Disclaimer: This video series and files are all for educational purposes only. High-frequency trading involves significant risk. The strategies discussed involve complex financial modeling and hardware requirements. Always do your own research.

Note this complete download with source and video files totals 3,2 gb in size!

Course Title: Advanced HFT & Quantitative Strategy Development: From AI to C++

Course Description:
This comprehensive video series takes you behind the curtain of institutional High-Frequency Trading (HFT). Moving far beyond retail technical analysis, this course constructs a complete quantitative workflow—from setting up institutional data feeds and utilizing AI for strategy generation to building ultra-low latency execution engines in C++.

Students will explore a multi-language approach, utilizing JavaScript and Python for rapid prototyping and backtesting, before transitioning to C++20 for the development of a high-performance market-making bot. The curriculum covers sophisticated mathematical frameworks including the Avellaneda-Stoikov model, Order Flow Imbalance (OFI), Kalman Filters, and Deep Reinforcement Learning. Included with the course are the relevant source files and PDF documentation to assist in your study.

⚠️ Important Caveats & Prerequisites:

• Advanced Level: This course involves complex financial modeling and advanced programming techniques. It is assumed all consumers possess a strong understanding of these topics.
• Data Limitations: While sample HFT high-frequency CSV data is provided, there is no source code provided for live market data connectivity nor live order execution.
• Simulation Only: All data generated within the C++ program demonstrations is randomly generated simulation data used for educational stress-testing.
•  

Video Summaries

Video 1: HFT Data Workflow & AI-Driven Strategies
This introductory session establishes the infrastructure for HFT. It covers connecting MotiveWave to Rithmic for cost-effective CME tick data and moves quickly into advanced theory. You will learn how to leverage AI to generate mathematical models for detecting "quote stuffing" and iceberg orders, and explore the implementation of Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) for predictive modeling.

Video 2: Institutional Architecture & Hardware Acceleration
We tear down the architecture of an industrial-grade Bitcoin HFT system. This video contrasts retail methods with institutional approaches, discussing the necessity of FPGA, Kernel Bypass, and C++ for nanosecond latency. Topics include Smart Order Routing, the Spooner market-making model, and using machine learning (XGBoost) to grade signal potential on tick-level data.

Video 3: AI-Generated JavaScript Backtester
Focusing on rapid prototyping, this video demonstrates a custom backtesting engine comprising 3,000 lines of AI-generated JavaScript. We test various retail strategies—Momentum, Mean Reversion, and Trend Following—against 1-minute Bitcoin data to analyze win ratios and Sharpe ratios, proving the efficiency of AI-assisted coding for strategy validation.

Video 4: Python & Streamlit Advanced Quant Analysis
Moving to Python, we explore a robust Streamlit application designed for deep quantitative analysis. This video introduces institutional-grade strategies such as Kalman Filters, Hidden Markov Models, and Order Book Imbalance analysis. We visualize opportunity costs, perform Monte Carlo simulations, and use AI to determine optimal capital allocation across portfolios.

Video 5: Building a C++ Market Maker (Avellaneda-Stoikov)
Transitioning to production-level code, this video covers the implementation of a C++ HFT market-making bot running in a Linux (WSL) environment. It details the math behind the Avellaneda-Stoikov model and how to calculate "Reservation Price" to balance inventory risk. The session demonstrates how to use Order Flow Imbalance (OFI) to detect toxic flow and avoid adverse selection.

Video 6: C++ Code Deep Dive & Low-Latency Optimization
A technical deep dive into the 1,700-line C++20 source code. This video explains how to achieve sub-microsecond processing without external libraries. Key topics include memory management (cache line alignment), building lock-free Single Producer Single Consumer (SPSC) queues, compiler optimizations, and using RDTSC for high-resolution timestamping.

Video 7: System Architecture & Visual Strategy Walkthrough
The final video utilizes a visual HTML slide deck to break down the logic of the custom market-making system. It provides a "production-ready" perspective on Linux kernel tuning, CPU thread pinning, and memory alignment. The walkthrough visualizes the interaction between the strategy layer, risk management (kill switches), and the mathematical models driving the bot.

