{"id":60,"date":"2019-10-16T23:39:39","date_gmt":"2019-10-16T21:39:39","guid":{"rendered":"https:\/\/market-and-us.com\/blog\/?p=60"},"modified":"2019-11-02T16:06:06","modified_gmt":"2019-11-02T15:06:06","slug":"how-to-code-a-backtest","status":"publish","type":"post","link":"https:\/\/market-and-us.com\/blog\/how-to-code-a-backtest-60\/","title":{"rendered":"How to code a backtest?"},"content":{"rendered":"\n

Because I love programming<\/strong> since my first experiences at the age of six I wanted to explain you how you can code your own backtests<\/strong>.<\/p>\n\n\n\n

Did you find a signal you would like to know how it worked in the past 20 years?<\/strong>
We do that know together!<\/p>\n\n\n\n

First the candlesticks<\/strong> …<\/h2>\n\n\n\n

At first we need the candlestick data<\/strong> with date, open, high, low, close and volume of each day.
You can get the data from your broker, for example through the API of Interactive Brokers or the csv-file export of TC2000, or you use one of the free or cheap APIs (IEX, Quandl, …).
For our example you can download the needed data here<\/a>.<\/p>\n\n\n\n

… and the import in your program<\/h2>\n\n\n\n

Now you\u2019re thinking: Hey, I didn\u2019t code anything yet! No problem, it\u2019s not that difficult.
I know many coding languages like JavaScript or PHP but after programming my tools for stocks and trading around 5 years I can recommend to you:<\/p>\n\n\n\n

The best language for programming with financial data is Python<\/a>!<\/strong><\/p><\/blockquote>\n\n\n\n

Let\u2019s start our example with the SPY<\/strong>, daily candles and a simple moving average crossing<\/strong> strategy.<\/p>\n\n\n\n

To read the data<\/span> of our source file (SPY.csv<\/code>, located in the same folder), we just need write this line into a text file named \u201cbacktest.py<\/code>\u201d:<\/p>\n\n\n\n

data = pd.read_csv(\"SPY.csv\")<\/code><\/pre>\n\n\n\n
The \u201cpd\u201d stands for pandas, a library for data calculations and comparison which we have to integrate in our code before<\/span> the line above:<\/p>\n\n\n\n
import pandas as pd<\/code><\/pre>\n\n\n\n
To check the data<\/span> by printing the last 5 rows\/days on the screen we can add<\/p>\n\n\n\n
print(data.tail())<\/code><\/pre>\n\n\n\n
The strategy<\/h2>\n\n\n\n
The simple moving average<\/span> over 200 days (200 SMA) for all rows can be calculated by<\/p>\n\n\n\n
data['SMA200'] = data.Close.rolling(200).mean()<\/code><\/pre>\n\n\n\n
and to check if the close is above the SMA 200<\/span> just type<\/p>\n\n\n\n
data['above']  = data.Close > data.SMA200<\/code><\/pre>\n\n\n\n
Now we have 2 new columns with ‘SMA200<\/code>‘ and ‘above<\/code>‘ in our dataset. We will add three more for the “not above” and for the buy and sell signal<\/span> when crossing (-<\/code> stands for not<\/code> and here we have to use &<\/code> instead of and<\/code>):<\/p>\n\n\n\n
data['notabove'] = -data.above\ndata['buy']  = data.above & data.notabove.shift(1)\ndata['sell'] = data.notabove & data.above.shift(1)<\/code><\/pre>\n\n\n\n
Checking each day with one loop<\/h2>\n\n\n\n
The ‘shift<\/code>‘ moves a column one row up (shift(-1)<\/code>) or down (shift(1)<\/code>), so here we can compare with the day before.
But how to code the backtest for every row of data? While iterating through the rows, we invest at the buy signal and close the position at the sell signal<\/span> while setting the variable invest<\/code> true or false:<\/p>\n\n\n\n
total_return = 0.0\ninvested = False\nfor index, row in data.iterrows():\n    if row.buy and not invested:\n        entry = row.Close\n        invested = True\n    if row.sell and invested:\n        exit = row.Close\n        trade_return = exit\/entry - 1\n        total_return += trade_return\n        invested = False<\/code><\/pre>\n\n\n\n
Show our result<\/h2>\n\n\n\n
And finally we just have to output the total return<\/span>:<\/p>\n\n\n\n
print(\"Total return from\", data.index[0], \"to\", data.index[-1], \"is\", round(total_return*100, 2), \"%.\")<\/code><\/pre>\n\n\n\n
Now you start our program<\/span> with this command – and hopefully the same output if you used the source file of the above link:<\/p>\n\n\n\n
>> python3 backtest.py\nTotal return from 1993-01-29 to 2019-10-08 is 172.33 %.<\/code><\/pre>\n\n\n\n
Therefore you had profits of 172.33% if you invested the same amount<\/span> with our buy signal since 1993. It’s not difficult to change it to the cumulative return or to compare with a buy & hold strategy.<\/p>\n\n\n\n
If there are any questions, experiences or wishes about how to code a backtest I would like to read your comment<\/span> below. I wish you good results with your code, fun with many combinations and a strategy that fits best to you!<\/p>\n","protected":false},"excerpt":{"rendered":"
Because I love programming since my first experiences at the age of six I wanted to explain you how you can code your own backtests. Did you find a signal you would like to know how it worked in the past 20 years?We do that know together! First the candlesticks … At first we need […]<\/p>\n","protected":false},"author":1,"featured_media":63,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"yasr_overall_rating":0,"yasr_post_is_review":"","yasr_auto_insert_disabled":"","yasr_review_type":""},"categories":[7],"tags":[18,26,20,25,22,19,21,23,9,24],"yoast_head":"\nHow to code a backtest? - The Market and Us<\/title>\n<meta name=\"description\" content=\"To know wether a trading strategy will lead to good profits or not a first step is to do a backtest. 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