 45 | class SellOrder(Resource): 46 | def get(self): 47 | # stock = sell_order[0] 48 | # sell_order.remove(stock) 49 | # sell_order.insert(-1, stock) 50 | # return {"data": stock.__dict__} 51 | return {"data_count": len(sell_json), "data": sell_json} 52 | 53 | 54 | class GetStock(Resource): 55 | def get(self, ticker): 56 | stock = None 57 | for s in stock_dict_list: 58 | if s.ticker == ticker: 59 | stock = s 60 | break 61 | 62 | if stock is not None: 63 | return {ticker: stock.__dict__} 64 | else: 65 | return {"error": "ticker not found!"} 66 | 67 | # Add resources to API 68 | api.add_resource(BuyOrder, '/stocks/buy') 69 | api.add_resource(SellOrder, '/stocks/sell') 70 | api.add_resource(GetStock, '/stocks/get/') 71 | 72 | if __name__ == '__main__': 73 | app.run(host='0.0.0.0') 74 | -------------------------------------------------------------------------------- /src/mean_reversion/backtest.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) 4 | 5 | from stock import Stock 6 | from data import retrieve_list 7 | from visualize import graph_mean_reversion 8 | from visualize import graph_mean_reversion_single 9 | from datetime import datetime 10 | import pandas as pd 11 | import numpy as np 12 | 13 | # Constants 14 | k = 1.2 15 | num_days = 200 16 | time_span = 100 17 | LOG = "logs/" 18 | 19 | 20 | # Go through all of the stock dictionaries 21 | def backtest(stock_dict_list, log): 22 | 23 | # Prepare the pandas dataframe 24 | df = pd.DataFrame(columns=['Ticker', 'Spent', 'Profit', 'Percent', 'K']) 25 | row = 0 26 | for stock_list in stock_dict_list: 27 | 28 | # Set if it should be buying (true) or selling (false), save results 29 | buy = True 30 | sell = False 31 | prev = 0 32 | profit = 0 33 | price = 0 34 | total = 0 35 | num = 0 36 | day = 1 37 | ticker = stock_list[0].ticker 38 | 39 | # Write title to log file 40 | log.write("-" * len(ticker) + "\n") 41 | log.write(ticker + "\n") 42 | log.write("-" * len(ticker) + "\n") 43 | 44 | # Go through all the stocks, each iteration represents one day 45 | for stock in stock_list: 46 | 47 | # Get bands and current price 48 | upper = stock.upper_band 49 | lower = stock.lower_band 50 | today = stock.today_price 51 | 52 | # Buy stock if price is lower than lower band 53 | if today <= lower and buy: 54 | log.write("%-12s %-12s %-8.3f \n%-12s %-12s %-4.0f" % ("Buying!", "@price", today, "", "@day", day) + "\n") 55 | price += today 56 | total += today 57 | num += 1 58 | buy = False 59 | 60 | # Allow buying again after the lower peak 61 | if today > lower and not buy: 62 | log.write("%-12s" % "Resetting!" + "\n") 63 | buy = True 64 | 65 | # Allow selling after the first day the stock goes down 66 | if today > prev: 67 | sell = True 68 | 69 | # Set the previous to today 70 | prev = today 71 | 72 | # Sell stock if price is higher than upper band 73 | if today >= upper and num != 0 and sell: 74 | profit += (today*num - price) 75 | log.write("%-12s %-12s %-8.3f \n%-12s %-12s %-4.0f \n%-12s %-12s %-8.3f \n%-12s %-12s %-2s" 76 | % ("Selling!", "@price", today, "", "@day", day, "", "@num_shares", num, "", "@profit", profit) + "\n") 77 | num = 0 78 | price = 0 79 | buy = True 80 | sell = False 81 | 82 | # Change to next day 83 | day += 1 84 | 85 | # Get the percent 86 | percent = 0 87 | if not profit == 0: 88 | percent = profit/total*100 89 | 90 | # Save data 91 | df.loc[row] = ['%5s' % ticker, '%8.3f' % total, '%8.3f' % profit, '%8.3f' % percent, '%4.1f' % k] 92 | 93 | # Increase the row 94 | row += 1 95 | log.write("\n") 96 | 97 | return df 98 | 99 | 100 | def main(): 101 | 102 | # List that holds the data 103 | stock_data = retrieve_list("input/complete.txt") 104 | 105 | # 2d arr, arr holds list of stocks throughout time span, each arr is a different stock 106 | stock_dict_list = [] 107 | 108 | # Go through backtest stocks 109 | print("Testing algorithm on historical stock data...") 110 | for key in stock_data: 111 | temp = [] 112 | for start in range(time_span, 0, -1): 113 | temp.append(Stock(key, stock_data[key], k, start, num_days)) 114 | stock_dict_list.append(temp) 115 | 116 | # Create a new log file 117 | path_dir = LOG + datetime.now().strftime("%m-%d-%Y") 118 | if not os.path.exists(path_dir): 119 | os.makedirs(path_dir) 120 | path = path_dir + "/" + datetime.now().strftime("%H_%M_%S") 121 | print(path + ".txt") 122 | log = open(path + ".txt", 'w') 123 | 124 | # Get the dataframe from the backtest 125 | df = backtest(stock_dict_list, log) 126 | 127 | # Print the backtested data 128 | print(df) 129 | df.to_csv(path + ".csv") 130 | 131 | # Store final results 132 | num_profitable = 0 133 | num_unprofitable = 0 134 | for i in range(0, len(stock_dict_list)): 135 | if float(df.values[i, 2]) > 0: 136 | num_profitable += 1 137 | elif float(df.values[i, 2]) < 0: 138 | num_unprofitable += 1 139 | log.write("Profitable: " + str(num_profitable) + "\n") 140 | log.write("Unprofitable: " + str(num_unprofitable)) 141 | 142 | # Don't need all the graphs 143 | num_graphs = input("Graphs: ") 144 | if num_graphs.lower() == "active": 145 | 146 | # Make sure something happened 147 | temp_dict_list = [stock_dict_list[i] for i in range(0, len(stock_dict_list)) if float(df.values[i, 1]) != 0] 148 | 149 | elif num_graphs.lower() == "profitable": 150 | 151 | # Make sure the graphs were profitable 152 | temp_dict_list = [stock_dict_list[i] for i in range(0, len(stock_dict_list)) if float(df.values[i, 2]) > 0] 153 | 154 | elif num_graphs.lower() == "unprofitable": 155 | 156 | # Make sure the graphs were unprofitable 157 | temp_dict_list = [stock_dict_list[i] for i in range(0, len(stock_dict_list)) if float(df.values[i, 1]) < 0] 158 | 159 | elif num_graphs.lower() == "all": 160 | 161 | # Get all the graphs 162 | num_graphs = len(stock_dict_list) 163 | temp_dict_list = [stock_dict_list[i] for i in range(0, int(num_graphs))] 164 | 165 | elif num_graphs.lower() == "max": 166 | 167 | # Get the most profitable 168 | max_stock = stock_dict_list[0] 169 | max_percent = 0.0 170 | for i in range(0, len(stock_dict_list)): 171 | if float(df.values[i, 3]) > max_percent: 172 | max_percent = float(df.values[i, 3]) 173 | max_stock = stock_dict_list[i] 174 | 175 | # Print the max percent 176 | print("Max percent: " + str(max_percent)) 177 | 178 | # Only graph one stock 179 | graph_mean_reversion_single(max_stock) 180 | return 181 | 182 | else: 183 | 184 | # Get the correct number of graphs 185 | temp_dict_list = [stock_dict_list[i] for i in range(0, int(num_graphs)) if stock_dict_list[i].ticker == num_graphs.upper()] 186 | 187 | graph_mean_reversion(temp_dict_list) 188 | 189 | # Get the net percent 190 | net_percent = np.sum(float(i) for i in df.values[:, 3] if float(i) > 0) 191 | print("Net percent: %6.3f" % net_percent) 192 | 193 | 194 | if __name__ == '__main__': 195 | main() 196 | -------------------------------------------------------------------------------- /src/purchase.py: -------------------------------------------------------------------------------- 1 | from stock import Stock 2 | from data import retrieve_list 3 | 4 | # Constants 5 | k = 1.5 6 | num_days = 200 7 | time_span = 300 8 | 9 | 10 | def main(): 11 | 12 | # List that holds the data 13 | stock_data = retrieve_list("input/complete.txt") 14 | 15 | # 2d arr, arr holds list of stocks throughout time span, each arr is a different stock 16 | stock_dict_list = [] 17 | 18 | init_money = int(input("Initial: ")) 19 | money = init_money 20 | 21 | # Go through backtest stocks 22 | for key in stock_data: 23 | stock_dict_list.append(Stock(key, stock_data[key], k, 0, num_days)) 24 | 25 | buyables = sorted(stock_dict_list, key=lambda stock_sorting: stock_sorting.lower_band_diff, reverse=True) 26 | 27 | if buyables.__len__() < 1 or buyables[0].lower_band_diff < 0: 28 | print("Don't buy stocks today. You either can't afford them or they're not in a profitable condition.") 29 | else: 30 | print() 31 | print("FIRST STOCK -- SPENDING 50%!") 32 | buy = buyables[0] 33 | spendable = init_money / 2 34 | 35 | before_spending = money 36 | 37 | num = 0 38 | spent = 0.0 39 | 40 | while spent < spendable: 41 | money -= buy.today_price 42 | spent += buy.today_price 43 | num += 1 44 | 45 | if spent > spendable: 46 | money += buy.today_price 47 | spent -= buy.today_price 48 | num -= 1 49 | 50 | while money < 0: 51 | money += buy.today_price 52 | spent -= buy.today_price 53 | num -= 1 54 | 55 | if num > 0: 56 | print("Buy %s stocks from ticker %s. Today's price is %.2f, and you have %.2f. " 57 | "You will have %.2f left." % 58 | (num, buy.ticker, buy.today_price, before_spending, money)) 59 | 60 | print() 61 | print("SECOND STOCK -- SPENDING 25%!") 62 | buy = buyables[1] 63 | spendable = init_money / 4 64 | 65 | before_spending = money 66 | 67 | num = 0 68 | spent = 0.0 69 | 70 | while spent < spendable: 71 | money -= buy.today_price 72 | spent += buy.today_price 73 | num += 1 74 | 75 | if spent > spendable: 76 | money += buy.today_price 77 | spent -= buy.today_price 78 | num -= 1 79 | 80 | while money < 0: 81 | money += buy.today_price 82 | spent -= buy.today_price 83 | num -= 1 84 | 85 | if num > 0: 86 | print("Buy %s stocks from ticker %s. Today's price is %.2f, and you have %.2f. " 87 | "You will have %.2f left." % 88 | (num, buy.ticker, buy.today_price, before_spending, money)) 89 | 90 | print() 91 | print("THIRD STOCK -- SPENDING 25% money") 92 | buy = buyables[2] 93 | spendable = init_money / 4 94 | 95 | before_spending = money 96 | 97 | num = 0 98 | spent = 0.0 99 | 100 | while spent < spendable: 101 | money -= buy.today_price 102 | spent += buy.today_price 103 | num += 1 104 | 105 | if spent > spendable: 106 | money += buy.today_price 107 | spent -= buy.today_price 108 | num -= 1 109 | 110 | while money < 0: 111 | money += buy.today_price 112 | spent -= buy.today_price 113 | num -= 1 114 | 115 | if num > 0: 116 | print("Buy %s stocks from ticker %s. Today's price is %.2f, and you have %.2f. " 117 | "You will have %.2f left." % 118 | (num, buy.ticker, buy.today_price, before_spending, money)) 119 | 120 | print("\nDone! Keep the remainder of your money.") 121 | 122 | # print("FURTHER STOCKS -- SPENDING AS MUCH money AS POSSIBLE") 123 | # for i in range(3, buyables.__len__()): 124 | # if money < 1: 125 | # break 126 | # 127 | # buyable = buyables[i] 128 | # num = 0 129 | # 130 | # while money > 0: 131 | # money -= buyable.today_price 132 | # num += 1 133 | # 134 | # if money < 0: 135 | # money += buyable.today_price 136 | # num -= 1 137 | # 138 | # if num > 0: 139 | # print("Buy %s stocks from ticker %s. Today's price is %.2f money, and you have %.2f money. " 140 | # "You will have %.2f money left." % 141 | # (num, buyable.ticker, buyable.today_price, init_money, money)) 142 | # 143 | # init_money = money 144 | 145 | if __name__ == '__main__': 146 | main() 147 | -------------------------------------------------------------------------------- /src/stock.