Detailed Videos description

1 hft quant strategy overview pdf course 1

Video Title: HFT Data Workflow: CME Bitcoin, Rithmic API & AI-Driven Quant Strategies

Description:

Welcome to the first section of the file package. In this video, we break down the complete workflow for acquiring, analyzing, and utilizing high-frequency trading (HFT) data. We explore how to set up a cost-effective institutional-grade data feed using Rithmic and MotiveWave, bypassing expensive alternatives like DataBento while accessing the same granular tick data from the CME.

We move beyond basic backtesting and demonstrate how to leverage AI to generate sophisticated mathematical models and trading strategies. From detecting "quote stuffing" and iceberg orders to implementing Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN), this overview covers the mathematical architecture used by top-tier HFT firms.

In this video, you will learn:

• Data Infrastructure: How to connect MotiveWave to Rithmic via Edge Clear/Phillips Capital for affordable CME tick data ($40/month).
• Data Granularity: Analyzing Tick, 1-Minute, and Hourly data for Bitcoin futures (CME) to identify microstructure price discovery.
• Algorithmic Detection: Using AI to generate formulas for detecting quote stuffing, iceberg orders (VWAP), and order flow toxicity.
• Advanced Math Models: A look at Kyle Lambda, Almgren-Chriss, and Ornstein-Uhlenbeck mean reversion processes.
• Machine Learning: Implementing Deep Reinforcement Learning (DQN, PPO) and Graph Neural Networks for predictive modeling.
• Options & Greeks: Utilizing volatility surfaces, Vanna, and Volga for hedging and arbitrage opportunities.

👇 Topics Covered & Timestamps:

• 0:00 - Introduction & Broker/Data Feed Setup (Rithmic vs. DataBento)
• 1:32 - Analyzing CME Bitcoin Data: Tick vs. OHLC
• 3:50 - Using AI to Generate HFT Strategies & Decompose Data
• 5:10 - Detecting Quote Stuffing & Iceberg Orders
• 7:40 - Institutional Algos: Kyle Lambda & Almgren-Chriss
• 10:00 - Risk Management: Order Flow Imbalance (OFI)
• 12:50 - Options Chain Analysis: Implied Volatility & Advanced Greeks
• 15:15 - Co-integration & Kalman Filters for Hedging
• 16:00 - AI Models: Deep Reinforcement Learning (DQN, PPO) & GNNs
• 18:00 - VPIN (Volume-Synchronized Probability of Informed Trading)
• 20:35 - Latency Arbitrage & Cross-Exchange Detection

2 hft quant strategy overview pdf course 1b

🚀 Ready to move beyond retail trading? In this advanced continuation (Part 1b), we tear down the architecture of an institutional-grade High-Frequency Trading (HFT) system for Bitcoin. This isn't just about basic technical analysis; we are diving into the complex mathematical frameworks, execution logic, and hardware acceleration used by top-tier quantitative firms.

We explore how AI and Machine Learning are revolutionizing market microstructure analysis, allowing traders to switch dynamically between strategies like Statistical Arbitrage, Calendar Spreads, and Market Making based on real-time volatility clustering.

In this video, you will learn:

• Advanced Quant Math: How to apply Stochastic Volatility, Dupire models, and Fama-French factor decomposition to Crypto.
• Smart Order Routing: The logic behind "Sniper" liquidity seeking and implementation shortfall algorithms.
• Market Making: Using the Spooner model and Kelly Criterion for optimal Q-positioning and spread capture.
• Ultra-Low Latency Tech: Why nanoseconds matter—moving from Python to C++, FPGA, DPDK, and Kernel Bypass methods.
• AI Signal Generation: Using XGBoost and regime change detection to grade signal potential on tick-level data.

⚠️ Reality Check: We also discuss the barrier to entry—why replicating this fully at an industrial scale often requires a $10M+ infrastructure investment and how you can implement simplified, yet powerful, versions of these strategies today.

