Multi-Timeframe Walk Forward Matrix Optimization for cTrader C# Bot (Data Only)
Budget: $10 – $30 USD
I am seeking an experienced quantitative trading analyst or algorithmic developer to perform a comprehensive Walk Forward Matrix (WFM) Optimization Analysis and detailed trade diagnostics on my trading strategy.
I do not want you to build me a tool, software, or script. I only want the final optimization data results, stability heatmaps, a comprehensive trade analysis report, and the best robust parameter settings.
Critical Multi-Timeframe Strategy Logic (Must Read):
Please review the attached C# code carefully. The strategy uses strict Multi-Timeframe (MTF) Filtering. A 1-minute execution signal is only valid if the higher timeframes (the 30-Minute and the Daily charts) are already locked into a matching trend direction. Therefore, your testing environment cannot optimize the 1-minute chart in isolation. Your pipeline or software setup must accurately track the synchronized states of the M1, M30, and D1 data streams simultaneously to validate historical entry rules accurately.
Pricing Note & AI/Software Efficiency:
Please note that modern AI coding assistants can translate or map the attached C# framework into Python or testing pseudocode within seconds. Furthermore, if you utilize advanced quantitative testing platforms like StrategyQuant / SQX, the entire Walk Forward Matrix grid and Trade Analysis dashboard are generated completely automatically by the software. Because AI and automation drastically reduce your actual manual workload, your bid price must be proportional to the minimal hours required to set up this automated run.
Flexibility, Optimization & Suggestions Note:
I am highly collaborative and completely open to adjusting my testing parameters (such as exact date-range boundaries, specific asset choices, or matrix step counts) to make your computational workflow more efficient. Furthermore, I welcome your professional suggestions regarding potential structural improvements to this multi-timeframe filtering setup. If your matrix sweeps or trade analysis reveal architectural bottlenecks or a better way to align these timeframes, please present your optimization ideas in your final report.
Long-Term Ongoing Work Opportunity:
This is not a one-off project. I have a large pipeline of trading strategies that require this exact same matrix validation and diagnostic testing. I am looking to establish a long-term relationship with a reliable, fairly-priced quant analyst. The freelancer who delivers high-quality reports on this first project will receive a steady stream of ongoing strategy testing contracts in the future.
Your Tasks & Timeframe Progression Mandate:
1. Use the attached C# cTrader source code template to run a full multi-segment grid optimization matrix across a selection of liquid assets (e.g., major Forex pairs, Indices, or Gold).
2. Timeframe Structure: Your optimization passes must strictly respect the hierarchical relationship outlined above, focusing on the 1-Minute (M1) execution chart filtered by 30-Minute (M30) and Daily (D1) trend engines.
3. Data History & Confidence: The historical data range must provide high statistical confidence. For the mandatory M1 chart, use at least 1 to 2 full years of high-quality tick data to ensure a massive trade sample size (minimum 500+ total trades). For M30 and Daily charts, scale the historical data back 3 to 5 years to capture varying market regimes.
4. Grid Dimensions: Test multiple Out-of-Sample (OOS) window sizes (e.g., 10%, 20%, 30%, 40%) against multiple rolling chronological window steps (e.g., 5, 10, 15, 20 runs) to locate a stable "parameter island" cluster where the strategy performs consistently across adjacent matrix cells.
Deliverables Required:
A. Walk Forward Matrix Heatmap Report: Visual, color-coded grid charts (PDF, SQX export, or Image format) mapping performance across the tested segments by Profit Factor, Sharpe Ratio, and Walk Forward Efficiency %.
B. Friction and Execution Cost Audit: All optimization passes—especially on the 1-Minute chart—must strictly include realistic transaction costs. You must configure your testing engine to apply standard broker round-turn commissions and a realistic, non-zero average variable spread for the asset tested. Reports generated assuming zero-spread or zero-commissions will be rejected.
C. Comprehensive Trade Analysis Breakdown: A detailed behavioral diagnosis of the final selected parameter cluster (matching institutional reporting dashboards like SQX's Trade Analysis tab). This must explicitly include:
1. Directional Symmetry: Separate performance metrics for Long vs. Short trades to detect directional biases.
2. Time-Based Distribution: Visual charts showing profit/loss distribution across hours of the day and days of the week to reveal time-dependent vulnerabilities.
3. Trade Outlier Impact: Metrics showing strategy performance with and without the top 5 largest winning trades to prove the edge doesn't rely on random statistical anomalies on the M1 chart.
Parameter Resolution Step Summary:
Provide a brief textual or data confirmation proving that the optimized settings operate inside a smooth numerical gradient (e.g., confirming that incrementing or decrementing the primary indicator variables by 5-10% does not result in an immediate, radical performance collapse).
Final Parameter Settings File:
The exact optimized parameter settings (.set file or cTrader parameter block text) derived from the dead center of the most stable cluster, ready for me to apply to my cTrader bot.
How to Apply (No Automated Bids):
To ensure you have read this description, please begin your proposal with the words "FINAL MATRIX RESULTS".
In your proposal, please specify:
1. What software or environment you plan to use to handle this specific multi-timeframe matrix setup (e.g., StrategyQuant/SQX, Python, or custom modules).
