plugyawn / coinframe

Library for analysing, backtesting, and presenting cryptocurrency portfolios reliably, and with very little code.

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Coinframe is a library for backtesting on cryptocurrency portfolios, as well as analyzing correlations, plotting relevant rolling graphs, and more.

With coinframe, you can set portfolios, buy, sell, and check value at every step. Designed to work with all standard cryptocurrency data-sources, Coinframe allows rapid checking of trading strategies on python, on-the-go.

A backtesting example.

Here, we have a coinframe object named cf, and in the coin_list, we have ETH and SOL; data has been pooled from the data directory in the repository.

i = 0
while (i < 1000):
    if rise.loc[rise.index == cf.backtest.index[offset+i]]["Derivative"].values[0] == 1:
        if cf.coin_list["ETH"] >= 1:
            success = cf.sell(1, "ETH")
            if not success:
                continue
        cf.buy(1, "SOL")
    else:
        if cf.coin_list["SOL"] >= 1:
            success = cf.sell(1, "SOL")
        cf.buy(1, "ETH")
    cf.progress(steps = 60)
    print(f"Total money: {cf.money}, Total coins: {cf.coin_list}, Total value: {cf.find_value()}")
    i +=1 

Coinframe Error: Not enough money to buy. 
 Require 3433.73 but only have 3003.49.
Total money: 3003.49, Total coins: {'Price': 0, 'SOL': 0, 'ETH': 37}, Total value: 130219.85
----------------------------------------
Sell 1 ETH for 3438.28. 
 Now have 36 ETH and 6441.77 money.
Buy 1 SOL for 177.69999999999982. 
 Now have 1 SOL and 6264.070000000001 money.
Total money: 6264.070000000001, Total coins: {'Price': 0, 'SOL': 1, 'ETH': 36}, Total value: 129625.16000000002
----------------------------------------
Sell 1 SOL for 177.73. 
 Now have 0 SOL and 6441.8 money.
Buy 1 ETH for 3421.76. 
 Now have 37 ETH and 3020.04 money.
Total money: 3020.04, Total coins: {'Price': 0, 'SOL': 0, 'ETH': 37}, Total value: 129893.78

A plotting example.

eth_data = pd.read_csv("./data/BitMart/ETH_USDT_5m.csv")
cf = coinframe(eth_data)
cf.rolling_average(hours = HOURS)
cf.coin.plot()

Future plans

Future plans include visualizing heatmaps and related graphs for finding correlations between coins, and more.

sns.clustermap(correlation_matrix, cmap="RdYlGn")
plt.show()

Contributing

Coinframe is a work-in-progress, so feel free to put in a PR and share in the work!

Notably, if you can find a way to add backtesting strategies through boolean expressions passed to the function, that would be really helpful! Reach me at [e-mail] or progyan.me.