Research Terminal
A central member workspace connecting current intelligence, published research and QTL resources.
About Quant Trading Labs
A research platform for serious traders.
QTL combines AI-assisted research, quantitative methods, documented backtests and reproducible workflows to help traders investigate markets with far more structure than traditional retail education.
Why QTL Exists
Most retail trading content begins with a claim.
QTL begins with a question.
Every research project follows a structured path designed to turn ideas into evidence rather than opinions into marketing.
01
Question
We begin with a market observation, behaviour or research idea worth investigating.
02
Research
We examine the theory, literature, market structure and relevant quantitative evidence.
03
Code
The research question is translated into reproducible Python and structured test logic.
04
Backtest
Historical behaviour is tested with explicit assumptions, transaction costs and controls.
05
Validate
Results are challenged through robustness work, drawdown analysis and limitations review.
06
Publish
The evidence, methodology, code and limitations are documented for members to study.
Evidence first. Opinions last.
What Members Get Access To
A complete research environment.
Membership connects current market intelligence, published strategies, research methodology, AI workflows, implementation resources and ongoing updates inside one platform.
Daily Quant Brief
Structured market intelligence covering regime, volatility, macro context and active research observations.
Research Vault
Published quantitative strategies, historical evidence, validation material and ongoing research updates.
AI Quant Blueprint
A structured guide to researching, coding, backtesting, validating and deploying quantitative ideas.
Governed Prompt Library
Structured AI prompts designed for research, coding, analysis, validation and deployment workflows.
Strategy Research + Source Code
Members receive the research context and implementation code behind published QTL strategy work.
No Technical Background Required
You don't need to arrive as a quant developer.
AI lowers the technical barrier. It does not lower the research standard.
ChatGPT and other LLMs can help explain unfamiliar concepts, generate and debug Python, interrogate results and translate plain-English questions into structured quantitative workflows.
Explain unfamiliar quantitative concepts in plain language
Generate and debug research and backtest code
Interrogate performance results and challenge assumptions
Turn plain-English questions into structured research workflows
AI gives you leverage. Research gives you an edge.
Research Question
Can you show me how to build and test a moving-average crossover without assuming I already know Python?
AI Research Assistant
import pandas as pd fast = data["close"].rolling(20).mean() slow = data["close"].rolling(50).mean() signal = (fast > slow).astype(int) # Next: # 1. define entries # 2. apply transaction costs # 3. calculate returns # 4. validate out-of-sample
Evidence, Not Marketing Claims
Proof through published research.
Published strategy research is accompanied by documented assumptions, historical evidence, equity curves, drawdown behaviour, risk metrics and the limitations required to understand what the results actually represent.
QTL1 · Published Strategy
FlipBB
Historical research simulation · Jan 2020 – Mar 2026
Illustrative £1/pip model
Historical Equity Curve
Drawdown Through Time
Full Performance Metrics
£10,000
Start Capital
£90,989
End Equity
43.3%
CAGR
4.8%
Max Drawdown
2.59
Sharpe Ratio
17.65
Sortino Ratio
4.71
Profit Factor
67.1%
Win Rate
1,124
Trades
72.1
Avg Pips / Trade
24.8
Median Pips / Trade
6.1 years
Test Period
Historical research simulation derived from the underlying 1,124-trade FlipBB research log using fixed £1-per-pip sizing from £10,000 starting capital. Position sizing materially affects monetary returns and drawdowns. Historical or simulated performance does not predict future results.
QTL2 · Published Strategy
OS1
Historical treasury research · Jan 2021 – Mar 2026
Treasury-ledger backtest
Historical Equity Curve
Drawdown Through Time
Full Performance Metrics
£100,000
Start Wealth
£386,812
End Wealth
29.8%
CAGR
16.4%
Max Trading DD
1.79
Profit Factor
61.1%
Win Rate
211
Trades
286.8%
Total Return
5.2 years
Test Period
Treasury ledger
Accounting
Historical OS1 treasury-ledger backtest results. Historical or simulated performance is not live realised performance and does not predict future results.
The Results Are Only the Surface
Membership gives you the research that produced them.
Full research dossiers
Complete source code
Trade and backtest evidence
Documented methodology
Implementation notes
Ongoing strategy updates
What QTL Is Not
Not a Signal Service
We do not sell trade entries, exits or alerts.
Not Guaranteed Returns
We do not promise profits, forecasts or risk-free outcomes.
Not a Black Box
Research assumptions, evidence and limitations are documented rather than hidden.
Not Generic Prompts
AI is used inside structured research workflows rather than as a shortcut to unsupported answers.
Not Hollow Education
The emphasis is on working research, evidence, code and repeatable process.
Membership That Compounds in Value
A living research programme.
Membership is not access to a static course. It is access to an evolving research environment designed to grow as new work is published.
Published strategy research
Full source code where provided
Research and backtest evidence
Daily Quant Brief
Member Library and publications
Governed AI prompts and workflows
Implementation resources
Ongoing strategy and research updates
Join a research programme, not a signal room.
Annual membership unlocks the Research Terminal, published strategy research, source code, member publications, governed AI prompts, Daily Quant Brief and ongoing strategy updates.
Annual membership · Research and educational content only