Why I made this

Most career roundups for “quants” are either hype lists of famous firms or broad, generic advice that’s hard to act on from an academic CV. I wanted to build something different: a concise, evidence‑first package that helps research‑focused applicants understand where to apply, how roles differ, what hiring teams actually care about, and what concrete materials to prepare.

What the guide contains (high level)

• Evidence‑first firm mapping
  A compact table of major firms known to hire PhDs, with short role descriptions and notes on what interviewers emphasize (research, coding, probability, market intuition, systems). This helps you match your profile to realistic targets instead of just chasing brand names.

• Salary transparency
  Recent job postings and publicly available wage filings are collected and labeled by source, with clear caveats distinguishing base salary, sign‑on, bonuses, and total compensation. Use this to form realistic expectations for offers across regions and role types.

• Geography & role briefings
  Short market briefs for places like New York City, London, Amsterdam (and others on request), plus role‑track notes (researcher, trader, ML researcher, low‑latency systems). These highlight local hiring patterns, visa considerations, and which skills get prioritized in each market.

• Application‑ready materials
  Outreach templates (both recruiter and referral variants), a PhD résumé framework tailored to quant roles (what to highlight, what to de‑emphasize), and a prioritized GitHub/portfolio checklist with project ideas that make research work translate to hiring signals.

• Practical next steps + sourcing note
  A short methodology note explains how the guide was compiled so you can trust the evidence and reproduce or extend items if you want to vet them yourself.

Who this is for

• PhD students and postdocs actively planning a transition into industry roles in quantitative finance, proprietary trading, or HFT.

• Academics with research-heavy backgrounds evaluating whether to apply for researcher, trader, ML research, or systems roles.

• Candidates who prefer to apply strategically — matching skills and role expectations — rather than applying broadly to prestige names.

How to get the guide
The guide is available as a free PDF in the QuantLabs discussion here:
https://www.quantlabsnet.com/group-page/quantlabsnet-com-public-quant-analytics-group/discussion/599bf375-f6fb-4655-b916-b02f2d387f07

Minh Hoang published a quiet eulogy on LinkedIn that marked the end of an era: “OpenBB is coming to an end. It closes the most important chapter of my career.”

For anyone who spent the last five years watching the intersection of open-source software and capital markets, the post carried an unmistakable weight. What …

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1. The 0.18% Recruitment Trap and the Rise of the Solo Quant

When Citadel Securities released its internship admissions data, the numbers underscored a staggering reality across elite quantitative finance: an acceptance rate of just 0.18%. Out of more than 115,000 hopeful applicants, a mere 210 secured an offer. To…

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This market analysis examines the inaugural deployment of an AI-driven watchdog system monitoring automated trading bot activity across virtual trading sessions conducted during the night of August 20–21, 2026. The watchdog system was configured to perform health checks at 10-minute intervals, enforce a loss stop threshold of negative $100.00, and generate periodic AI fleet health reports through integration with the Minimax-M2.7-FW framework. Over the approximately 15.5-hour observation window spanning from 17:54:59 on August 20 through 09:41:09 on August 21, the system monitored a fleet of between one and eight concurrent trading bots operating across diverse futures instruments including Euro FX (6E), Bitcoin (BTC), crude oil (CL), E-Mini S&P 500 (ES), gold (GC), copper (HG), natural gas (NG), and 10-year Treasury notes (ZN).

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Welcome to our very first live breakdown on The Order Book Edge!

For those who joined us live, thank you for tuning in to our unannounced broadcast. We took a deep dive under the hood of a brand-new, high-performance C++ trading application connected directly to live Rhythmic data.

If you missed the stream, or prefer to digest the technicals in writing, here is the full recap of what we’re running, how we are utilizing AI for post-market optimization, and what the current market behavior is signaling.

The Tech Stack: Low-Latency C++ & Python Transpilation

One of the core focuses of today’s session was showcasing a custom C++ application that has been built out over recent iterations. Designed with low latency in mind, these independent, self-contained bots avoid bulky third-party dependencies.

Interestingly, the foundation of these systems often starts in Python before being transpiled into high-performance C++ binaries. Why? Because when you are dealing with tick-level data, execution speed and resource management matter.

