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Top 23 Python Crypto Projects
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ccxt
A JavaScript / TypeScript / Python / C# / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Crypto-Signal
Github.com/CryptoSignal - Trading & Technical Analysis Bot - 4,100+ stars, 1,100+ forks
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blankly
🚀 💸 Easily build, backtest and deploy your algo in just a few lines of code. Trade stocks, cryptos, and forex across exchanges w/ one package.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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TradingView-Webhook-Bot
📊 Send TradingView alerts to Telegram, Discord, Slack, Twitter and Email.
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algobot
Cryptocurrency trading bot with a graphical user interface with support for simulations, backtests, optimizations, and running live bots.
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AutoTrader
A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.
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intelligent-trading-bot
Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering
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defi
Tools for use in DeFi. Impermanent Loss calculations, staking and farming strategies, coingecko and pancakeswap API queries, liquidity pools and more (by gauss314)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Project mention: JavaScript Libraries for Implementing Trendy Technologies in Web Apps in 2024 | dev.to | 2024-04-09CCXT
have you seen the https://openbb.co/ project? an open source Bloomberg Terminal project you may find interesting ;-)
Project mention: hummingbot: NEW Extended Research - star count:6122.0 | /r/algoprojects | 2023-08-06
Project mention: algotrading: NEW Extended Research - star count:858.0 | /r/algoprojects | 2023-06-10
I agree that the conventional (numeric) forecasting can hardly benefit from the newest approaches like transformers and LLMs. I made such a conclusion while working on the intelligent trading bot [0] by experimenting with many ML algorithms. Yet, there exist some cases where transformers might provide significant advantages. They could be useful where the (numeric) forecasting is augmented with discrete event analysis and where sequences of events are important. Another use case is where certain patterns are important like those detected in technical analysis. Yet, for these cases much more data is needed.
[0] https://github.com/asavinov/intelligent-trading-bot Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering
There are 3 courses that I usually recommend to folks looking to get into MLE/MLOps that already have a technical background. The first is a higher-level look at the MLOps processes, common challenges and solutions, and other important project considerations. It's one of Andrew Ng's courses from Deep Learning AI but you can audit it for free if you don't need the certificate: - Machine Learning in Production For a more hands-on, in-depth tutorial, I'd recommend this course from NYU (free on GitHub), including slides, scripts, full-code homework: - Machine Learning Systems And the title basically says it all, but this is also a really good one: - Hands-on Train and Deploy ML Pau Labarta, who made that last course, actually has a series of good (free) hands-on courses on GitHub. If you're interested in getting started with LLMs (since every company in the world seems to be clamoring for them right now), this course just came out from Pau and Paul Iusztin: - Hands-on LLMs For LLMs I also like this DLAI course (that includes Prompt Engineering too): - Generative AI with LLMs It can also be helpful to start learning how to use MLOps tools and platforms. I'll suggest Comet because I work there and am most familiar with it (and also because it's a great tool). Cloud and DevOps skills are also helpful. Make sure you're comfortable with git. Make sure you're learning how to actually deploy your projects. Good luck! :)
Project mention: Mnemonikey | Determinstic PGP key recovery using phrases | v0.0.1 prerelease published | /r/GnuPG | 2023-06-07It doesn't support signing and authentication subkeys (But maybe it will soon!).
Python Crypto related posts
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Learn and Test DMARC
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paradigm-data-portal: NEW Data - star count:223.0
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paradigm-data-portal: NEW Data - star count:223.0
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paradigm-data-portal: NEW Data - star count:223.0
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Nado Version Release 0.26
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algotrading: NEW Extended Research - star count:858.0
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api: NEW Data - star count:120.0
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A note from our sponsor - InfluxDB
www.influxdata.com | 7 May 2024
Index
What are some of the best open-source Crypto projects in Python? This list will help you:
Project | Stars | |
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1 | ccxt | 31,417 |
2 | OpenBBTerminal | 26,121 |
3 | hummingbot | 7,329 |
4 | jesse | 5,250 |
5 | Crypto-Signal | 4,716 |
6 | cryptofeed | 2,072 |
7 | blankly | 1,973 |
8 | alpaca-trade-api-python | 1,687 |
9 | pybroker | 1,610 |
10 | TradingView-Webhook-Bot | 1,126 |
11 | featherduster | 1,058 |
12 | pycoingecko | 1,033 |
13 | algotrading | 1,000 |
14 | rate.sx | 966 |
15 | algobot | 893 |
16 | uniswap-python | 883 |
17 | AutoTrader | 856 |
18 | intelligent-trading-bot | 745 |
19 | lumibot | 696 |
20 | hands-on-train-and-deploy-ml | 660 |
21 | trezor-agent | 559 |
22 | tstock | 556 |
23 | defi | 540 |
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