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | class Stock(object): 5 | 6 | ticker = "" 7 | k = 0 8 | num_days = 0 9 | mean_price = 0 10 | today_price = 0 11 | deviation = 0 12 | upper_band = 0 13 | lower_band = 0 14 | upper_band_diff = 0 15 | lower_band_diff = 0 16 | ma_200 = 0 17 | ma_50 = 0 18 | ma_diff = 0 19 | yest_price = 0 20 | 21 | def __str__(self): 22 | return "\nTicker: " + self.ticker + "\nStd Deviation: " + str(self.deviation) + "\nMean Price: " + str(self.mean_price) + \ 23 | "\nUpper Band: " + str(self.upper_band) + "\nLower Band: " + str(self.lower_band) + \ 24 | "\nToday Price: " + str(self.today_price) + "\nDays: " + str(self.num_days) 25 | 26 | def __init__(self, ticker, data, k, start, num_days): 27 | 28 | # Use numpy array to get features 29 | prices = np.array(data.iloc[start:start+num_days, 4].values) 30 | self.k = k 31 | self.ticker = ticker 32 | self.num_days = num_days 33 | self.mean_price = prices.mean() 34 | self.today_price = prices[0] 35 | self.yest_price = prices[1] 36 | self.deviation = prices.std() 37 | 38 | # Calculate the upper and lower band 39 | self.upper_band = self.mean_price + k*self.deviation 40 | self.lower_band = self.mean_price - k*self.deviation 41 | self.lower_band_diff = (self.lower_band - self.today_price) / self.today_price 42 | self.upper_band_diff = (self.today_price - self.upper_band) / self.today_price 43 | 44 | # Get the 200 and 50 day moving averages 45 | self.ma_200 = prices[0:200].mean() 46 | self.ma_50 = prices[0:50].mean() 47 | self.ma_diff = (self.ma_50 - self.ma_200) / self.ma_200 48 | -------------------------------------------------------------------------------- /src/train.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import statsmodels.api as sm 3 | import numpy as np 4 | 5 | 6 | def train(): 7 | 8 | # Get data 9 | df = pd.read_csv("input/train.csv") 10 | df.columns = ["200avg", "100avg", "50avg", "Undervalued"] 11 | # print(df.head()) 12 | 13 | # Redistribute the data 14 | df.insert(0, 'Intercept', 1) 15 | # print(df.head()) 16 | 17 | # Train the data 18 | train_cols = df.columns[0:4] 19 | # print(df[train_cols].head()) 20 | # print(df['Undervalued'].head()) 21 | logistic = sm.Logit(df['Undervalued'], df[train_cols]) 22 | result = logistic.fit() 23 | 24 | # Show results 25 | print(result.summary()) 26 | return result 27 | 28 | 29 | def test(params): 30 | 31 | # Get csv data 32 | df = pd.read_csv("input/test.csv") 33 | df.columns = ["200avg", "100avg", "50avg", "Undervalued"] 34 | matrix = np.matrix(df) 35 | 36 | # Store data 37 | acc = 0 38 | total = 0 39 | 40 | # Go through matrix and make predictions 41 | for row in matrix: 42 | ma_200 = row[0, 0] 43 | ma_100 = row[0, 1] 44 | ma_50 = row[0, 2] 45 | result = row[0, 3] 46 | pred = params.predict([ma_200, ma_100, ma_50, 1])[0] 47 | 48 | # Add to count if accurate 49 | if pred > 0.5 and result == 1: 50 | acc += 1 51 | if pred < 0.5 and result == 0: 52 | acc += 1 53 | total += 1 54 | 55 | # Get percent correct 56 | percent = acc/total 57 | print(percent) 58 | 59 | 60 | def main(): 61 | 62 | # Train data 63 | params = train() 64 | 65 | # Test data 66 | test(params) 67 | 68 | 69 | if __name__ == '__main__': 70 | main() 71 | -------------------------------------------------------------------------------- /src/trend_following/__pycache__/backtest.