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What You Can Do (Logged In)

Configure strategy parameters for trend, mean reversion, and scalping bots
Run backtests with 30+ days of historical data
View performance metrics dashboard with real-time bot status
Set up strategy performance alerts via email or webhook
Use A/B testing framework to compare strategy variants
Deploy paper trading bots with virtual balance

How It Works

Our automated trading engine uses Python with CCXT library for exchange connectivity. Strategies run on Celery task queues with Redis for real-time execution. Machine learning layer with TensorFlow/PyTorch is available for predictive models.

Backtesting
Engine

Performance
Metrics

Strategy
Alerts

H13 Automated Trading Platform

Professional-grade algorithmic trading bots for cryptocurrency markets — built for learning, testing, and mastering automated strategies.

🤖 Algorithmic Trading

How Automated Trading Works

Flow Demo

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📱 Mobile Flow

Auto Trading · 7 Steps

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What We've Built

H13 AutoTrader is a standalone automated trading machine that communicates with our platform via secure APIs. It includes three professional trading strategies:

Trend Following - MA crossovers + RSI signals
Mean Reversion - Bollinger Bands strategy
Scalping - High-frequency short-term trades

Key Features

Backtesting with 30+ days historical data
Real-time performance metrics dashboard
Email & webhook strategy alerts
A/B testing framework for strategy comparison
Paper trading bots with virtual balance

REAL vs VIRTUAL Trading — What's the Difference?

REAL Trading

Uses your actual virtual balance from the platform. When a bot executes a trade, it calls the platform's /deduct or /add API — just like manual trading. Every transaction is recorded and affects your real platform balance.

✅ Same API calls as manual trading
✅ Affects your actual portfolio
✅ Real profit/loss tracking

PAPER Trading (Virtual)

Uses a separate simulated balance (starting $100,000). Trades are executed with the same logic but DO NOT affect your real platform balance. Perfect for testing strategies risk-free.

✅ Same bot logic as real trading
✅ No risk to your balance
✅ Learn before deploying live

Key Insight: The ONLY difference between REAL and PAPER trading is which balance the bot uses. Both use identical strategy logic, API calls, and execution paths. This means your paper trading results accurately predict real performance.

Risk-Free Learning

Test strategies with paper trading before committing real funds. Our paper bots use identical logic to real trading.

Data-Driven Decisions

Backtest strategies on 30+ days of historical market data to see how they would have performed.

A/B Testing Framework

Compare two strategies side-by-side to identify which performs better in current market conditions.

 Technical Note: Standalone Machine ArchitectureThis Automated Trading system runs as its own standalone machine (Port 5011) and communicates with the H13 platform only via secure APIs. No direct database access. No file manipulation. Each trade request is validated, rate-limited, and logged — ensuring complete isolation from core platform systems.

Getting Started

Configure your strategy parameters (MA periods, RSI thresholds, position sizing)
Run a backtest with 30+ days of historical data to validate performance
Deploy a Paper Trading bot to test live market conditions with virtual funds
Set up alerts to monitor bot performance via email or webhook
Use A/B testing to compare strategy variants before scaling up

Educational Note: This is a complete algorithmic trading learning environment. You can modify parameters, test hypotheses, and observe how automated strategies behave in different market conditions — all without risking real capital.

Strategy Comparison

Strategy	Best For	Key Indicators	Timeframe	Risk Level
Trend Following	Trending markets	MA Crossover, RSI	1h - 4h	Moderate
Mean Reversion	Range-bound markets	Bollinger Bands	15m - 1h	Low to Moderate
Scalping	High volatility	Order Book, Spread	1m - 5m	High

Educational Purpose Only: This is a learning tool. Past backtest performance does not guarantee future results. Always validate strategies thoroughly before using with real funds.

Technical Features

Python with CCXT library
Celery/Redis task queuing
TensorFlow/PyTorch ML layer

Historical data backtester
Multiple strategy support
A/B testing framework