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Founding Project Python AI + Automation

Autonomous Trading Bot
& Market Scanner

A fully autonomous Python trading system that monitors live markets, backtests strategies, and executes trades around the clock — without human intervention.

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Project Type

Internal / Personal Trading System

Category

Python Automation & AI

The Problem

Modern markets generate enormous amounts of data across hundreds of tickers simultaneously. Manually monitoring even a handful of signals across different timeframes is impractical — by the time a human spots an opportunity, the window has often already closed.

The core challenge was multifaceted:

  • Monitoring dozens of market signals simultaneously across multiple instruments
  • Executing trades automatically based on complex, rule-driven strategy logic
  • Backtesting strategies against historical data before deploying real capital
  • Running continuously 24/7 without requiring manual oversight or intervention
  • Maintaining a performance record and risk controls to prevent catastrophic loss

Off-the-shelf trading platforms lacked the flexibility for truly custom strategy logic. A bespoke system was needed.

The Solution

We built a fully autonomous Python system from the ground up — designed to operate independently as a self-sustaining trading engine. The architecture separates concerns cleanly: market data ingestion, strategy evaluation, order execution, and performance reporting each live in their own module.

The system connects to live market data via the Alpaca API, continuously scans configured instruments, and evaluates each against a library of pluggable strategies. When a signal fires, the execution engine submits the appropriate order automatically and logs the result.

Before any strategy touches live capital, it runs through the backtesting engine against historical price data — giving a clear statistical picture of expected win rate, drawdown, and return characteristics. Scheduled via cron for fully hands-off operation, with a performance dashboard for monitoring without needing to touch the code.

Key Features

📡

Live Market Scanner

Continuously polls live price data across configured tickers, evaluating technical indicators and signal conditions in real time.

📊

Backtesting Engine

Runs any strategy against years of historical OHLCV data to produce win rate, max drawdown, Sharpe ratio, and cumulative return metrics before going live.

🧠

Strategy Manager

Pluggable strategy architecture supports momentum, mean reversion, breakout, and custom logic. Strategies can be toggled, adjusted, and added without touching the core engine.

Automated Execution

When a strategy fires a signal, the execution engine submits market or limit orders via the Alpaca API automatically — no human action required.

📈

Performance Dashboard

Matplotlib-powered charts and an SQLite trade log provide a full picture of P&L, trade history, and strategy performance over any time period.

🛡️

Risk Controls

Configurable position sizing, max daily loss limits, and stop-loss logic prevent runaway losses. The system halts trading automatically if risk thresholds are breached.

Tech Stack

Python Pandas NumPy Alpaca API Matplotlib SQLite Cron

Results

24/7

Continuous Operation

15+

Strategies Backtested

100%

Automated Execution

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Whether you need a trading system, data pipeline, or any kind of automation — I build it right and make it last.

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