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Davarco/AlgoBot/2ad8ce28563186806c43f918614cc57c799391a1/src/trend_following/__pycache__/backtest.cpython-36.pyc -------------------------------------------------------------------------------- /src/trend_following/api.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) 4 | 5 | from flask import Flask 6 | from flask_restful import Resource, Api 7 | from data import retrieve_list 8 | from stock import Stock 9 | from trend_following.backtest import num_days, k 10 | 11 | 12 | app = Flask(__name__) 13 | api = Api(app) 14 | 15 | todos = {} 16 | 17 | # Get stock data 18 | stock_data = retrieve_list("input/complete.txt") 19 | stock_dict_list = [] 20 | for key in stock_data: 21 | stock_dict_list.append(Stock(key, stock_data[key], k, 0, num_days)) 22 | 23 | # Sort stocks to buy and sell 24 | buy_order = sorted(stock_dict_list, key=lambda stock_sorting: stock_sorting.ma_diff, reverse=False) 25 | sell_order = sorted(stock_dict_list, key=lambda stock_sorting: stock_sorting.ma_diff, reverse=True) 26 | 27 | buy_json = [] 28 | for stock in buy_order: 29 | buy_json.append(stock.__dict__) 30 | 31 | sell_json = [] 32 | for stock in sell_order: 33 | sell_json.append(stock.__dict__) 34 | 35 | 36 | class BuyOrder(Resource): 37 | def get(self): 38 | # stock = buy_order[0] 39 | # buy_order.remove(stock) 40 | # buy_order.insert(-1, stock) 41 | # return {"data": stock.__dict__} 42 | return {"data_count": len(buy_json), "data": buy_json} 43 | 44 | 45 | class SellOrder(Resource): 46 | def get(self): 47 | # stock = sell_order[0] 48 | # sell_order.remove(stock) 49 | # sell_order.insert(-1, stock) 50 | # return {"data": stock.__dict__} 51 | return {"data_count": len(sell_json), "data": sell_json} 52 | 53 | 54 | class GetStock(Resource): 55 | def get(self, ticker): 56 | stock = None 57 | for s in stock_dict_list: 58 | if s.ticker == ticker: 59 | stock = s 60 | break 61 | 62 | if stock is not None: 63 | return {ticker: stock.__dict__} 64 | else: 65 | return {"error": "ticker not found!"} 66 | 67 | # Add the resources to the API 68 | api.add_resource(BuyOrder, '/stocks/buy') 69 | api.add_resource(SellOrder, '/stocks/sell') 70 | api.add_resource(GetStock, '/stocks/get/') 71 | 72 | if __name__ == '__main__': 73 | app.run(host='0.0.0.0') 74 | -------------------------------------------------------------------------------- /src/trend_following/backtest.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) 4 | 5 | from stock import Stock 6 | from data import retrieve_list 7 | from visualize import graph_moving_average 8 | from visualize import graph_moving_average_single 9 | from datetime import datetime 10 | import pandas as pd 11 | import numpy as np 12 | 13 | # Constants 14 | k = 1.2 15 | num_days = 200 16 | time_span = 200 17 | LOG = "logs/" 18 | 19 | 20 | # Go through all of the stock dictionaries 21 | def backtest(stock_dict_list, log): 22 | 23 | # Prepare the pandas dataframe 24 | df = pd.DataFrame(columns=['Ticker', 'Spent', 'Profit', 'Percent', 'K']) 25 | row = 0 26 | for stock_list in stock_dict_list: 27 | 28 | # Set if it should be buying (true) or selling (false), save results 29 | buy = True 30 | sell = False 31 | prev = 0 32 | profit = 0 33 | price = 0 34 | total = 0 35 | num = 0 36 | day = 1 37 | ticker = stock_list[0].ticker 38 | 39 | # Write title to log file 40 | log.write("-" * len(ticker) + "\n") 41 | log.write(ticker + "\n") 42 | log.write("-" * len(ticker) + "\n") 43 | 44 | # Go through all the stocks, each iteration represents one day 45 | for stock in stock_list: 46 | 47 | # Get bands and current price 48 | ma_200 = stock.ma_200 49 | ma_50 = stock.ma_50 50 | today = stock.today_price 51 | 52 | # Buy stock if price is lower than lower band 53 | if ma_50 <= ma_200 and buy: 54 | log.write("%-12s %-12s %-8.3f \n%-12s %-12s %-4.0f" % ("Buying!", "@price", today, "", "@day", day) + "\n") 55 | price += ma_50 56 | total += ma_50 57 | num += 1 58 | buy = False 59 | 60 | # Allow buying again after the lower peak 61 | if ma_50 > ma_200 and not buy: 62 | log.write("%-12s" % "Resetting!" + "\n") 63 | buy = True 64 | 65 | # Allow selling after the first day the stock goes down 66 | if ma_50 > prev: 67 | sell = True 68 | 69 | # Set the previous to today 70 | prev = today 71 | 72 | # Sell stock if price is higher than upper band 73 | if ma_50 > ma_200 and num != 0 and sell: 74 | profit += (today*num - price) 75 | log.write("%-12s %-12s %-8.3f \n%-12s %-12s %-4.0f \n%-12s %-12s %-8.3f \n%-12s %-12s %-2s" 76 | % ("Selling!", "@price", today, "", "@day", day, "", "@num_shares", num, "", "@profit", profit) + "\n") 77 | num = 0 78 | price = 0 79 | buy = True 80 | sell = False 81 | 82 | # Change to next day 83 | day += 1 84 | 85 | # Get the percent 86 | percent = 0 87 | if not profit == 0: 88 | percent = profit/total*100 89 | 90 | # Save data 91 | df.loc[row] = ['%5s' % ticker, '%8.3f' % total, '%8.3f' % profit, '%8.3f' % percent, '%4.1f' % k] 92 | 93 | # Increase the row 94 | row += 1 95 | log.write("\n") 96 | 97 | return df 98 | 99 | 100 | def main(): 101 | 102 | # List that holds the data 103 | stock_data = retrieve_list("input/complete.txt") 104 | 105 | # 2d arr, arr holds list of stocks throughout time span, each arr is a different stock 106 | stock_dict_list = [] 107 | 108 | # Go through backtest stocks 109 | print("Testing algorithm on historical stock data...") 110 | for key in stock_data: 111 | temp = [] 112 | for start in range(time_span, 0, -1): 113 | temp.append(Stock(key, stock_data[key], k, start, num_days)) 114 | stock_dict_list.append(temp) 115 | 116 | # Create a new log file 117 | path_dir = LOG + datetime.now().strftime("%m-%d-%Y") 118 | if not os.path.exists(path_dir): 119 | os.makedirs(path_dir) 120 | path = path_dir + "/" + datetime.now().strftime("%H_%M_%S") 121 | print(path + ".txt") 122 | log = open(path + ".txt", 'w') 123 | 124 | # Get the dataframe from the backtest 125 | df = backtest(stock_dict_list, log) 126 | 127 | # Print the backtested data 128 | print(df) 129 | df.to_csv(path + ".csv") 130 | 131 | # Store final results 132 | num_profitable = 0 133 | num_unprofitable = 0 134 | for i in range(0, len(stock_dict_list)): 135 | if float(df.values[i, 2]) > 0: 136 | num_profitable += 1 137 | elif float(df.values[i, 2]) < 0: 138 | num_unprofitable += 1 139 | log.write("Profitable: " + str(num_profitable) + "\n") 140 | log.write("Unprofitable: " + str(num_unprofitable)) 141 | 142 | # Don't need all the graphs 143 | num_graphs = input("Graphs: ") 144 | if num_graphs.lower() == "active": 145 | 146 | # Make sure something happened 147 | temp_dict_list = [stock_dict_list[i] for i in range(0, len(stock_dict_list)) if float(df.values[i, 1]) != 0] 148 | 149 | elif num_graphs.lower() == "profitable": 150 | 151 | # Make sure the graphs were profitable 152 | temp_dict_list = [stock_dict_list[i] for i in range(0, len(stock_dict_list)) if float(df.values[i, 2]) > 0] 153 | 154 | elif