This program is a twist on the Martingale gambling strategy. As the price of a stock drops below the previous/current average stock price, it will append +2 buys from the previous position. This program has a cap at 9 buys which represent 25 total positions at once. While in positions, once the price of the stock jumps above the average price of stock positions, it will sell all current holdings.
- Pandas
- NumPy
- robin_stocks api (or yfinance... Read below if so)
| date | close_price | buy | sell | account_value | weighted_avg |
|---|---|---|---|---|---|
| 2017-04-11 | 235.06 | 1 | 0 | 10490.63 | 235.06 |
| 2017-04-12 | 234.03 | 3 | 0 | 10489.61 | 234.28 |
| 2017-04-13 | 232.51 | 5 | 0 | 10485.05 | 233.29 |
| 2017-04-17 | 234.57 | 0 | 9 | 10503.59 | 0.0 |
First note: This dataset was recieved from the robin_stocks api, if you use
different library to retrieve data, then in the backtesting function you will
have to change a couple of the column names.
Replace 'close_price' column to whatever your close/adjusted close prices are.
Also, replace the dropped columns to whatever columns you don't need as part of
your dataset.
- This series in the dataset is a common occurrence when it comes to profiting from this algorithm. Analyzing the data there are multiple occasions of this happening
- As you can see, the program enters the trade at a price of 235.06. As the price decreases, it appends 2 more buys to the previous buy.
- As the program price averages the stock price. It will sell all positions once the price jumps above the average price.
- Notice the that there was a profit of +$12 despite selling the stock nearly $.50 below the initial buy.
- The buy column represents to how many shares were bought near close. The sell represents how many shares were sold that day near close (all positions)
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