This is an script that determines when to buy or sell stocks based off of linear regression statistics. This script detects changes in the rate of change of the price.
The script is currently built to handle 15 stocks, but can easily modified for 100+.
Download the zip and open Stock_Regress.py in your prefered IDE. Make sure to have python-binance API package.
The program is divided into 4 scripts: stock_connect: connects to RestAPI web socket and stores stock data
stock_plots: generates the ggplot graphs and UI interface to change stocks
stock_trade: contains the buying and selling scripts
stock_reg: performs the regression and error calculation
The code is run from the stock_plots file, but the API credentials and selected stocks need to be set in the stock_connect file. P
Place person Binance info below:
api_key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
api_secret = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
Choose which stocks you want to monitor for trade activity. Place this into the stock_connect file.
tickerlist = ['bnbeth@ticker', 'neoeth@ticker','bateth@ticker', 'xlmeth@ticker','zrpeth@ticker',
'zrxeth@ticker', 'adaeth@ticker','kmdeth@ticker', 'naveth@ticker','enjeth@ticker',
'knceth@ticker', 'rlceth@ticker','rcneth@ticker', 'evxeth@ticker','icxeth@ticker']
Once stock_connect has all the parameters, run the stock_plots function.
The Jesus int controlls whether the script sends the sell command to the API. Set to zero for simulation. Set to 1 for active trading.
Click the "Next Stock Button" to navigate between stocks
Click the "Generate Graph" button to graph all historical data for selected stock
Click the "Wrap it Up" button to sell all traded stocks bath to ETH
Standard errors for regression are measures of how spread out your y variables are around the mean, μ.The standard error of the regression slope, s (also called the standard error of estimate) represents the average distance that your observed values deviate from the regression line. The smaller the “s” value, the closer your values are to the regression line.
In the script, two slopes are determined. One for the last 35 data points and the other for the next 95 data points after that. The slope and assoiciated error for each time period are plotted against the server time. A sample graph is seen below:
The calculation for the last 35 data points is shown below. The lists are converted to numpy arrays so that they can be passed through the linregress function. The slope and intercept are calculated.
if(len(tracker_list[position]) >= 35):
temptimearray = numpy.asarray(temptime_list[position]).astype(numpy.float)
temparray = numpy.asarray(temp_list[position]).astype(numpy.float)
model = linregress(temptimearray,temparray)
slope = model.slope
slope_list[position].append(slope)
slopetime_list[position].append(timer)
intercept = model.intercept
line = slope*temptimearray + intercept
Next, the 99.9% confidence interval is determined
avgx = sum(temptimearray)/len(temptimearray)
devx = sum((temptimearray - avgx)**2)**0.5
error = (sum((temparray - line)**2)/33)**0.5
error = 4.957*error/devx
- Python-Binance - API for stock price websocket, buying, and selling
- Numpy - scientific computing with Python
- Scipy - used for linear regression calculations
- Preston Phillips - Preston5789
This project is licensed under the MIT License - see the LICENSE.md file for details