2. Where you source your high-quality, tick-accurate historical data for 1-minute testing.
3. A realistic timeline for delivering the final data reports and settings files.
I do not want you to build me a tool, software, or script. I only want the final optimization data results, stability heatmaps, a comprehensive trade analysis report, and the best robust parameter settings.
Critical Multi-Timeframe Strategy Logic (Must Read):
Please review the attached C# code carefully. The strategy uses strict Multi-Timeframe (MTF) Filtering. A 1-minute execution signal is only valid if the higher timeframes (the 30-Minute and the Daily charts) are already locked into a matching trend direction. Therefore, your testing environment cannot optimize the 1-minute chart in isolation. Your pipeline or software setup must accurately track the synchronized states of the M1, M30, and D1 data streams simultaneously to validate historical entry rules accurately.
Pricing Note & AI/Software Efficiency:
Please note that modern AI coding assistants can translate or map the attached C# framework into Python or testing pseudocode within seconds. Furthermore, if you utilize advanced quantitative testing platforms like StrategyQuant / SQX, the entire Walk Forward Matrix grid and Trade Analysis dashboard are generated completely automatically by the software. Because AI and automation drastically reduce your actual manual workload, your bid price must be proportional to the minimal hours required to set up this automated run.
Flexibility, Optimization & Suggestions Note:
I am highly collaborative and completely open to adjusting my testing parameters (such as exact date-range boundaries, specific asset choices, or matrix step counts) to make your computational workflow more efficient. Furthermore, I welcome your professional suggestions regarding potential structural improvements to this multi-timeframe filtering setup. If your matrix sweeps or trade analysis reveal architectural bottlenecks or a better way to align these timeframes, please present your optimization ideas in your final report.
Long-Term Ongoing Work Opportunity:
This is not a one-off project. I have a large pipeline of trading strategies that require this exact same matrix validation and diagnostic testing. I am looking to establish a long-term relationship with a reliable, fairly-priced quant analyst. The freelancer who delivers high-quality reports on this first project will receive a steady stream of ongoing strategy testing contracts in the future.
Your Tasks & Timeframe Progression Mandate:
1. Use the attached C# cTrader source code template to run a full multi-segment grid optimization matrix across a selection of liquid assets (e.g., major Forex pairs, Indices, or Gold).
2. Timeframe Structure: Your optimization passes must strictly respect the hierarchical relationship outlined above, focusing on the 1-Minute (M1) execution chart filtered by 30-Minute (M30) and Daily (D1) trend engines.
3. Data History & Confidence: The historical data range must provide high statistical confidence. For the mandatory M1 chart, use at least 1 to 2 full years of high-quality tick data to ensure a massive trade sample size (minimum 500+ total trades). For M30 and Daily charts, scale the historical data back 3 to 5 years to capture varying market regimes.
4. Grid Dimensions: Test multiple Out-of-Sample (OOS) window sizes (e.g., 10%, 20%, 30%, 40%) against multiple rolling chronological window steps (e.g., 5, 10, 15, 20 runs) to locate a stable "parameter island" cluster where the strategy performs consistently across adjacent matrix cells.
Deliverables Required:
A. Walk Forward Matrix Heatmap Report: Visual, color-coded grid charts (PDF, SQX export, or Image format) mapping performance across the tested segments by Profit Factor, Sharpe Ratio, and Walk Forward Efficiency %.
B. Friction and Execution Cost Audit: All optimization passes—especially on the 1-Minute chart—must strictly include realistic transaction costs. You must configure your testing engine to apply standard broker round-turn commissions and a realistic, non-zero average variable spread for the asset tested. Reports generated assuming zero-spread or zero-commissions will be rejected.
C. Comprehensive Trade Analysis Breakdown: A detailed behavioral diagnosis of the final selected parameter cluster (matching institutional reporting dashboards like SQX's Trade Analysis tab). This must explicitly include:
1. Directional Symmetry: Separate performance metrics for Long vs. Short trades to detect directional biases.
2. Time-Based Distribution: Visual charts showing profit/loss distribution across hours of the day and days of the week to reveal time-dependent vulnerabilities.
3. Trade Outlier Impact: Metrics showing strategy performance with and without the top 5 largest winning trades to prove the edge doesn't rely on random statistical anomalies on the M1 chart.
Parameter Resolution Step Summary:
Provide a brief textual or data confirmation proving that the optimized settings operate inside a smooth numerical gradient (e.g., confirming that incrementing or decrementing the primary indicator variables by 5-10% does not result in an immediate, radical performance collapse).
Final Parameter Settings File:
The exact optimized parameter settings (.set file or cTrader parameter block text) derived from the dead center of the most stable cluster, ready for me to apply to my cTrader bot.
How to Apply (No Automated Bids):
To ensure you have read this description, please begin your proposal with the words "FINAL MATRIX RESULTS".
In your proposal, please specify:
1. What software or environment you plan to use to handle this specific multi-timeframe matrix setup (e.g., StrategyQuant/SQX, Python, or custom modules).
2. Where you source your high-quality, tick-accurate historical data for 1-minute testing.
3. A realistic timeline for delivering the final data reports and settings files.