Quant Development Process
Client Requirements Brief
FieldDetails
Strategy TypeTrend Following
PlatformBinance IBKR
Asset ClassesCrypto Perps US Stocks
Max Drawdown≤ 12% total portfolio
Target Sharpe> 1.8 annualised
Delivery8–10 weeks · Full deployment
Step 01 of 07
Requirement
Discovery

Every quant system starts with rigorous requirements gathering — defining the strategy edge, constraints, platforms, risk limits, and success criteria before any design begins.

Free deep-dive consultation
Strategy edge & hypothesis definition
Platform, instrument & risk scoping
Technical proposal within 24 hours
Trading System Architecture
📡 Market Data Feed
🗄️ Time-Series DB
⚙️ Strategy Engine — Signal Generation
🛡️ Risk Engine
📐 Position Sizer
⚡ Order Management & Execution Engine
Binance API
|
IBKR API
|
IG API
Step 02 of 07
System
Architecture

We design the full trading system architecture before writing code — defining every component, data flow, and integration point to ensure a robust, scalable production system.

Market data & database design
Strategy & risk engine architecture
Order management system design
Multi-platform execution layer
Data Infrastructure
📊ohlcv_data
PKidBIGINT
symbolVARCHAR
timestampTIMESTAMPTZ
closeDECIMAL
📋orders
PKorder_idUUID
symbolVARCHAR
sideENUM
statusENUM
💼positions
symbolVARCHAR
qtyDECIMAL
unrealised_pnlDECIMAL
📈signals
symbolVARCHAR
signalENUM
confidenceFLOAT
📡
Binance API
🔄
ETL Pipeline
🗄️
TimescaleDB
⚙️
Strategy
Step 03 of 07
Data
Infrastructure

Institutional-grade data pipelines — real-time ingestion, clean storage, and fast retrieval. TimescaleDB optimised for time-series trading data at high frequency.

Real-time market data ingestion
TimescaleDB time-series storage
OHLCV, orders, positions schema
Historical data for backtesting
strategy_engine.py
Python 3.11
strategy_engine.py AI-Assisted ✓
class TrendStrategy(BaseStrategy): """EMA Crossover + ATR position sizing""" def generate_signal(self, df): fast = df['close'].ewm(12).mean() slow = df['close'].ewm(26).mean() signal = 1 if fast > slow else -1 atr = compute_atr(df, period=14) size = self.risk.kelly_size( signal=signal, atr=atr ) return Order(side=signal, qty=size) # Validated by Claude AI · ChatGPT
Built with: 🤖 Claude 💬 ChatGPT ⚡ AI Agents
Step 04 of 07
Strategy
Development

Clean, modular Python code built to institutional standards — accelerated by Claude AI and ChatGPT for faster delivery, fewer bugs, and comprehensive documentation.

Python · institutional grade
AI-assisted with Claude & ChatGPT
Full API integration & error handling
Modular, documented, maintainable
Backtest Report · 2021–2024
TOTAL RETURN
+247%
SHARPE RATIO
2.14
MAX DRAWDOWN
−8.3%
WIN RATE
58.4%
Equity Curve — Walk-Forward Validated
Step 05 of 07
Backtesting
& Validation

Rigorous walk-forward backtesting with realistic assumptions — slippage, maker/taker fees, funding costs. No lookahead bias. Paper trading validation before any live capital is deployed.

Historical data backtesting
Walk-forward out-of-sample test
Full transaction cost modelling
Paper trading before live capital
Live System · All Platforms
Binance
Perpetuals · Futures
LIVE
Pos: BTCUSDT
PnL: +2.3%
Interactive Brokers
US Stocks · Options
LIVE
Pos: AAPL, NVDA
PnL: +1.1%
IG Platform
CFD · Spread Betting
LIVE
Mode: CFD
PnL: +0.8%
☁️ Cloud Server AWS EC2 · 24/7
📊 Monitoring Grafana Dashboard
🔔 Alerts Telegram · Email
Step 06 of 07
Live
Deployment

Production deployment across all platforms simultaneously — 24/7 cloud operation, real-time Grafana monitoring dashboard, Telegram alerts, and emergency stop controls.

AWS cloud · 24/7 uptime
Grafana monitoring dashboard
Telegram & email alerting
Emergency stop controls
Post-Deployment Support
🔧
Bug Fixes & Patches

Priority bug fixes, hotpatching, and API change handling as platforms update.

📈
Strategy Optimisation

Ongoing parameter tuning, signal refinement, and strategy evolution as markets change.

📊
Performance Reporting

Monthly P&L reports, attribution analysis, and drawdown review with recommendations.

🚀
New Feature Builds

Add new instruments, strategies, or platform integrations as your needs evolve.

Recent Support Activity
Binance API rate limit patch deployed
2h ago
Done
Monthly P&L report generated & sent
1d ago
Done
IBKR TWS version upgrade in progress
3d ago
Active
Ongoing
Support

Our relationship doesn't end at deployment. We provide ongoing support — monitoring, optimisation, reporting, and new feature development as your trading operation grows.

Priority bug fixes & API updates
Monthly performance reporting
Strategy refinement & tuning
New instruments & platform adds
Requirements
Architecture
Database
Development
Backtesting
Deployment
Support
0:00
Requirement Discovery
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