We also touched on a common infrastructure hurdle: broker connection limits (such as Rhythmic or Interactive Brokers, which typically restrict accounts to a single active connection). To bypass this without violating constraints, traders often split architectures—using one account/connection strictly for live data feeds and another for order execution.

Active Strategies: Gen 2 Bots in Action

During the stream, we looked at three specific Gen 2 trading bots that had been running for roughly 90 minutes. These bots are built around approximately 400 institutional-grade trading rules:

1. GC Wheel Yield Caller (Gold): Focused on capturing yield and managing gold-market volatility.

2. Gold Safe Haven Demand Capture: Engineered to scale into gold demand as macro conditions shift.

3. NASDAQ Futures Put Back Ratio Crash Hedge G2: Our heavy-hitter risk-mitigation bot designed to handle sudden downside volatility in NQ futures.

While these bots were running live during the session, immediate order execution was secondary to data collection. Why? Because true edge comes from analyzing what happens beneath the surface.

Moving Beyond OHLC: AI-Powered Log Analysis

Most retail traders rely entirely on lagging Open-High-Low-Close (OHLC) chart data and standard technical indicators. The Order Book Edge focuses on something entirely different: institutionally applied order flow.

Because our C++ apps capture granular bid and ask spread data in real time, we generate exceptionally detailed logs. Once the market closes, we feed these raw bid/ask and order book logs directly into AI models.

By running AI against these execution logs, we can systematically identify slippage patterns, missed fills, and micro-optimization opportunities that standard charts completely hide. Once we finish analyzing today’s logs, I will be posting the breakdown right here on the publication for free so you can evaluate the findings yourself.

Market Outlook: What is Gold Telling Us?

While reviewing the broader markets during the live stream, a few key themes stood out:

• Bitcoin: Continuing to face downward headwinds, sitting significantly lower over the trailing year.

• Oil: Remaining sensitive to geopolitical headlines (such as ongoing tensions involving Iran).

• Gold & Silver: Showing strong resilience and bouncing back.

When gold starts rising alongside or counter to traditional equity markets, it is often a telling indicator. It whispers something important about where “smart money” and institutional insiders are quietly parking their capital as a hedge against macro uncertainty.

What’s Next?

We are just scratching the surface with these live Substack broadcasts. Given the incredible turnout for an unannounced first stream, we will definitely be ramping up more live sessions, technical deep dives, and log analyses in the near future.

What are your thoughts?

• Are you currently incorporating raw order book data into your strategies, or are you still relying solely on traditional indicators?

• How are you tackling broker connection limits in your own automated setups?

Let us know your thoughts in the comments below, and stay tuned for the post-market AI log analysis dropping soon!

In the world of quantitative finance, low latency is measured in nanoseconds, and the infrastructure required to capture it typically costs millions of dollars in specialized hardware, fiber-optic routes, and elite systems engineering talent.

Yet, for exactl…

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And everyone says I have no sense of humor…

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With modern APIs, cloud infrastructure, and Large Language Models (LLMs) acting as massive force multipliers, a single developer can now deploy and run execution systems that previously required an entire infrastructure team.

If you are building your own independent trading desk, here is the exact architectural blueprint to compete with the street.

1. The Architecture of C++ Algorithmic Trading with the IBKR API

When microseconds dictate your fill rate, C++ is non-negotiable. Python is excellent for research and prototyping, but it cannot handle high-frequency execution loops.

However, developers attempting to implement C++ algorithmic trading with the Interactive Brokers (IBKR) API quickly run into a major roadblock: the dual-class architecture of the Trader Workstation (TWS) API.