👇 Topics Covered & Timestamps:

• 0:00 - Cross-Exchange Arbitrage & Co-integration
• 0:30 - Stochastic Volatility & Strategy Selection (Butterfly vs. Calendar Spreads)
• 1:10 - PCA & Dynamic Factor Loading in Crypto
• 1:55 - AI Cross-Correlation: Spot vs. Futures Lead/Lag relationships
• 3:15 - Smart Order Routing & Sniper Liquidity Algorithms
• 4:20 - Market Making Logic: Spooner Model & Inventory Skewing
• 5:20 - Regulatory Compliance & Risk Controls (Fat Finger Prevention)
• 6:45 - The Tech Stack: FPGA, Kernel Bypass, & Nanosecond Latency
• 8:00 - C++ vs. Python & DPDK Frameworks
• 9:00 - AI Analysis: Grading Bitcoin Tick Data & Volatility Clustering
• 13:20 - Machine Learning Signals (XGBoost) & Regime Change
• 14:15 - The Institutional Reality: $10M Infrastructure vs. Retail

Disclaimer: This video is for educational purposes only. High-frequency trading involves significant risk. The strategies discussed involve complex financial modeling and hardware requirements. Always do your own research.

3 hft quant strategy backtest javascript 2a

Video Title: AI-Generated JavaScript Backtester: Testing Bitcoin Strategies (Trend vs. Mean Reversion)

Description:

Is manual coding obsolete? In this video, we explore a custom-built backtesting engine generated 100% by AI. Comprising nearly 3,000 lines of JavaScript, this tool allows for sophisticated visualization and strategy testing without the weeks of development time usually required.

We put this AI-generated tool to the test using real-world 1-minute Bitcoin data. Given the current flat and sideways market conditions, we compare how different retail strategies—Momentum, Mean Reversion, and Trend Following—perform under pressure.

In this video, you will see:

• The Power of AI Coding: How we generated a full HTML/JS backtesting suite (approx. 3,000 lines) using AI.
• Strategy Stress Tests: Running Momentum, Breakout, and Volatility strategies on difficult, flat Bitcoin price action.
• Comparative Analysis: Why Trend Following outperformed Mean Reversion in our specific data set with a positive Sharpe ratio.
• Forecasting: Using Monte Carlo simulations and Linear Regression to project potential future price bounds.

👇 Topics Covered & Timestamps:

• 0:00 - The End of Manual Coding? AI for Backtesting
• 1:20 - Reviewing the Code: 3,000 Lines of AI-Generated JavaScript
• 2:45 - Loading Data: Simulated vs. Real 1-Minute Bitcoin Data
• 3:50 - Setting Strategy Parameters (Commission, Lookback, Capital)
• 5:30 - Testing Momentum Strategies in a Flat Market (Poor Results)
• 7:10 - Testing Mean Reversion: Slightly Better Performance?
• 7:50 - The Comparison Tool: Trend Following vs. The Rest
• 8:45 - Analyzing Win Ratios & Sharpe Ratios
• 9:50 - Monte Carlo Simulations & Price Forecasting
• 10:50 - Moving from JavaScript to Python

4 hft quant strategy backtest python 2b

Video Title: Python & Streamlit Backtester: Advanced Quant Strategies for Bitcoin

Description:

Moving beyond JavaScript, we step into the power of Python for this second installment of our backtesting series. In this video, I demonstrate a robust, AI-generated Streamlit application designed for deep quantitative analysis and strategy comparison.

While the previous tool focused on retail concepts, this Python framework introduces institutional-grade strategies like Kalman Filters, Hidden Markov Models, and Order Book Imbalance analysis. We test these against real-world Bitcoin data to see if any strategy can beat a simple Buy & Hold in the current market conditions.

In this video, you will see:

• The Tech Stack: How to run this Streamlit application directly from your terminal.
• Advanced Strategies: A look at mean reversion, VWAP, Kalman filtering, volatility surfaces, and liquidity premium extraction.
• The "Buy & Hold" Myth: Visualizing opportunity costs and why holding a losing asset is a portfolio killer.
• Portfolio Optimization: Using AI to determine the optimal capital allocation across multiple strategies to minimize volatility and maximize Sharpe ratios.
• Forecasting: Leveraging Monte Carlo simulations and volatility-adjusted models to predict future price action.

