0.1 — Project 2: Live Paper Trading Bot#

Extend your backtesting engine into a real-time trading bot that runs continuously, consumes live market data, generates signals, and places paper (simulated) orders via a broker API.

Objective#

Build a bot that runs continuously, generates signals from live data, places paper trades through an API, and tracks performance in real time.

Focus Areas#

• API integration (market data and orders)
• Order execution and lifecycle
• Real-time market data (REST and/or WebSockets)
• Handling market microstructure

Technologies#

• Python — Core implementation
• Alpaca API or Interactive Brokers API — Paper trading and market data
• WebSockets / REST APIs — Real-time data and order submission
• Scheduling and event loops — Running the bot (e.g., schedule, asyncio, or a simple loop)

New Concepts#

• Bid/ask spreads — Trading at mid vs. crossing the spread
• Slippage — Price movement between signal and fill
• Latency — Impact of delay on execution quality
• Order types — Market, limit, stop; when to use each
• Risk management — Position limits, daily loss limits, circuit breakers

Deliverable#

A bot that:

• Runs continuously (or on a schedule) during market hours
• Subscribes to or polls live market data
• Generates signals using logic similar to (or evolved from) your backtester
• Places paper orders via the broker API
• Tracks open positions and P&L in real time
• Logs activity and supports graceful shutdown

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Prerequisites (with links to lessons)#

Topic	Why you need it	Where to learn
Project 1 — Backtester	Signal logic and performance mindset	Project 1: Backtesting Engine
Python & Pandas	Data handling, time series	Python & Pandas
REST / APIs	Calling broker endpoints	General dev experience; Networking (stub) for concepts
Concurrency / async	Event loops, non-blocking I/O	Concurrency & Multithreading (stub)
Probability / risk	Interpreting live P&L and risk	Quant Research

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Steps to Complete the Project#

1. Choose a broker and get paper credentials
   Sign up for paper trading with Alpaca or Interactive Brokers. Obtain API keys (and optionally WebSocket URLs). Store credentials in environment variables or a local config file (never commit secrets).

2. Verify API access
   Write a small script to authenticate and fetch account info, then fetch recent quotes or bars for one symbol. Confirm you can read market data and account state.

3. Design the bot loop
   Decide how often to run (e.g., every 1 minute, or event-driven via WebSocket). Implement a main loop that: fetches or receives market data, runs your signal logic, and optionally submits/cancels orders. Handle exceptions and log errors.

4. Connect your backtester signal logic
   Reuse or adapt the indicator and signal code from Project 1. Input: latest bars or quote. Output: desired position or explicit buy/sell/hold. Keep the logic identical at first so you can compare paper results to backtest.

5. Implement order placement
   Translate signals into orders (market or limit). Enforce risk rules (e.g., max position size, one open order at a time). Track order IDs and poll or use callbacks for fill status.

6. Track positions and P&L
   Maintain current positions and cost basis. Update from fills and from periodic account/position API calls. Compute unrealized and realized P&L; log or display in a simple dashboard or file.

7. Add robustness
   Handle disconnects (reconnect WebSocket or retry REST). Add a graceful shutdown (e.g., cancel open orders, flush logs). Consider a daily loss limit or max drawdown check that pauses the bot.

8. Document and run
   README: how to install, configure, and run the bot; how to interpret logs. Run in paper mode for at least a few days and compare behavior to your backtest expectations.

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Hints (with backlinks)#

• Paper vs. live — Use only paper/sim endpoints and paper keys. Double-check that you’re not pointing to live trading. Quant Research and risk lessons (stub) reinforce the importance of testing before real capital.
• Idempotency and order state — Track pending orders so you don’t double-submit. Use order IDs and status from the API to know when to place the next trade. Concurrency (stub) for state and race conditions.
• Latency and slippage — In paper mode, fills may be optimistic. Note the timestamp of your signal vs. fill; later you can model slippage. Networking (stub) for latency concepts.
• Market hours — Only trade when the market is open (and optionally avoid first/last minutes). Use the broker’s market-hours API or a known schedule.
• Logging — Log every signal, order submit, fill, and position change with timestamps. Makes debugging and comparison to backtest much easier.

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Goals#

By the end of this project you should be able to:

• Authenticate and use a broker’s paper API for data and orders
• Run a signal loop that consumes live data and places paper orders
• Track positions and P&L in real time
• Handle errors and disconnects and shut down cleanly
• Use this infrastructure as the execution layer for Project 3 (ML strategy)

Next step: Project 3 — Machine Learning Strategy, where you’ll add predictive models and feed their signals into this bot.