asciidoc

+-----------------------------------------------------------------+
|                         Your Strategy                           |
|  (Manages state, risk parameters, and execution logic)          |
+-----------------------------------------------------------------+
                               |
                               v  (Passes smart pointer / interface)
+-----------------------------------------------------------------+
|                         EClientSocket                           |
|  (Sends outbound orders, contract details, and data requests)   |
+-----------------------------------------------------------------+
                               |
                               |  TCP / IP Connection
                               v
+-----------------------------------------------------------------+
|                     Interactive Brokers TWS /                   |
|                            IBGateway                            |
+-----------------------------------------------------------------+
                               |
                               |  TCP / IP Connection
                               v
+-----------------------------------------------------------------+
|                           EWrapper                              |
|  (Virtual abstraction layer receiving inbound callbacks)        |
+-----------------------------------------------------------------+
                               |
                               v  (Dispatches events)
+-----------------------------------------------------------------+
|                     Strategy Event Handler                      |
+-----------------------------------------------------------------+

The EWrapper Virtual Abstraction Dilemma

The IBKR C++ API relies heavily on EClientSocket (for sending requests to IB) and EWrapper (an abstract interface class you must implement to handle incoming messages, market data, and order execution reports).

Because EWrapper is a virtual abstraction layer, handling asynchronous callbacks cleanly without spaghetti code is incredibly difficult. Many developers make the mistake of inheriting their strategy class directly from EWrapper and rewriting their API connection and event-handling logic for every single new strategy. This introduces massive surface areas for bugs.

The Solution: Smart Pointer Client Passing

To build a production-grade framework, you must decouple your connection management from your trading logic.

Instead of inheriting your strategy class directly from EWrapper, implement a dedicated network/client manager class. This manager implements EWrapper and holds a smart pointer to EClientSocket. You then define an abstract interface class for your strategies (IStrategy) and pass a smart pointer of your client manager to the strategy.

Here is how to structure this in modern C++:

cpp

#include <iostream>
#include <memory>
#include "EWrapper.h"
#include "EClientSocket.h"
#include "EReaderOSSignal.h"
#include "EReader.h"

// Abstract Strategy Interface
class IStrategy {
public:
    virtual ~IStrategy() = default;
    virtual void onTick(double price, long size) = 0;
    virtual void onOrderUpdate(long orderId, const std::string& status) = 0;
};

// IB Client Manager implementing EWrapper
class IBClientManager : public EWrapper {
private:
    std::unique_ptr<EClientSocket> m_client;
    std::unique_ptr<EReaderOSSignal> m_signal;
    std::shared_ptr<IStrategy> m_activeStrategy;

public:
    IBClientManager() {
        m_signal = std::make_unique<EReaderOSSignal>(2000); // 2000ms timeout
        m_client = std::make_unique<EClientSocket>(this, m_signal.get());
    }

    void registerStrategy(std::shared_ptr<IStrategy> strategy) {
        m_activeStrategy = strategy;
    }

    bool connect(const char* host, int port, int clientId) {
        bool res = m_client->eConnect(host, port, clientId);
        if (res) {
            std::cout << "Connected to IB Gateway successfully!" << std::endl;
        }
        return res;
    }

    // EWrapper virtual overrides
    void tickPrice(TickerId reqId, TickType field, double price, const TickAttrib& attrib) override {
        if (m_activeStrategy && field == LAST) {
            m_activeStrategy->onTick(price, 0);
        }
    }

    void tickSize(TickerId reqId, TickType field, Decimal size) override {}
    
    void orderStatus(OrderId orderId, const std::string& status, Decimal filled,
                     Decimal remaining, double avgFillPrice, int permId, int parentId,
                     double lastFillPrice, int clientId, const std::string& whyHeld, double mktCapPrice) override {
        if (m_activeStrategy) {
            m_activeStrategy->onOrderUpdate(orderId, status);
        }
    }
};

By passing a smart pointer of this interfaced client around, you can swap strategies dynamically without ever redoing the underlying API connection, signal handling, or message loop thread logic.

2. Keeping Your Bot Online 24/7 (The IBGateway Challenge)

By default, IBGateway and TWS are designed to restart or log out daily at a designated time (typically around midnight). For automated, 24/7 algorithmic trading bots, this is an operational hazard.

To bypass this and keep your bots online continuously from Sunday market open through Friday close:

1. Use IBGateway, Not TWS: TWS is resource-heavy, has a complex GUI, and is prone to memory leaks. IBGateway is lightweight and designed specifically for headless API connections.

