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8 C. H. Best Python Libraries for Algorithmic Trading

Just as individual with significant undergo in software engineering and some knowledge of data science, I underwent a learning curve when I started algorithmic trading. Feeling rich took some time. I found myself writing my own Bollinger bands, or scouring for trading calendars, or using each cryptocurrency exchange's single Apis instead of an abstraction over all of them. These are the Python libraries I wish I'd proverbial when I began chasing alpha. They'll help you make money faster.

1. FinTA

FinTA (Financial Technical Analytic thinking) implements over 80 trading indicators in Pandas. Unlike umteen other trading libraries, which try to do a bit of everything, FinTA lonesome ingests dataframes and spits out trading indicators. Even the comments above each method are instructive, e.g., this comment annotating MACD. You'll believable see close to indicators you wear't even recognize, and the breadth of technical analysis encourages experiment.

2. Zipline

Zipline is the best of the Renaissance man trading libraries. It has almost 13k stars (see my article on using data to judge software program packages here) and powers Quantopian, one of the most popular quant-finance communities, at least until Robinhood fresh acquired it. Zipline allows you to ingest data from the command line (or a Jupyter notebook) and comes improved-in with methods to facilitate writing analyzable strategies and backtesting them.

3. CCXT

CCXT (CryptoCurrency commutation Trading) is a lifeguard if you programmatically trade cryptocurrency. Atomic number 102 Thomas More will you have to indite custom logic for to each one commutation. CCXT abstracts away differences between various exchange APIs with a unified interface. It supports more than 120 exchanges. If you're not a Pythonist, you put up even habit the JavaScript and PHp implementations of CCXT (though you should get punter taste in programming languages).

4. Freqtrade

Freqtrade is another crypto trading program library that supports some exchanges. It facilitates backtesting, plotting, auto learning, performance status, reports, etc. You might be sighing at this point. How many cryptocurrency trading libraries does one algorithmic trading partisan need? What's surprising nigh Freqtrade is that you can control it with Telegram. That's right: you behind henceforward Decimeter your automaton investing director. Here are extraordinary of its amazing Telegram commands:

/status [table]: lists all open trades;
/profit: lists accumulative benefit;
/forcesell danlt;trade_iddangt;|all: sells the granted sell;
/performance: carrying into action of each finished trade grouped aside pair;
/balance: account balance per currency;
/daily danlt;ndangt;: profit or loss per day, over the last n years.

If you privation to power upfield your Freqtrade trading bot and turn on IT into a Gundam ready to ravage financial markets on your behalf, chequer tabu Freqtrade Strategies, which is what its name suggests.

5. YFinance

If you've been trading for long, you've in all likelihood detected of Yahoo! Finance. YFinance allows you to reliably and efficiently download market data from Yahoo! Finance. The library arose from a dire need when Yahoo decommissioned their real data API. The depository library's creator wrote a helpful instructor hither.

6. Backtrader

Backtrader is a popular Python framework for backtesting and trading that includes data feeds, resampling tools, trading calendars, etc. What sets Backtrader apart aside from its features and dependableness is its active biotic community and blog. Backtrader's community could fill a need given Quantopian's recent shutdown.

7. TensorTrade

TensorTrade is a framework for edifice trading algorithms that wont deep reinforcer learning. It provides abstractions over numpy, pandas, gym, keras, and tensorflow to accelerate development. TensorTrade is static in beta, but it's quickly gaining traction and will likely become a mainstay in the quant community. Ecstasy Rex, the creator of Tensor Trade, wrote an fantabulous tutorial.

8. Trump2Cash

I saved the memeiest library for next-to-last. Trump2Cash monitors Donald Trump's tweets. When he mentions publicly traded companies, it analyzes the tweet's sentiment and executes trades accordingly. The subroutine library even includes a utility to benchmark its historical performance. I'm not making some kind of recommendation, only the algorithm has been surprisingly successful.

Even supposing that Trump's power to influence financial markets will presently go down, the root code is well adaptable to other Twitter accounts. If you'ray interested in Chitter sentiment as a feature for a trading strategy, the repo is more than worth a look.

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