Quantitative Finance

ACTIVE STREAM

Master high-frequency trading, risk analysis, and algorithmic execution.

Projects

Project 1: The Market Maker

Become the liquidity provider. Build a Limit Order Book, match trades, and manage the spread.

Project 2: The Arbitrageur

Find free money. Detect and exploit pricing inefficiencies in Forex markets.

2.1 Forex Basics

2.1.1 Pairs 2.1.2 Inverse 2.1.3 Cross Rates 2.1.4 Pips 2.1.5 Bid/Ask

2.2 Triangular Arb

2.2.1 The Triangle 2.2.2 Graph 2.2.3 Log Transform 2.2.4 Bellman-Ford 2.2.5 The Path

2.3 Execution

2.3.1 Latency 2.3.2 Slippage 2.3.3 Fees 2.3.4 Sizing 2.3.5 Risk Free?

2.4 Stat Arb

2.4.1 Pairs Trading 2.4.2 Cointegration 2.4.3 Z-Score 2.4.4 Signal 2.4.5 Half-Life

2.5 Capstone: The Scanner

2.5.1 Live Feed 2.5.2 Scan 2.5.3 Execute 2.5.4 Logging 2.5.5 Speed

Project 3: The Portfolio Optimizer

Don't put all eggs in one basket. Modern Portfolio Theory and the Efficient Frontier.

3.1 Returns & Stats

3.1.1 Daily Returns 3.1.2 Log Returns 3.1.3 Expected Return 3.1.4 Volatility 3.1.5 Annualization

3.2 Covariance

3.2.1 Covariance 3.2.2 Correlation 3.2.3 Cov Matrix 3.2.4 Portfolio Risk 3.2.5 Diversification

3.3 Efficient Frontier

3.3.1 Random Search 3.3.2 Sharpe Ratio 3.3.3 Optimization 3.3.4 Constraints 3.3.5 The Solution

3.4 CAPM

3.4.1 Beta 3.4.2 Alpha 3.4.3 SML 3.4.4 Market Neutral 3.4.5 Fama-French

3.5 Capstone: Robo-Advisor

3.5.1 User Profile 3.5.2 Allocation 3.5.3 Selection 3.5.4 Rebalancing 3.5.5 Tax Harvesting

Project 4: The Option Pricer

Master derivatives. Black-Scholes, Greeks, and hedging strategies.

4.1 Option Basics

4.1.1 Payoff 4.1.2 Value 4.1.3 Moneyness 4.1.4 Parity 4.1.5 Strategies

4.2 Black-Scholes

4.2.1 d1 and d2 4.2.2 N(x)4.2.3 The Formula 4.2.4 Implied Vol 4.2.5 Vol Smile

4.3 The Greeks

4.3.1 Delta 4.3.2 Gamma 4.3.3 Theta 4.3.4 Vega 4.3.5 Rho

4.4 Binomial Tree

4.4.1 Tree Setup 4.4.2 Build Tree 4.4.3 Backward 4.4.4 American 4.4.5 Convergence

4.5 Capstone: Delta Hedging

4.5.1 Portfolio 4.5.2 Hedge 4.5.3 Move 4.5.4 Re-hedge 4.5.5 Cost

Project 5: The Algo Trader

Automate your edge. Build a backtesting engine and test technical strategies.

5.1 Data Handling

5.1.1 OHLCV 5.1.2 Resampling 5.1.3 Cleaning 5.1.4 Returns 5.1.5 Storage

5.2 Indicators

5.2.1 SMA 5.2.2 EMA 5.2.3 RSI 5.2.4 Bollinger 5.2.5 MACD

5.3 Backtester

5.3.1 Event Loop 5.3.2 Signals 5.3.3 Positions 5.3.4 Execution 5.3.5 Equity Curve

5.4 Strategies

5.4.1 Crossover 5.4.2 Mean Reversion 5.4.3 Breakout 5.4.4 Momentum 5.4.5 Stop Loss

5.5 Capstone: Optimization

5.5.1 Param Sweep 5.5.2 Overfitting 5.5.3 Walk Forward 5.5.4 Ranking 5.5.5 Report

Project 6: The Risk Manager

Survive the crash. Value at Risk (VaR), Monte Carlo simulations, and stress testing.