2. Implement IBC (Interactive Brokers Controller): IBC is an open-source bootstrap program that wraps around IBGateway. It automatically inputs your credentials, handles the daily restart sequence, and bypasses manual 2FA prompts.

3. Dockerize the Setup: Running IBGateway + IBC inside an Ubuntu Linux Docker container on a Virtual Private Server (VPS) ensures that if the process crashes, the container daemon immediately restarts it, and IBC handles the re-authentication seamlessly.

3. Rithmic vs. Interactive Brokers: The Hybrid Data Play

While Interactive Brokers is an exceptional, cost-effective broker for multi-asset portfolio management, it has distinct limitations when it comes to ultra-low-latency futures trading.

The Chicago-to-NYC Lead-Lag Arbitrage Case Study

Consider a classic high-frequency trading (HFT) strategy: Gold Lead-Lag Arbitrage.

An institutional-grade system might monitor the Gold Futures contract (/GC) traded on the COMEX in Chicago and exploit microsecond-level lead-lag relationships with the Gold ETF (GLD) traded on the NYSE in New York:

Δtarbitrage=tGLD−tGC\Delta t_{\text{arbitrage}} = t_{\text{GLD}} - t_{\text{GC}}Δtarbitrage​=tGLD​−tGC​

If you run this setup on Interactive Brokers, you will encounter data feed throttling. IB aggregates market data ticks into 100ms to 250ms snapshots rather than delivering a true, unfiltered tick-by-tick feed. For HFT, a 100ms delay is an eternity.

Enter Rithmic

Rithmic is a specialized high-frequency trading protocol and data provider. Unlike IB, Rithmic provides:

• Unfiltered Tick Data: You receive every single trade and quote update directly from the exchange matching engine.

• Direct Market Access (DMA): Rithmic’s execution APIs (such as R|API+ in C++) bypass standard broker routing layers, sending your orders directly to the exchange with sub-millisecond latency.

The Hybrid Architecture: A highly effective setup for solo quants is to ingest market data via Rithmic (to trigger your signals) and route execution to Interactive Brokers via your optimized C++ API setup. This gives you the best of both worlds: institutional-grade data speed and retail-friendly clearing.

4. Scalping Options Flow & Delta Hedging Effects

For options traders, the data landscape is vastly different. To scalp large order flow and capitalize on institutional delta hedging effects, you need real-time visibility into every single option transaction across all strikes and expirations.

When a massive institutional block trade or sweep order hits the options market, it forces the market makers on the other side of that trade to immediately hedge their exposure. To remain delta-neutral, the market maker must buy or sell the underlying stock:

Δportfolio=∑iwi∂Vi∂S=0\Delta_{\text{portfolio}} = \sum_{i} w_i \frac{\partial V_i}{\partial S} = 0Δportfolio​=∑i​wi​∂S∂Vi​​=0

This buying/selling pressure creates a highly predictable, preemptive momentum wave in the underlying equity. If your algorithm can detect these massive options sweeps in real-time, you can front-run the market maker’s delta-hedging execution.

To do this, you need a modern data budget:

• Theta Data (USD 160/mo): Low-latency raw OPRA options data. Best for systematic backtesting and real-time streaming.

• Data Bento (USD 200/mo pay-as-you-go): Raw tick-by-tick historical and live cloud data.

• Unusual Whales (USD 400/mo): Pre-filtered, highly curated “smart money” flow.

5. Integrating Real-Time Data with LLMs & AI

Once you have a high-performance data pipeline streaming options flow and order book updates into your C++ environment, the next challenge is decision-making.

Instead of using AI to generate static backtests, use it as a real-time portfolio optimization engine:

asciidoc

+-----------------------------------------------------------------+
|                     Real-Time Market Data                       |
|             (Rithmic Futures / Theta Data Options)              |
+-----------------------------------------------------------------+
                               |
                               v  (High-speed JSON / Protobuf)
+-----------------------------------------------------------------+
|                      C++ Ingestion Engine                       |
|            (Filters sweeps, blocks, and imbalances)             |
+-----------------------------------------------------------------+
                               |
                               v  (Structured Context Window)
+-----------------------------------------------------------------+
|                        Local LLM / API                          |
|         (Evaluates market regime, sentiment, and weights)       |
+-----------------------------------------------------------------+
                               |
                               v  (Optimal Portfolio Weights)
+-----------------------------------------------------------------+
|                      C++ Execution Engine                       |
|          (Executes trades via Interactive Brokers API)          |
+-----------------------------------------------------------------+

The Real-Time AI Pipeline

1. Context Construction: Your C++ engine aggregates real-time options flow, calculating the net delta and gamma exposure across all strikes.

