IBKR Trading Bot Hub + AI Interview Preparation Platform

Complete Quant Finance Career Development System

Product Overview

A comprehensive, integrated platform combining a production-quality trading system codebase with AI-powered interview preparation and career coaching, designed to transform you from candidate to hired quant professional at top-tier hedge funds and trading firms.

Core Components

1. IBKR Trading Bot Hub (The Foundation)

Multi-asset algorithmic trading platform

• Real-time market data processing (equities, forex, commodities, futures)

• 4 production trading strategies implemented:

• NVDA SMA Crossover (trend-following)

• EUR/USD RSI (mean reversion)

• BHP ASX SMA (international equities)

• XAU/USD Bollinger (commodity momentum)

• Hub-and-spoke WebSocket architecture

• Claude AI integration for trade confirmation

• Complete order management and position tracking

• Simulated execution and P&L calculation

2. AI-Powered Interview Preparation Suite (The Innovation)

Claude Code + VS Code Integration

• Generates 500+ technical interview questions directly from the codebase

• Role-play mock interviews with firm-specific scenarios

• Real-time strategy discussion coaching

• Code review and feedback from AI "senior researchers"

3. Firm-Specific Preparation Modules

Citadel & Citadel Securities

• Probability and statistics deep dives

• Advanced coding challenges

• Systematic trading strategy frameworks

• Questions specifically calibrated to Citadel's rigorous style

Two Sigma

• Systems design interviews

• Production-quality code expectations

• Large-scale data pipeline architecture

• ML model evaluation frameworks

Renaissance Technologies

• Scientific methodology in financial markets

• Pattern recognition and signal processing

• Research scientist interview formats

• Abstract mathematical reasoning

Jump Trading

• Ultra-low-latency systems design

• C++ optimization and performance

• FPGA programming concepts

• Market microstructure at HFT scale

Crypto & DeFi Funds

• DeFi protocol mechanics (AMMs, lending, derivatives)

• On-chain data analysis

• MEV extraction strategies

• Smart contract security

4. Career Development Features

Real-Time Coaching

• Resume optimization for quant roles

• Cover letter personalization by firm

• Behavioral question preparation

• Salary negotiation guidance

Compensation Intelligence

• New York: $250K-$400K+ for junior roles → $5M-$50M+ for principals

• London: £120K-£250K+ → £800K-£8M+

• Chicago: $200K-$350K+ → $500K-$3M+

• Crypto-focused roles: 10-30% premium over traditional

• 30-year career trajectory modeling

Interview Timeline Planning

• 4-week to 12-week preparation schedules

• Daily task breakdowns

• Progress tracking

• Weakness identification and targeted drilling

Key Features

Codebase-Driven Learning

• Every interview question grounded in real, working code

• Understand how production systems actually work

• Discuss implementation trade-offs like a real researcher

• Not abstract problems — real engineering decisions

Interactive Strategy Development

• Role-play as a quant researcher pitching to CIO/risk committee

• Receive challenging follow-up questions from AI "senior researchers"

• Discuss mean reversion, stat arb, options trading, market making

• Get rated on clarity, rigor, and practical awareness

Multi-Model AI Assistance

• Claude Opus (most powerful): Complex mathematical derivations, final mock interviews

• Claude Sonnet (balanced): Standard prep, code review, strategy discussion

• Claude Haiku (fastest/cheapest): Rapid practice, brain teasers, quick reference

Production-Ready Code Examples

# Mean reversion strategy with risk constraints

class MeanReversionStrategy:

    def __init__(self, lookback=5, max_position_pct=0.02):

        self.lookback = lookback

        self.positions = {}

    def generate_signals(self, residuals, date_idx):

        # Real implementation included

        pass

# Gamma scalping with Greeks management

class GammaScalper:

    def compute_residuals(self, returns, factor_returns):

        # Full Fama-French factor adjustment

        pass

Real-World Scenarios

• Handling March 2020 volatility crash

• Managing pin risk at options expiration

• Navigating cross-chain arbitrage

• Responding to MEV competition

• Building risk management frameworks

Learning Outcomes

After Using This System, You Will:

Technical Mastery

• Understand real production trading system architecture

• Master algorithmic trading strategies and their implementation

• Learn how to evaluate strategy quality rigorously

• Understand market microstructure at multiple timescales

Interview Readiness

• Answer 500+ interview questions with confidence

• Conduct mock interviews indistinguishable from real ones

• Discuss complex strategies with a senior quant researcher

• Defend implementation choices under scrutiny

Career Positioning

• Know market compensation across all major hubs

• Understand career progression at top firms

• Negotiate effectively from an informed position

• Target the right firm for your skill set

Soft Skills

• Communicate complex ideas clearly under pressure

• Demonstrate intellectual honesty about limitations

• Ask insightful follow-up questions

• Think creatively about edge cases and risks

System Requirements

Software

• VS Code (free, all platforms)

• Python 3.10+ or Anaconda (free)

• Claude Code CLI (free with Claude subscription)

• Git (free)

Claude Access

• Option 1: Claude Pro ($20/month) — sufficient for casual prep

• Option 2: Claude API (pay-per-use) — ~$30-100 for full prep cycle

• Option 3: Claude Max ($100-200/month) — unlimited usage

Hardware

• 4GB+ RAM (8GB recommended)

• ~2GB disk space

• Stable internet connection

• Any OS (Windows, macOS, Linux)

Typical Usage Flow

Week 1: Foundation Building

Day 1-2: Probability & Statistics review with AI tutor

Day 3-4: Data structures & algorithms practice with code review

Day 5: Stochastic calculus deep dive

Day 6-7: Full mock interview (general quant topics)

Week 2: Strategy Development

Day 8-9: Implement & discuss mean reversion strategy

Day 10-11: Options pricing & Greeks workshop

Day 12-13: Market microstructure & HFT concepts

Day 14: Full mock interview (strategy focus)

Week 3: Firm-Specific Preparation

Day 15-17: Target Firm #1 specific prep

Day 18-19: Target Firm #2 specific prep

Day 20: System design deep dive

Day 21: Review weak areas

Week 4: Polish & Final Prep

Day 22-23: Full-length timed mock interviews

Day 24-25: Behavioral questions & culture fit

Day 26-27: Brain teasers & probability practice

Day 28-29: Light maintenance

Day 30: Rest day before real interview