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1 | GNU GENERAL PUBLIC LICENSE
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535 |
536 | Nothing in this License shall be construed as excluding or limiting
537 | any implied license or other defenses to infringement that may
538 | otherwise be available to you under applicable patent law.
539 |
540 | 12. No Surrender of Others' Freedom.
541 |
542 | If conditions are imposed on you (whether by court order, agreement or
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548 | to collect a royalty for further conveying from those to whom you convey
549 | the Program, the only way you could satisfy both those terms and this
550 | License would be to refrain entirely from conveying the Program.
551 |
552 | 13. Use with the GNU Affero General Public License.
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561 | combination as such.
562 |
563 | 14. Revised Versions of this License.
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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
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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. If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 |
579 | If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
581 | public statement of acceptance of a version permanently authorizes you
582 | to choose that version for the Program.
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584 | Later license versions may give you additional or different
585 | permissions. However, no additional obligations are imposed on any
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587 | later version.
588 |
589 | 15. Disclaimer of Warranty.
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591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
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612 | 17. Interpretation of Sections 15 and 16.
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621 | END OF TERMS AND CONDITIONS
622 |
623 | How to Apply These Terms to Your New Programs
624 |
625 | If you develop a new program, and you want it to be of the greatest
626 | possible use to the public, the best way to achieve this is to make it
627 | free software which everyone can redistribute and change under these terms.
628 |
629 | To do so, attach the following notices to the program. It is safest
630 | to attach them to the start of each source file to most effectively
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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
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/src/__pycache__/data.cpython-36.pyc:
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https://raw.githubusercontent.com/Davarco/AlgoBot/2ad8ce28563186806c43f918614cc57c799391a1/src/__pycache__/data.cpython-36.pyc
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/src/__pycache__/stock.cpython-36.pyc:
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https://raw.githubusercontent.com/Davarco/AlgoBot/2ad8ce28563186806c43f918614cc57c799391a1/src/__pycache__/stock.cpython-36.pyc
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/src/__pycache__/visualize.cpython-36.pyc:
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/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:
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https://raw.githubusercontent.com/Davarco/AlgoBot/2ad8ce28563186806c43f918614cc57c799391a1/src/mean_reversion/__pycache__/api.cpython-36.pyc
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/src/mean_reversion/__pycache__/backtest.cpython-36.pyc:
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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 |
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