2. LLM Ingestion: Feed this structured, real-time data stream into a high-throughput LLM (such as Claude 3.5 Sonnet or a fine-tuned local Llama-3 model).

3. Dynamic Calibration: The AI runs an efficient portfolio optimization based on forward-looking option expected values and volatilities:

max⁡w(wTμ−γ2wTΣw)\max_{w} \left( w^T \mu - \frac{\gamma}{2} w^T \Sigma w \right)maxw​(wTμ−2γ​wTΣw)

Where www represents the portfolio weights, μ\muμ is the vector of forward-looking expected returns, Σ\SigmaΣ is the covariance matrix, and γ\gammaγ is the risk-aversion parameter.

Why You Should Skip Backtesting and Go Straight to Live Sim

Many traditional quants get stuck in “backtesting paralysis,” spending months tweaking historical parameters only to find their models fail in live markets due to execution slippage and regime shifts.

Instead of backtesting complex AI-driven options models, hook your system up live in a simulation environment immediately.

• Build a custom simulation environment that mirrors IBKR’s execution rules.

• Feed it real-time, live options data from Rithmic or Theta Data.

• Let the AI calibrate its models and execute virtual trades in real-time.

This is the only way to truly verify that your data pipelines, C++ parsing logic, and AI decision-making loops are calibrated correctly before risking a single dollar of real capital.

The Solo Quant’s Edge

As a solo quant, you do not have the massive overhead, compliance restrictions, or slow decision-making pipelines of a traditional firm. By combining high-performance C++ execution, unfiltered data feeds, and real-time AI calibration, you can pivot your strategies in minutes and exploit micro-inefficiencies that are too small for billion-dollar funds to care about.

Treat your trading infrastructure like a world-class software engineering product. Decouple your API layers, optimize your memory allocation, and never stop adapting.

Are you building your own independent trading stack? What does your data budget look like? Let’s discuss in the comments below.

In May 2026, Ken Griffin, the billionaire founder and CEO of Citadel, stood before an audience at the Stanford Graduate School of Business and made a startling confession [1].

He admitted that he went home on a Friday “fairly depressed” after watching what agentic artificial intelligence could do within his own firm [1][2].

“To be blunt, work …

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If you’ve been drawing trendlines on a chart, trying to guess how the market will react to the latest geopolitical headline, I have some bad news: you are bringing a knife to a laser fight.

The landscape of quantitative trading has fundamentally shifted. For years, the barrier to entry for high-frequency trading (HFT) and serious algorithmic strategies was a PhD and a mastery of C++. Today? It’s a well-crafted prompt.

In our latest deep dive, we explored a massive breakthrough in our trading infrastructure. Here is what you need to know about the new era of AI-driven trading, “vibe coding,” and surviving extreme market volatility.

🐍 The Python & Rithmic Breakthrough

For a long time, if you wanted to use Rithmic—one of the premier data feeds and order routing platforms for futures—you had to navigate complex C++ APIs.

Not anymore. We have successfully integrated Python with Rithmic.

Why is this a game-changer? Because Python is the undisputed language of AI. By bridging Rithmic with Python, you can now pipe live, full depth-of-market (DOM) data directly into machine learning models and Large Language Models (LLMs). You can go from a backtested concept to live, automated execution in hours.

🤖 What is “Vibe Coding”?

If you hang around modern developer circles, you’ve heard of “vibe coding”—using AI to generate complex software via natural language instead of writing every line manually.

Here is how we are applying it to the markets right now:

1. The Brain: We use AI to generate massive, 41-page analytical reports synthesizing global news, stagflation data, and geopolitical events.

2. The Prompt: We feed this report into an LLM with a 1,500-line prompt, instructing it to act as a Systematic Portfolio Manager.