num_graphs.lower() == "unprofitable": 155 | 156 | # Make sure the graphs were unprofitable 157 | temp_dict_list = [stock_dict_list[i] for i in range(0, len(stock_dict_list)) if float(df.values[i, 1]) < 0] 158 | 159 | elif num_graphs.lower() == "all": 160 | 161 | # Get all the graphs 162 | num_graphs = len(stock_dict_list) 163 | temp_dict_list = [stock_dict_list[i] for i in range(0, int(num_graphs))] 164 | 165 | elif num_graphs.lower() == "max": 166 | 167 | # Get the most profitable 168 | max_stock = stock_dict_list[0] 169 | max_percent = 0.0 170 | for i in range(0, len(stock_dict_list)): 171 | if float(df.values[i, 3]) > max_percent: 172 | max_percent = float(df.values[i, 3]) 173 | max_stock = stock_dict_list[i] 174 | 175 | # Print the max percent 176 | print("Max percent: " + str(max_percent)) 177 | 178 | # Only graph one stock 179 | graph_moving_average_single(max_stock) 180 | return 181 | 182 | else: 183 | 184 | # Get the correct number of graphs 185 | temp_dict_list = [stock_dict_list[i] for i in range(0, int(num_graphs)) if stock_dict_list[i].ticker == num_graphs.upper()] 186 | 187 | graph_moving_average(temp_dict_list) 188 | 189 | # Get the net percent 190 | net_percent = np.sum(float(i) for i in df.values[:, 3] if float(i) > 0) 191 | print("Net percent: %6.3f" % net_percent) 192 | 193 | 194 | if __name__ == '__main__': 195 | main() 196 | -------------------------------------------------------------------------------- /src/visualize.py: -------------------------------------------------------------------------------- 1 | from data import retrieve_list 2 | from stock import Stock 3 | import pylab as plt 4 | import pandas as pd 5 | 6 | 7 | def graph_mean_reversion_single(stock): 8 | 9 | # Get the upper, lower, and price band, and curr 10 | upper_band = pd.DataFrame([item.upper_band for item in stock]) 11 | lower_band = pd.DataFrame([item.lower_band for item in stock]) 12 | price_band = pd.DataFrame([item.mean_price for item in stock]) 13 | today_band = pd.DataFrame([item.today_price for item in stock]) 14 | 15 | # Set index columns 16 | upper_band.insert(0, 'day', upper_band.index+1) 17 | lower_band.insert(0, 'day', lower_band.index+1) 18 | price_band.insert(0, 'day', price_band.index+1) 19 | today_band.insert(0, 'day', today_band.index+1) 20 | 21 | # Plot graphs 22 | plt.figure() 23 | plt.plot(upper_band.values[:, 0], upper_band.values[:, 1], color="green") 24 | plt.plot(lower_band.values[:, 0], lower_band.values[:, 1], color="red") 25 | plt.plot(price_band.values[:, 0], price_band.values[:, 1], color="blue") 26 | plt.plot(today_band.values[:, 0], today_band.values[:, 1], color="black") 27 | plt.xlabel("Day") 28 | plt.ylabel("Price") 29 | plt.title(stock[0].ticker + ": Mean Reversion") 30 | plt.gcf().canvas.set_window_title(stock[0].ticker + ": Mean Reversion") 31 | plt.ylim(ymin=0) 32 | plt.draw() 33 | 34 | plt.show() 35 | 36 | 37 | def graph_mean_reversion(stock_data_list): 38 | 39 | # Go through all of the stocks 40 | for stock in stock_data_list: 41 | 42 | # Get the upper, lower, and price band, and curr 43 | upper_band = pd.DataFrame([item.upper_band for item in stock]) 44 | lower_band = pd.DataFrame([item.lower_band for item in stock]) 45 | price_band = pd.DataFrame([item.mean_price for item in stock]) 46 | today_band = pd.DataFrame([item.today_price for item in stock]) 47 | 48 | # Set index columns 49 | upper_band.insert(0, 'day', upper_band.index+1) 50 | lower_band.insert(0, 'day', lower_band.index+1) 51 | price_band.insert(0, 'day', price_band.index+1) 