Developing a trading algorithm for cryptocurrency can be both enjoyable and informative. Unlike traditional finance, there is a sense of freedom in developing these codes, as you are not constrained by the formalities and bureaucracy of traditional financial institutions. Moreover, even non-finance professionals can learn how the trading process works.
Recently, I experimented with some automated tools that connect Binance with TradingView, during the steep increase of cryptocurrency values in 2021-2022. It was fascinating to see the results of different strategies, as we gained and lost profits. However, I now aim to develop my own code, possibly using Python due to its extensive libraries and integrated tools.
For the trading platform, I’m considering Derebit, as it allows for a more streamlined trading experience, without the need for traditional currency. Although its API seems well-developed, I’m still unsure if there are enough transactions on it to enable a smooth trading process.
Python’s flexibility allows for the creation of more customized solutions and a wider range of algorithmic strategies. With numerous libraries available and countless algorithms at your disposal, there is an abundance of tools to explore. Furthermore, I’m excited about integrating a portfolio and wallet management system, which can provide a more comprehensive trading experience.
In the development stage, testing the algorithm is crucial, and I plan to test it on a testnet before moving on to real-time trading. To enhance my algorithm’s efficacy, I’m considering using ChatGPT to propose some algorithms and for testing. Overall, creating a trading algorithm is both an exciting and informative process, especially when working with cryptocurrency.
Right now, we focus on Deribit because it is the lighter in terms of formal registration with lengthy video-validation of Binance and other similar platforms. Deribit seems well done with good background information. Of course, the limitation is that you work purely in cryptocurrencies (BTC or ETH). The approach is very instructive to learn options and futures in more detail. Of course, ChatGPT is already able to output a meaningful to quickly establish a trading code. Let’s how we can develop on that.
For portfolio management and trading, several Python libraries can help streamline processes, analyze data, and execute trades. Here are some of the best Python libraries for these purposes:
Portfolio Management
PyPortfolioOpt:
Purpose: Portfolio optimization and efficient frontier plotting.
Features: Mean-variance optimization, Black-Litterman model, and various risk models.
QuantLib:
Purpose: Comprehensive library for quantitative finance.
Features: Pricing of financial instruments, risk management, and portfolio management tools.
empyrical:
Purpose: Performance and risk statistics for portfolios.
Features: Calculates various financial metrics like Sharpe ratio, beta, alpha, etc.
riskfolio-lib:
Purpose: Multi-objective portfolio optimization.
Features: Modern Portfolio Theory (MPT), Black-Litterman, Hierarchical Risk Parity (HRP), and other models.
Trading
Backtrader:
Purpose: Event-driven backtesting and trading library.
Features: Strategy testing, optimization, live trading support, and integration with various data feeds.
Zipline:
Purpose: Algorithmic trading library for backtesting and live trading.
Features: Event-driven system, extensive data support, and used by Quantopian.
TA-Lib:
Purpose: Technical analysis library.
Features: Over 150 indicators like MACD, RSI, Bollinger Bands, etc.
ccxt:
Purpose: Cryptocurrency trading library.
Features: Unified API for multiple exchanges, supports trading, and market data retrieval.
pandas:
Purpose: Data manipulation and analysis.
Features: Essential for handling time series data, creating trading strategies, and backtesting.
yfinance:
Purpose: Downloading historical market data from Yahoo Finance.
Features: Easy access to historical data, fundamental data, and more.
alpaca-trade-api:
Purpose: Trading API for Alpaca.
Features: Commission-free trading, paper trading, and real-time market data.
ib_insync:
Purpose: Interactive Brokers API.
Features: Asynchronous and synchronous API support for Interactive Brokers.
Data Analysis and Visualization
matplotlib:
Purpose: Plotting and visualization.
Features: Essential for visualizing financial data, creating charts, and graphs.
seaborn:
Purpose: Statistical data visualization.
Features: Advanced visualization capabilities, useful for financial analysis.
plotly:
Purpose: Interactive graphing library.
Features: Interactive plots, great for dashboarding and visual analysis.
statsmodels:
Purpose: Statistical modeling and hypothesis testing.
Features: Time series analysis, econometric models, and more.
These libraries collectively provide a comprehensive toolkit for anyone looking to delve into portfolio management and trading using Python. They cover a broad spectrum of needs from data retrieval, analysis, and visualization, to backtesting and live trading.