3. The Execution: The AI analyzes the news and automatically writes the Python code for the exact trading bots needed for that specific day’s market conditions.

⚔️ The Great LLM Showdown: Codex vs. Claude vs. GLM

We ran massive prompts through the top AI models to see which one builds the best trading bots. The results were surprising:

• Claude 4.6 (Anthropic): The Overpriced Premium. Claude struggled with our massive 1,500-line prompts, frequently breaking connection. It’s also incredibly expensive. Unless you have capital to burn, it’s not the most efficient choice for high-frequency bot generation.

• GLM 5 (Zhipu AI): The Budget Powerhouse. This Chinese LLM handled the massive prompts flawlessly and generated high-quality Python code at a fraction of the cost of Western models. It’s a bit slower, but if you run your generation overnight, it’s a massive winner.

• Codex 5.3 (OpenAI): The Sweet Spot. Fast, reliable, and reasonably priced. When you need to generate and deploy a new bot in minutes based on breaking news, Codex is currently the king.

📊 Real Market Results: Crypto vs. Commodities

We let the AI loose during a recent period of extreme market stress (driven by war news and oil fluctuations).

• The Winner: A Bitcoin futures momentum bot. It identified the crypto momentum and generated the highest profits with surprisingly low drawdowns.

• The Wildcard: Natural Gas (NG). The AI deployed a mean-reverting NG strategy based on EU gas cap news. It achieved a massive Sharpe Ratio of 4.3, but experienced a violent $36,000 drawdown along the way. High profitability in commodities right now requires an iron stomach.

• The Loser: Gold. Traditionally a safe haven, the AI’s gold tail-hedging strategy suffered worse drawdowns than Bitcoin. Human intuition (”buy gold during a war”) is losing to algorithmic reality.

⏳ Time is Running Out

The traditional discretionary trader is an endangered species. The future belongs to the Systematic Portfolio Manager—the trader who manages a fleet of AI agents adapting to the news in real-time.

Stay systematic,
Bryan at Quantlabs

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The world of AI-powered trading is evolving at a breakneck pace, and staying ahead means embracing tools that don’t just keep up—they push the boundaries of what’s possible. After exploring the latest capabilities of MiniMax M2.1, one thing is crystal clear: this platform is fundamentally reshaping h…

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Join us as we explore a developer’s journey to simplify his high-speed trading stack. We discuss the “lightbulb moment” when he realized that C++ was causing his AI tools to lose context, making debugging a nightmare. By switching to C#, he didn’t just change languages; he unlocked a new way to organize his trading strategies.

What we cover:

• Why “fast code” doesn’t matter if development is slow.

• How to stop fighting Windows package managers.

• A guide to using Redis to connect a central server to multiple strategy clients.

• Why your software architecture should match your trading philosophy.

1.0 Introduction: The Imperative for Architectural Agility in Trading Systems

The development o…

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What you’ll learn in this dev log:

API Interop: How to connect managed code (.NET) to Rithmic’s native libraries.

Architecture: Why you must separate your execution engine from your UI (WinForms/WPF failures).

Routing: How to handle multiple strategies through a single Rithmic connection.

The Legal Wall: Why I can’t release the source code (ToS issues) and the future of my “Quant Math” teaching.

Whether you are building a bot for Crypto or Futures, these architectural lessons are universal.

Resources:

https://www.quantlabsnet.com/post/deep-dive-into-high-frequency-trading-iquant-development-nfrastructure-api-constraints-a

In the world of algorithmic and quantitative trading, the gap between theoretical strategy and execution infrastructure is often a chasm that takes years to cross. On December 12, 2025, Bryan from QuantLabsNet.com announced a significant milestone in this journey: a successful, stable implementation of the Rithmic API using .NET/C#, capable of handling Level 2 order book data and complex execution strategies.

This breakthrough, achieved after weeks of intensive quant development/coding and decades of programming experience, highlights the specific challenges of modern trading APIs. It brings to light the friction between user interface design and backend stability, the architectural necessities of single-socket connections, and the stringent legal realities of proprietary trading technologies.

