A framework for building and testing minute to minute automatic trading using the Alpaca API.
You will need the ASP.NET and web development workflow installed in order to have the right tools to develop this application
In the US, the FINRA defines a "pattern day traders" as a person who execute 4 or more “day trades”
within five business days. To do this, you must have at least $25,000 in their accounts and can only trade in margin accounts. If the account falls below that requirement, the pattern day trader will not be
permitted to day trade until the account is restored to the $25,000 minimum equity level. The margin rule applies to day trading in any security, including options. Therefore, if you do meet the above requirements,
you can set the Is_Pattern_Day_Trader to true and set the trading frequency to any or all of the employed strategies to Minute. If you do not meet these requires, do not set Is_Pattern_Day_Trader
and use only the Day trading frequency. This will ensure you do not get marked as a pattern day trader and have your account locked.
Click the button below! You can customize the strategy and stock list using env vars on deployment (examples below). You will need to add your own Alpaca app ids and secret keys to the env vars through the UI.
This is a command line tool written in .NET 5. After compiling the project using Visual Studio or .NET cli, you will need to add the following environment variables:
"ALPACA_SECRET_KEY": "Your personal Alpaca secret key",
"ALPACA_APP_ID": "Your personal Alpaca app id",
The command app runs on the paper api by default. If you want to run the app using a live account, you need to set another enviorment variable ALPACA_USE_LIVE_API to true.
To set the strategy to be used and the stocks the strategy should be run on, you will need to do one of two things:
- Pass in the values as command line arguments. Ex.
stonks2 --stock-symbols GE F AAPL --strategy-names MeanReversionStrategy MicrotrendStrategy --trading-freqencies Minute Day - Pass the values in via a environment variables. Ex.
export STOCK_LIST="GE, F, AAPL" && export TRADING_STRATEGIES="MeanReversionStrategy, MicrotrendStrategy && export TRADING_FREQENCIES="Minute, Day"
The two examples above are essentially equivalent. They employ the "MeanReversionStrategy" every minute and the "MicrotrendStrategy" every day for the stocks GE, F, and AAPL.
As the trading frequency is variable, the units are then variable. Thus, "units" as used below are the used trading frequency (Minute, Hour, Day, etc.)
Risk: 🐲 🐲 Possible reward: 💰 💰
Calculates the average price for the past 20 units. If the price is lower than the average, buy the stock. If the price is above the average, sell the position.
Risk: 🐲 Possible reward: 💰
If the price increases for 2 consecutive minutes, buy the stock. If the price has dropped in the past 2 units, sell the position. If the price remained the same, do not buy or sell.
Risk: 🐲 🐲 🐲 Possible reward: 💰
Uses ML.Net to create a forecasting model using Singular Spectrum Analysis on the past days price movements. After the model is trained, we use to predict the next minutes price. If the prediction is the price will go up, we buy. If we predict the price will go down, we sell. This training and prediction happens every trading cycle.