👇 Topics Covered & Timestamps:

• 0:00 - Introduction to the Python Streamlit App
• 1:25 - Strategy Overview: Kalman Filters, Hidden Markov, & Order Book Analysis
• 2:20 - "Extraction" Strategies & High-Frequency Concepts
• 3:35 - Backtesting: Comparing Equity Curves vs. Buy & Hold
• 4:40 - AI-Driven Forecasting & Monte Carlo Simulations
• 5:10 - Portfolio Management: Correlation & Optimal Allocation
• 5:50 - Key Metrics: Sharpe Ratio, Max Drawdown, & Win Ratio
• 7:40 - Live Demo: Running the App & Loading Data
• 9:10 - Results Analysis: Why VWAP Outperformed in a Down Market
• 11:00 - The Reality of Trading Bitcoin in Current Conditions
• 12:15 - Factor Exposure & Risk Contribution

hft quant strategy cpp build and algo strategy breakdown

Building a C++ HFT Market Maker: Avellaneda-Stoikov & Order Flow Imbalance

Description:

In this final and most technical section of our series, we leave the high-level abstractions of Python and JavaScript behind to focus on raw speed and performance. This video walks you through the "nitty-gritty" C++ implementation of a High-Frequency Trading (HFT) market-making strategy.

We explore a hybrid approach combining the classic Avellaneda-Stoikov model with Order Flow Imbalance (OFI) signals to manage inventory risk and predict short-term price drift. Running inside a Windows Subsystem for Linux (WSL) environment, this self-contained C++ engine is capable of processing over 2 million ticks per second with sub-microsecond latency potential.

In this video, you will learn:

• The Strategy: How to mathematically calculate the "Reservation Price" to balance execution risk vs. inventory risk.
• The Code: A walkthrough of the 1,700-line C++ source code that handles the limit order book, matching engine simulation, and signal generation.
• Toxic Flow: How to use OFI to detect adverse selection and avoid "catching the falling knife" during volatility crashes.
• Linux Optimization: Specific kernel tweaks (CPU affinity, disabling IRQ balance) to minimize jitter and achieve ultra-low latency.
• Performance Metrics: Analyzing the backtest results, including Sharpe Ratios > 3.0 and inventory management efficiency.

👇 Topics Covered & Timestamps:

• 0:00 - Introduction: Moving to C++ & WSL Environment
• 1:25 - Compiling the Code & Running the Binaries
• 3:10 - Theory Deep Dive: Optimal HFT Market Making
• 5:00 - The Market Maker's Dilemma: Inventory vs. Execution Risk
• 8:20 - The "Secret Sauce": Calculating Reservation Price
• 12:40 - Interpreting Alpha Parameters (Kappa & A)
• 15:40 - Inventory Management Logic (The Gamma Parameter)
• 20:15 - Mitigating Toxic Flow & Adverse Selection
• 22:00 - Latency Considerations: From Research to Production
• 25:20 - Build Instructions: Compiling on Linux/Ubuntu
• 27:30 - System Tuning: Kernel Tweaks for Ultra-Low Latency
• 29:20 - Code Overview: 100% AI-Generated C++ Architecture

5 hft quant strategy cpp build and algo strategy breakdown

Video Title: C++ HFT Market Maker: Avellaneda-Stoikov Strategy & Low-Latency Build

Description:

In this final and most technical section of the course, we transition from research to production by diving into the "nitty-gritty" C++ implementation of our High-Frequency Trading (HFT) bot. Running inside a Windows Subsystem for Linux (WSL/Ubuntu) environment, this video demonstrates how to build a self-contained trading engine capable of processing over 2 million ticks per second with sub-microsecond latency potential.

We explore a hybrid algorithmic approach that combines the classic Avellaneda-Stoikov market-making model with Order Flow Imbalance (OFI) signals. This combination allows the bot to dynamically adjust its "Reservation Price" to manage inventory risk while using short-term momentum to avoid toxic flow and adverse selection.

