QTL1 · Published Strategy
FlipBB
Historical research simulation · Illustrative £1/pip model
Historical Research
£10,000
£90,989
43.3%
CAGR
4.8%
Max Drawdown
2.59
Sharpe
4.71
Profit Factor
67.1%
Win Rate
1,124
Trades
Quant Trading Labs
Quantitative research built from evidence, not opinion.
Use AI, structured research and reproducible backtesting to investigate markets, understand systematic strategies and access the evidence and research behind published QTL systems — without needing to arrive as a programmer.
Membership includes published strategy research, source code, member publications, governed AI prompts, Daily Quant Brief and ongoing research updates.
5
Connected member publications
104
Governed AI research prompts
2
Published strategy research programmes
1,335
Historical trades across FlipBB + OS1
Research → Deploy
Connected quantitative workflow
Annual
Full member research access
Published Strategy Evidence
Research claims should survive contact with the data.
QTL publishes the evidence behind strategy research rather than presenting headline performance in isolation.
Members can examine the associated methodology, assumptions, historical backtest evidence, implementation notes and supporting source code.
QTL2 · Published Strategy
OS1
Historical treasury-ledger backtest · Jan 2021 – Mar 2026
Historical Research
£100,000
£386,812
29.8%
CAGR
16.4%
Max Trading DD
1.79
Profit Factor
61.1%
Win Rate
286.8%
Total Return
211
Trades
The Performance Is Only the Surface
Members get the research behind the curve.
The public evidence shows what was measured. Membership unlocks the research process, documentation and implementation resources behind the results.
Full research dossiers
Complete supporting source code
Backtest and trade evidence
Documented assumptions
Implementation notes
Ongoing strategy updates
Historical and simulated results are research outputs only. Past performance is not a reliable indicator of future results.
A Growing Research Archive
Join with two published strategies. Build your research archive throughout your membership.
QTL is not designed as a static course or one-off strategy bundle. The research programme continues as new systems are investigated, tested, documented and published.
Our publishing programme is built around a target cadence of one new fully backtested strategy research dossier each month, adding new evidence and research to the member archive over time.
1 / month
Target new strategy research cadence
Full
Historical performance evidence
Code
Supporting strategy source code
Ongoing
Research and strategy updates
Every New Strategy Research Dossier
Documented research hypothesis
Historical backtest evidence
Equity and drawdown analysis
Full performance metrics
Methodology and assumptions
Source code supporting the strategy
Limitations and failure conditions
Subsequent research updates
Research That Compounds
Your membership starts with today's archive — not where the archive ends.
Active members gain access to new strategy research and subsequent updates as the QTL publishing programme develops.
Join the Research Programme£997 / year
QTL Research Framework · Version 3.2
Every published strategy begins long before the first trade.
Every Quant Trading Labs project begins with a question, not a conclusion. Market observations are transformed into measurable hypotheses, explored with artificial intelligence, implemented in Python and challenged through statistical validation before they can become published research.
Many investigations do not survive this process. That is not failure. It is evidence that the framework is doing its job.
01
Market Observation
Identify recurring behaviour, anomalies, structural patterns and possible market inefficiencies.
- Observation log
- Initial research question
- Notebook entry
02
Data Collection
Collect the historical market information required to investigate the observation objectively.
- Raw market data
- Session and volatility data
- Data inventory
03
Data Preparation
Clean, normalise and align the dataset before any hypothesis is tested.
- Validated dataset
- Missing-data audit
- Quality-control report
04
Feature Engineering
Transform raw observations into variables capable of describing behaviour mathematically.
- Feature library
- Behavioural variables
- Feature matrix
05
Research Hypothesis
Define exactly what is being tested, why it may matter and what evidence would invalidate it.
- Testable hypothesis
- Null hypothesis
- Research plan
06
AI-Assisted Research
Use modern language models to challenge assumptions, explore explanations and accelerate experimentation.
- Prompt log
- Alternative hypotheses
- Research review
07
Python Implementation
Convert the research specification into reproducible data pipelines, experiments and backtests.