52 | today_band.insert(0, 'day', today_band.index+1) 53 | 54 | # Plot graphs 55 | plt.figure() 56 | plt.plot(upper_band.values[:, 0], upper_band.values[:, 1], color="green") 57 | plt.plot(lower_band.values[:, 0], lower_band.values[:, 1], color="red") 58 | plt.plot(price_band.values[:, 0], price_band.values[:, 1], color="blue") 59 | plt.plot(today_band.values[:, 0], today_band.values[:, 1], color="black") 60 | plt.xlabel("Day") 61 | plt.ylabel("Price") 62 | plt.title(stock[0].ticker + ": Mean Reversion") 63 | plt.gcf().canvas.set_window_title(stock[0].ticker + ": Mean Reversion") 64 | plt.ylim(ymin=0) 65 | plt.draw() 66 | 67 | plt.show() 68 | 69 | 70 | def graph_mean_reversion_default(): 71 | 72 | # Constants 73 | k = 1.5 74 | num_days = 200 75 | time_span = 1000 76 | 77 | # List that holds the data 78 | stock_data = retrieve_list("input/custom.txt") 79 | 80 | # 2d arr, arr holds list of stocks throughout time span, each arr is a different stock 81 | stock_data_list = [] 82 | 83 | # Go through backtest stocks 84 | for key in stock_data: 85 | temp = [] 86 | for start in range(time_span, 0, -1): 87 | temp.append(Stock(key, stock_data[key], k, start, num_days)) 88 | stock_data_list.append(temp) 89 | 90 | # Graph the historical data 91 | graph_mean_reversion(stock_data_list) 92 | 93 | 94 | def graph_moving_average_single(stock): 95 | 96 | # Get the upper, lower, and price band, and curr 97 | ma_200 = pd.DataFrame([item.ma_200 for item in stock]) 98 | ma_50 = pd.DataFrame([item.ma_50 for item in stock]) 99 | today_band = pd.DataFrame([item.today_price for item in stock]) 100 | 101 | # Set index columns 102 | ma_200.insert(0, 'day', ma_200.index + 1) 103 | ma_50.insert(0, 'day', ma_50.index + 1) 104 | today_band.insert(0, 'day', today_band.index + 1) 105 | 106 | # Plot graphs 107 | plt.figure() 108 | plt.plot(ma_200.values[:, 0], ma_200.values[:, 1], color="red") 109 | plt.plot(ma_50.values[:, 0], ma_50.values[:, 1], color="blue") 110 | plt.plot(today_band.values[:, 0], today_band.values[:, 1], color="black") 111 | plt.xlabel("Day") 112 | plt.ylabel("Price") 113 | plt.title(stock[0].ticker + ": Moving Average") 114 | plt.gcf().canvas.set_window_title(stock[0].ticker + ": Moving Average") 115 | plt.ylim(ymin=0) 116 | plt.draw() 117 | 118 | plt.show() 119 | 120 | 121 | def graph_moving_average(stock_data_list): 122 | 123 | # Go through all of the stocks 124 | for stock in stock_data_list: 125 | 126 | # Get the upper, lower, and price band, and curr 127 | ma_200 = pd.DataFrame([item.ma_200 for item in stock]) 128 | ma_50 = pd.DataFrame([item.ma_50 for item in stock]) 129 | today_band = pd.DataFrame([item.today_price for item in stock]) 130 | 131 | # Set index columns 132 | ma_200.insert(0, 'day', ma_200.index + 1) 133 | ma_50.insert(0, 'day', ma_50.index + 1) 134 | today_band.insert(0, 'day', today_band.index + 1) 135 | 136 | # Plot graphs 137 | plt.figure() 138 | plt.plot(ma_200.values[:, 0], ma_200.values[:, 1], color="red") 139 | plt.plot(ma_50.values[:, 0], ma_50.values[:, 1], color="blue") 140 | plt.plot(today_band.values[:, 0], today_band.values[:, 1], color="black") 141 | plt.xlabel("Day") 142 | plt.ylabel("Price") 143 | plt.title(stock[0].ticker + ": Moving Average") 144 | plt.gcf().canvas.set_window_title(stock[0].ticker + ": Moving Average") 145 | plt.ylim(ymin=0) 146 | plt.draw() 147 | 148 | plt.show() 149 | 150 | if __name__ == '__main__': 151 | graph_mean_reversion_default() 152 | --------------------------------------------------------------------------------