In this video, you will learn:

• The Strategy: A deep dive into the math behind the Avellaneda-Stoikov model, including the calculation of optimal bid/ask spreads based on inventory skew (Gamma) and market volatility.
• The Code: A look at the 1,700-line, AI-generated C++ source code that handles the limit order book, matching engine simulation, and signal generation all in one file.
• Risk Management: How to use Order Flow Imbalance (OFI) to detect "toxic flow" and prevent the bot from "catching a falling knife" during market crashes.
• Linux Optimization: Essential kernel tweaks for HFT, including setting CPU affinity, disabling IRQ balance, and managing huge pages to minimize OS jitter.
• Backtesting: Analyzing performance metrics, including Sharpe Ratios > 3.0, max drawdown, and profit factor using historical tick data.

👇 Topics Covered & Timestamps:

• 0:00 - Introduction: Moving to C++ & WSL Environment
• 1:26 - Running the Backtest: 2 Million Ticks/Sec Throughput
• 3:13 - Theory: Optimal HFT Market Making (Avellaneda-Stoikov)
• 5:00 - The Market Maker's Dilemma: Execution vs. Inventory Risk
• 8:20 - The "Secret Sauce": Calculating Reservation Price
• 9:30 - Using Order Flow Imbalance (OFI) as an Alpha Signal
• 12:40 - Estimating Alpha Parameters (Kappa & A)
• 15:40 - Inventory Management Logic (The Gamma Parameter)
• 20:15 - Mitigating Toxic Flow & Adverse Selection
• 22:00 - Latency Considerations & Micro-Price Adjustment
• 25:25 - Build Instructions: Compiling on Linux/Ubuntu
• 27:35 - OS Tuning: Kernel Tweaks for Ultra-Low Latency
• 29:20 - Code Overview: 100% AI-Generated Architecture

#CPlusPlus #HFT #QuantTrading #AlgoTrading #MarketMaking #Linux #LowLatency #AvellanedaStoikov #Bitcoin #FinTech

Disclaimer: This video is for educational purposes only. The strategies and code presented are based on historical simulations. High-frequency trading involves significant financial risk.

6 hft quant strategy cpp build and algo strategy breakdown 2

Here is a detailed video description based on the transcript provided.

Video Title: C++ HFT Coding Breakdown: Market Making, OFI & Low Latency Optimization

Description:

In this second part of our coding breakdown series, we dive deep into the C++ source code for a high-frequency trading (HFT) market-making system. We explore the architecture of a 1,700-line, single-file C++20 program designed for sub-microsecond task processing on Ubuntu Linux.

This video walks you through the PDF documentation and code structure, explaining how we implement the Avellaneda-Stoikov model enhanced with Order Flow Imbalance (OFI) signals—all without external libraries. We focus on low-latency optimization techniques, memory management, and the mathematical models driving the strategy.

In this video, you will learn:

• System Architecture: A layered approach covering application, strategy, market data, and infrastructure, capable of processing up to 3 million ticks per second.
• C++20 Optimization: Utilizing std:: libraries only, cache line alignment (64-byte), and compiler hints (likely/unlikely) for maximum speed.
• Core Strategy: Implementing the Avellaneda-Stoikov market-making model with inventory risk management and reservation pricing.
• Order Flow Imbalance (OFI): How to calculate and use OFI signals to predict short-term price direction and adjust quotes dynamically.
• Low Latency Infrastructure: Building a lock-free Single Producer Single Consumer (SPSC) queue and using RDTSC for high-resolution timestamping.
• Mathematical Models: Fast math utilities for volatility estimation (EWMA), fixed-point arithmetic, and "fast inverse square root" calculations.
• Risk Management: Automated kill switches based on max drawdown, position limits, and inventory controls.