6.1 VaR Basics

6.1.1 Definition 6.1.2 Historical VaR 6.1.3 Parametric VaR 6.1.4 CVaR 6.1.5 Time Scaling

6.2 Monte Carlo

6.2.1 GBM 6.2.2 Many Paths 6.2.3 Distribution 6.2.4 MC VaR 6.2.5 Convergence

6.3 Stress Testing

6.3.1 Scenarios 6.3.2 Sensitivity 6.3.3 History 6.3.4 Corr Breakdown 6.3.5 Reverse

6.4 Credit Risk

6.4.1 PD 6.4.2 LGD 6.4.3 EAD 6.4.4 Credit VaR 6.4.5 CDS

6.5 Capstone: Risk Report

6.5.1 Load 6.5.2 Agg VaR 6.5.3 Marginal 6.5.4 Stress 6.5.5 Hedge

Project 7: The High-Frequency Trader

Speed is everything. Analyze market microstructure, order flow, and tick data.

7.1 Tick Data

7.1.1 Trades & Quotes 7.1.2 Nanoseconds 7.1.3 Tick Bars 7.1.4 Volume Bars 7.1.5 Dollar Bars

7.2 Microstructure

7.2.1 Effective Spread 7.2.2 Realized Spread 7.2.3 Price Impact 7.2.4 Roll Model 7.2.5 VPIN

7.3 Order Flow

7.3.1 OFI 7.3.2 TFI 7.3.3 Prediction 7.3.4 Cross Impact 7.3.5 Queue Position

7.4 Strategies

7.4.1 Avellaneda-Stoikov 7.4.2 Latency Arb 7.4.3 Ignition 7.4.4 Spoofing 7.4.5 Rebate Capture

7.5 Capstone: The Simulator

7.5.1 LOB Sim 7.5.2 Latency 7.5.3 Backtest 7.5.4 Analysis 7.5.5 Optimize

Project 8: The Sentiment Analyzer

Read the market's mind. Use NLP to trade on news, tweets, and earnings calls.

8.1 NLP Basics

8.1.1 Tokenization 8.1.2 Stop Words 8.1.3 Bag of Words 8.1.4 TF-IDF 8.1.5 Embeddings

8.2 Sentiment Scoring

8.2.1 Dictionary 8.2.2 VADER 8.2.3 BERT 8.2.4 Aspect Based 8.2.5 Aggregation

8.3 Data Sources

8.3.1 Scraping 8.3.2 Social Media 8.3.3 Earnings Calls 8.3.4 SEC Filings 8.3.5 Volume

8.4 Trading Signals

8.4.1 Event Study 8.4.2 Correlation 8.4.3 Noise 8.4.4 Contrarian 8.4.5 Momentum

8.5 Capstone: News Trader

8.5.1 Pipeline 8.5.2 Backtest 8.5.3 Word Cloud 8.5.4 Topics 8.5.5 Real-Time

Project 9: The Crypto Structurer

DeFi and Smart Contracts. Build Automated Market Makers (AMMs) and analyze tokenomics.

9.1 Blockchain

9.1.1 Hashing 9.1.2 Wallets 9.1.3 Ledger 9.1.4 Gas 9.1.5 Contracts

9.2 AMM Math

9.2.1 CPMM 9.2.2 Swap 9.2.3 Impact 9.2.4 LP Tokens 9.2.5 IL

9.3 Protocols

9.3.1 Lending 9.3.2 Liquidation 9.3.3 Flash Loans 9.3.4 Staking 9.3.5 Governance

9.4 MEV

9.4.1 Front Running 9.4.2 Sandwich 9.4.3 Atomic Arb 9.4.4 Gas Wars 9.4.5 Private Txs

9.5 Capstone: The DEX

9.5.1 Create Pool 9.5.2 Add Liq 9.5.3 Swap 9.5.4 Remove Liq 9.5.5 Dashboard

Project 10: The Reinforcement Learner

AI that learns to trade. Q-Learning, Deep RL, and policy gradients.

10.1 RL Basics

10.1.1 Environment 10.1.2 State 10.1.3 Action 10.1.4 Reward 10.1.5 Exploration

10.2 Q-Learning

10.2.1 Q-Table 10.2.2 Bellman 10.2.3 Discretization 10.2.4 Training 10.2.5 Eval

10.3 Deep Q-Network

10.3.1 Network 10.3.2 Replay 10.3.3 Target Net 10.3.4 Loss 10.3.5 Train DQN

10.4 Advanced RL

10.4.1 Policy Gradient 10.4.2 Continuous 10.4.3 Curriculum 10.4.4 Risk Penalty 10.4.5 Generalization

10.5 Capstone: AlphaZero

10.5.1 Setup 10.5.2 Train 10.5.3 Learning Curve 10.5.4 Test 10.5.5 Deploy

Total Projects

Estimated Time

40+ hrs

Difficulty Range

BeginnerIntermediateAdvanced

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