Using Numpy, generate an array of 500 floats between 0 and 1. For each float in the array, check if > or < 0.5
Price data for each timestamp is in a list of lists. Calculate a Standard Moving Average and compare its value to the closing price.
The dataset is a csv file of OHLC data for BTCUSDT (Bitcoin) Using Btalib, create two Standard Moving Averages with window sizes of 5 and 10. While keeping track of profit, position and order price, apply this trading strategy: Buy when the SMA_5 < SMA_10, Sell when SMA_10 < SMA_5.
Get historical data for BTCUSDT (Bitcoin). Using websocket protocol, create a websocket app which will process historical data and new data streamed live from Binance. Apply this trading strategy:
- SMA_200 < Close: close long & go short
- SMA_200 > Close: close short & go long
Trying algorithms with multiple technical indicator parameters
Trying algorithms with a weighted portfolio of assets to trade
Using backtrader, a backtesting package to backtest trading strategies and evaluate their performance
Linear regression example and multivariate linear regression with pandas, sklearn, matplotlib and seaborn
- Linear regression
The dataset is a timeseries of ETHUSDT (Ethereum) price from Binance. The goal is to check if there is a linear relation between ETHUSDT minutely price and the number of trades made each minute.
- Multivariate linear regression
The datasets are timeseries of various cryptocurrencies. The goal is to check for interesting relations between cryptocurrencies.