Overview of My Submission

Crypto Bot is a collection of micro services which uses algorithmic trading to generate the BUY / SELL Signals based on the data received from the crypto market (Binance API)

the bot uses the pubsub to publish the live crypto prices to the strategy micro service and redis json to store the orders data and the micro services communicate via gRPC with each other

custom strategies can be writen under the strategy-service/strategies

an example ema.py (Exponential Moving Average) strategy is given in the repo

Submission Category: Microservice

Language Used: Python, Protobuf

Link to Code

rohit20001221 / trading-bot-binance

Crypto Bot

crypto bot uses algorithmic trading strategies to generate buy and sell signals for trading the crypto currency

How it works

How the data is stored:

    //historical data

    {
        "open": float,
        "high": float,
        "low": float,
        "close": float,
        "volume": float
    }

    // live data

    {
        'open': float,
        'high': float,
        'low': float,
        'close': float,
        'volume': float,
        'is_interval': boolean,
        "symbol": string, // btcusdt
        "interval": string // "5m", "1m", "1h", "1d"
    }

How the data is accessed:

# subscribing to the live data service
REDIS> SUBSCRIBE <channel-name>

# reading historical data
REDIS> JSON.GET historical-<tradingsymbol> '$'

How to run it locally?

Prerequisites

1. python
2. gRPC
3. Redis Stack Server
4. Docker
5. Binance API Key and Secret
6. Pushbullet Access Token

Local installation

to start the…

View on GitHub

Additional Resources / Info

Collaborators

@rohit20001221

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• Check out Redis OM, client libraries for working with Redis as a multi-model database.
• Use RedisInsight to visualize your data in Redis.
• Sign up for a free Redis database.

Comments

[jiwoh]

This is a well-structured project. I like how you’ve used gRPC for microservice communication and Redis JSON with pub/sub for handling live and historical trading data. The modular approach with a base EMA strategy and support for custom strategies under strategy-service/strategies makes it very extensible. Clear data models for both historical and live market data are also a plus. While exploring algorithmic trading, I’ve also seen discussions around best crypto scam recovery, so adding proper risk disclaimers or guidance for safe usage might be useful. To improve, you could add easier setup instructions (like docker-compose) and maybe provide sample backtesting results for more credibility. Overall, it’s a solid foundation for building and experimenting with algorithmic crypto trading.

[annie lopez]

I recently tried converting some of my XMR and was really impressed with how smooth the whole Monero to Ethereum swap went. It's awesome having the flexibility to move between a privacy-focused coin like Monero and a widely used platform like Ethereum, especially when you're exploring different DeFi options or just diversifying your crypto portfolio. Definitely worth checking out if you're looking to balance privacy with broader utility!