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This is the closest a retail trader can get to true institutional quant and HFT methodologies. I spent days refining the AI prompting to create this seamless pipeline that takes trading ideas, backtests them, forecasts their potential, and generates production-ready C++ code. If you’re serious about algorithmic trading, you don’t want to miss this.

📈 THE COMPLETE AI-DRIVEN WORKFLOW YOU’LL SEE:

THE BLUEPRINT (The Quant PDF): We start with an AI-generated PDF filled with sophisticated quant formulas and strategies for futures and options, including order flow mechanics and arbitrage.

THE BACKTEST (Python & Streamlit): I feed two years of real MEES market data into a custom Python Streamlit dashboard. The AI-generated code backtests numerous strategies (LDI Momentum, Long Straddle, Iron Condor) to identify the top performers based on Sharpe Ratio, Volatility, and Win Rate.

THE FORECAST (Walk-Forward Analysis): Using a second Streamlit dashboard, we forecast the future performance of the winning strategies. This involves advanced modeling with Geometric Brownian Motion (GBM), GARCH, and Jump Diffusion to project returns and volatility.

THE ENGINE (C++ HFT Simulators): The final, most powerful step. I showcase three separate C++ simulators, each built by AI to trade based on the top models (GBM, GARCH, Jump Diffusion). This code is clean, efficient (using only the standard library), and designed to be the core logic for a live trading bot connected to the Rhythmic API.

🕒 TIMESTAMPS:

0:00 - Introduction to the Sophisticated End-to-End Workflow

1:08 - Step 1: The AI-Generated Quant Research PDF

4:17 - The Real Market Data for MEES (Micro E-mini S&P)

5:10 - Step 2: The Python Backtesting Dashboard in Streamlit

9:53 - Analyzing Futures vs. Options Strategies

10:30 - Identifying the Top Performing Strategies (Iron Condor & Long Straddle)

14:35 - Deep Dive: Performance Metrics of the Iron Condor Strategy

18:52 - Step 3: The Python Forecasting Dashboard

20:23 - Forecasting with GARCH, GBM, and Jump Diffusion Models

24:53 - Step 4: The C++ Trading Bot Simulators

25:51 - C++ Simulator 1: Geometric Brownian Motion (GBM) Model

30:09 - C++ Simulator 2: GARCH Model for Adaptive Volatility Trading

35:43 - C++ Simulator 3: Merton Jump Diffusion Model for HFT

39:16 - Final Thoughts & The Path to Live Trading

40:31 - How to Get the Full Source Code & Learn More

DISCLAIMER:

The content in this video is for educational and informational purposes only. It is not financial or investment advice. Trading involves significant risk, and you should consult with a qualified professional before making any financial decisions. The performance results shown are based on backtests and simulations and are not a guarantee of future results.

#AI #TradingBot #HFT #Quant #AlgorithmicTrading #CPP #Python #Streamlit #FuturesTrading #OptionsTrading #Rhythmic #MotiveWave #Finance #Investing #StockMarket #AIinFinance

The episode highlights the significant challenges developers face. Keeping track of countless open source libraries, identifying vulnerabilities, and implementing upgrades can be incredibly time-consuming and prone to errors. Brian proposes a compelling solution: AI co-generation techniques.

These techniques leverage the power of AI to assist developers in various coding tasks, including generating code snippets or even full scripts. The episode specifically focuses on the effectiveness of code generation in tackling the complexities of dependency management.

Brain puts different AI models to the test, including prominent players like Google Gemini and Microsoft Copilot. He emphasizes a crucial point: the success of AI models heavily relies on crafting well-defined prompts. This process, known as "prompt engineering," involves meticulously instructing the AI on what kind of code to generate. By carefully articulating the desired outcome within the prompt, developers can leverage the AI's capabilities to automate repetitive tasks.

The episode showcases practical examples of how AI code generation can empower developers. Imagine an arbitrage script for gold assets. Traditionally, writing such a script would involve manual coding and extensive testing. However, with a well-constructed prompt, an AI model could potentially generate a significant portion of the script, saving developers valuable time and effort.