👇 Topics Covered & Timestamps:

• 0:00 - Introduction & Resources (PDF & HTML Slide Deck)
• 1:15 - Executive Summary: C++20, Ubuntu, & No External Libraries
• 2:25 - Layered Architecture: Strategy, Market Data, & Infrastructure
• 4:10 - Market Making Strategy: Avellaneda-Stoikov & OFI
• 6:20 - Data Structures: Tick Data & Cache Line Alignment
• 7:15 - Compiler Optimizations & Memory Layout
• 11:40 - Lock-Free SPSC Queue Implementation
• 13:15 - Fixed Point Arithmetic vs. Floating Point
• 16:15 - Fast Math Utilities & Volatility Estimation (EWMA)
• 18:55 - Order Flow Imbalance (OFI) Calculator & Logic
• 21:10 - The "AS" Model: Reservation Price & Optimal Spread
• 23:40 - Quote Generation & Inventory Risk Management
• 26:30 - Fill Detection Logic & P&L Calculation
• 28:10 - Geometric Brownian Motion (GBM) Simulator
• 30:00 - Live Trading Engine & Thread Pinning
• 32:40 - Mathematical Reference & Final Review

7 hft quant strategy cpp build and algo strategy breakdown 3

Video Title: C++20 HFT Market Maker: Visual Code Walkthrough & Strategy Optimization

Description:

In this final video of our C++ High-Frequency Trading (HFT) series, we switch gears to a visual HTML slide deck to break down the architecture, algorithms, and logic of our custom market-making system. This walkthrough complements the previous source code review by providing a visual representation of how the Avellaneda-Stoikov model, enhanced with Order Flow Imbalance (OFI), operates at a sub-microsecond level.

We explore the "production-ready" mindset, covering everything from Linux kernel tuning and compiler flags to lock-free queues and memory alignment. This system is designed to handle over 1 million ticks per second using standard C++20 with zero external dependencies.

In this video, we cover:

• System Architecture: A 4-layer design (Infrastructure, Market Data, Strategy, Application) optimized for low latency.
• Performance Metrics: Targeting a Sharpe Ratio > 3.0 and sub-microsecond tick processing.
• Low Latency Techniques: Implementing Lock-Free SPSC Queues, RDTSC high-resolution timestamping, and CPU thread pinning.
• Memory Optimization: Using power-of-two ring buffers, bitwise modulo operations, and 64-byte cache line alignment to prevent false sharing.
• The Math: Visualizing the Avellaneda-Stoikov inventory risk penalty, Reservation Price calculations, and the OFI signal for short-term price prediction.
• Risk & Regulation: Building automated kill switches, "fat finger" checks, and detecting toxic flow/spoofing.

👇 Timestamps & Topics:

• 0:00 - Introduction: The Final Breakdown (HTML Slide Deck)
• 1:15 - Performance Goals: 1M+ Ticks/Sec & Sharpe Ratio Targets
• 2:25 - System Architecture: Layered Design & No External Dependencies
• 4:10 - Compiler Optimization: C++20 Flags, Inlining & Branch Prediction
• 6:20 - Memory Management: Power of 2 Buffers & Bitwise Operations
• 7:40 - High-Resolution Timing: Using RDTSC & CPU Cycles
• 8:20 - Lock-Free Infrastructure: SPSC Queue & Memory Ordering
• 9:40 - Data Structures: Cache-Optimized Tick Data & Order Books
• 11:15 - OFI (Order Flow Imbalance): Predicting Price Direction
• 12:10 - The Strategy: Avellaneda-Stoikov & Reservation Price Logic
• 15:00 - Risk Management: Inventory Limits & Drawdown Controls
• 16:30 - Backtesting Framework & Performance Metrics
• 18:00 - Live Engine: Thread Affinity & CPU Pinning
• 20:20 - Analytics: Technical Indicators & Monte Carlo Simulations
• 22:40 - Advanced Topics: Adverse Selection & Machine Learning Integration
• 26:50 - Regulatory Compliance: Spoofing Detection & Pre-Trade Checks

#Cplusplus #HighFrequencyTrading #MarketMaking #QuantDev #AlgoTrading #LinuxOptimization #OrderFlow #AvellanedaStoikov #BitcoinTrading #LowLatency