- Research notebook
- Test scripts
- Reproducible results
08
Statistical Validation
Test robustness, significance and stability across instruments, years, regimes and assumptions.
- Validation report
- Out-of-sample evidence
- Robustness summary
09
Portfolio Construction
Evaluate how validated research behaves when combined with risk, sizing, diversification and exposure controls.
- Portfolio model
- Risk report
- Allocation framework
10
Research Publication
Document and publish validated research for the Quant Trading Labs ecosystem.
- AI Quant Blueprint
- Quant Research Manual
- OS1 Research Notebook
- Daily Quant Brief
- Research Vault
Continuous improvement · New observations · New hypotheses · Better models
Why this framework matters
Most strategies fail because implementation begins before investigation. The framework ensures every idea is measured, challenged and validated before it is exposed to capital.
- Evidence before execution
- Objective decision-making
- Reproducible research
- Documented failure criteria
Built for rigour. Designed for access.
Every stage is documented inside the Quant Trading Labs research ecosystem. Members see the complete journey, not only the final strategy.
- Research notebooks and prompt histories
- Python experiments and validation
- Version-controlled publications
- Ongoing refinement
Technical knowledge is no longer the barrier
Modern AI can explain statistical concepts, write Python code, review assumptions and help reproduce sophisticated workflows. You do not need to arrive as a programmer. You need curiosity and a structured process.
- AI explains the complexity
- AI assists with code
- QTL supplies the framework
- You remain the researcher
Explore the complete methodology
See how every stage of the Quant Trading Labs framework is applied in practice—from raw market observations and AI-assisted exploration to Python validation and published research.
Everything Connected
One research ecosystem. One governed workflow.
QTL resources are designed to work together rather than exist as isolated courses, PDFs or prompts.
01
01
Research Methodology
A governed process for moving from observation to evidence.
02
02
AI Quant Blueprint
Structured guidance for research, Python, backtesting and deployment.
03
03
Prompt Library
104 governed prompts supporting research and implementation workflows.
04
04
Research Vault
Published strategy research, evidence and subsequent updates.
05
05
Daily Quant Brief
Current quantitative market intelligence and research context.
06
06
Strategy Case Studies
Documented strategy development from hypothesis through evidence.
Member Research Library
Your research operating system is already built.
Members gain access to structured publications spanning research, AI-assisted workflows, operational practice and controlled deployment.
Research workflow
Research Operating System
A governed end-to-end operating framework for quantitative research.
104 governed prompts
AI Prompt Library
104 structured prompts for research, coding, validation and deployment.
Operational research
Field Manual
Practical research artefacts and operational guidance for active work.
8 deployment modules
Technical Deployment Guide
Move from research parity toward controlled implementation.
30 deployment prompts
Backtest-to-Live MT5 Guide
Structured prompts and controls for the transition from research to live implementation.
No Technical Background Required
You don't need to arrive as a quant developer.
Modern AI and LLMs can explain concepts, assist with Python, challenge assumptions and accelerate research workflows.
QTL provides the structure, prompts and operating framework. AI lowers the technical barrier without lowering the research standard.
AI gives you leverage. Research gives you structure.
Explain
Use AI to translate quantitative concepts into plain English.
Build
Generate, understand and debug Python research workflows.
Challenge
Interrogate assumptions, performance results and analytical weaknesses.
Control
Human judgement retains authority over evidence, validation and deployment.
Annual Membership
Everything required to research, test and understand systematic strategies.
One annual membership unlocks the connected QTL research environment rather than a collection of disconnected products.
QTL Membership
£997/ year
Research Terminal
Five connected member publications
FlipBB + OS1 published research
Source code supporting published strategy work
104 governed AI research prompts
Daily Quant Brief
Research and deployment resources
Ongoing strategy and research updates
Research and educational material only. Historical or simulated results do not guarantee future performance.
Start With Evidence
Stop consuming trading opinions. Start working from evidence.
Join Quant Trading Labs and access the research, source code, backtests, AI workflows, publications and current intelligence behind a growing systematic research programme.