Beyond the power of AI, the conversation delves into the accessibility of different tools available to developers. Tools like Lama 2 and Startcoder 2 are explored, highlighting their advantages and limitations. Open source by nature, these tools offer a cost-effective option but might require a steeper learning curve compared to more user-friendly solutions.

The episode concludes by inviting listeners to engage in further discussions on Brian's Discord channel and Substack. These platforms provide a forum for developers to share experiences with various tools and strategies for managing software upgrades and dependencies.

Key Takeaways:

• Managing open source dependencies presents a significant challenge for developers.

• AI co-generation techniques offer promising solutions for automating repetitive tasks.

• The effectiveness of AI code generation hinges on prompt engineering – crafting clear instructions for the AI.

• Tools like Lama 2 and Startcoder 2 provide open source options for managing dependencies, although a steeper learning curve might be involved.

The Future of AI and Dependency Management

The exploration of AI co-generation in dependency management is an exciting development. As AI models become more sophisticated and user-friendly, we can expect them to play an increasingly significant role in streamlining development workflows. Imagine a future where developers can focus on the core logic of their applications while AI handles the heavy lifting of managing dependencies and upgrades. This would not only boost developer productivity but also potentially lead to more secure and reliable software.

However, it's important to acknowledge the limitations of AI. While AI can automate tasks, it doesn't replace the critical thinking and expertise of human developers. The ability to understand code structure, foresee potential issues, and tailor solutions will remain crucial.

The future likely lies in a collaborative approach – an effective partnership between human developers and AI co-generation tools. Developers will leverage AI to automate tedious tasks, freeing them to focus on higher-level problem-solving and strategic decision-making. This synergy has the potential to revolutionize the development landscape, leading to faster innovation and more robust software solutions.

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This article dives into IBKR Desktop, exploring its features, benefits, and how it compares to TWS.

Built for Everyone

IBKR Desktop boasts a clean and intuitive int…

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The Friction of Regulation vs. The Freedom to Innovate

A recent article from risk.net highlights concerns voiced by market participants regarding the approach taken by the Monetary Futures Trading Commission (MFTC) towards AI regulation. Jennifer Hahn, Chief Counsel and Head of Regulatory Affairs at the Managed Funds Association, argues for utilizing the existing framework instead of creating cumbersome new regulations that could hinder innovation.

This resonates with broader anxieties surrounding the tension between fostering a free and open market environment and protecting against potential scams. The rapid rise of Web3 projects, often characterized by decentralized control over software development, further intensifies this debate. How can we ensure a free society with minimal government intrusion, while simultaneously safeguarding the integrity of financial markets?

The Risks of Unfettered Automation and Remotely Hosted Code

The transformative power of AI in trading cannot be understated. Automated trading strategies and remote code hosting, while offering efficiency and potentially superior returns, also introduce significant risks. These risks extend beyond individual markets, potentially impacting entire economies.

Brian, the host of the quantlabs.net podcast, raises valid concerns about the potential for systematic risks arising from the automation of trades. When coupled with remotely hosted code, the potential for manipulation and unforeseen consequences increases.

Regulation for Systemic Risks, Not Micromanagement

While Brian acknowledges the importance of market freedom, he also believes that regulators have a crucial role to play in mitigating systemic risks, particularly those that pose a threat to financial stability. The discussion, however, centers on the extent and method of effective regulation.

Drawing parallels between the highly regulated military and banking sectors with the seemingly unfettered operations of large AI-powered trading companies like Citadel, BlackRock, and Vanguard, Brian emphasizes the need for closer scrutiny. Evaluating and potentially regulating practices within these major entities could be a critical step towards ensuring market stability in the age of AI.

The Path Forward: A Collaborative Approach

The debate surrounding AI regulation in trading requires a collaborative approach. Brian concludes by inviting his listeners to participate in the discourse on platforms like Substack and TradingView. By encouraging open discussions amongst traders, regulators, and industry experts, we can navigate this complex landscape and develop regulations that foster innovation while safeguarding the integrity of financial markets.

This in-depth analysis underscores the urgency of finding a balance between market freedom and the need for regulation. By acknowledging both the potential benefits and risks of AI in trading, we can work towards a future

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