Validate CRT, VWAP Futures Trading Strategy

@intelliresponse

Hello, I’d be excited to help validate your CRT and VWAP strategy through a rigorous, data-driven research process. My approach is to translate discretionary trading concepts into objective, reproducible rules and evaluate them using robust statistical methods rather than relying on isolated backtest results. I'll build a modular Python research framework that calculates session VWAP, deviation bands/z-scores, detects CRT sweeps and reclaims, and evaluates CRT-only, VWAP-only, and confluence-based signals across each specified market and trading session. The framework will incorporate realistic assumptions, including commissions, slippage, chronological out-of-sample testing, and parameter sensitivity analysis to minimize overfitting and provide reliable conclusions. The final deliverables will include well-documented Python source code, trade-level CSV files, comprehensive performance reports, and detailed analytics such as expectancy, drawdown, MAE/MFE, monthly results, long vs. short analysis, time-of-day performance, and VWAP deviation comparisons. If desired, I can also develop a TradingView indicator that mirrors the validated logic with session VWAP, deviation bands, CRT levels, confluence signals, and alert support. I look forward to discussing your data source and research methodology to ensure the analysis is both statistically sound and fully reproducible.

@ShaikhAneesa

Hi there, I understand you need a quantitative researcher with strong Python and statistical modeling skills to validate a futures trading strategy based on CRT session sweeps/reclaims, VWAP, and VWAP deviation analysis across multiple instruments and trading sessions. I am confident I can build a rigorous research framework that converts the trading concept into objective rules and provides statistically reliable insights into whether the strategy has a measurable edge. My approach is to first translate the CRT and VWAP methodology into precise, testable conditions by defining sessions, signal logic, entry/exit rules, VWAP calculations, deviation thresholds, and risk parameters. Next, I'll develop a reproducible Python backtesting pipeline using one-minute historical data to construct five-minute signals, incorporating realistic commissions, slippage, chronological out-of-sample testing, and sensitivity analysis to reduce overfitting risk. Finally, I'll perform detailed performance analysis across YM, GC, and SI markets, comparing CRT-only, VWAP-only, and combined confluence results while delivering trade-level data, statistical metrics, reports, and complete source code. Will you be providing the one-minute historical futures data, or would you like the data sourcing and preparation process included as part of the research workflow? I'm ready to start immediately. Warm Regards, Aneesa.

@gutibs

Hi! This is something we can handle. The scope is well-defined, which helps. One thing I'd want to clarify before quoting: where does the 1-minute historical data come from? If you already have a source (Databento, Rithmic, CQG, a flat file), that's fine — if not, data acquisition and any licensing costs need to be factored in, since clean futures tick/minute data isn't free. A couple of smaller things: for the VWAP deviation threshold, do you have a starting hypothesis (e.g., 1.5–2.5 standard deviations) or should we define a reasonable search range as part of the research? And for CRT reclaim, do you have a written rule for what counts as a confirmed reclaim, or is translating that concept into code part of the work? We'd build this in Python — pandas/numpy for signal logic, vectorbt or a lightweight custom loop for the backtest, clean modular code so you can re-run it on new data. The deliverable would be everything you listed: trade-level CSV, performance report with the full breakdown per instrument/session, and all source code with instructions. The TradingView Pine Script indicator is doable within budget if the core research doesn't run long. Happy to move forward once those data questions are sorted. Gustavo & the DoTheCode team

@gotucat

Hi, I reviewed the request to validate a futures strategy using session VWAP, VWAP deviation (z-score), and CRT session-range sweeps with reclaims, then compare CRT-only, VWAP-only, and their confluence via rigorous statistical testing. I’ll translate the trading concept into objective rules across YM/MYM (New York) and GC/MGC, SI/micro Silver (London/New York), with Eastern Time session definitions, 1-minute data to form five-minute signals, and realistic execution timing. I’ll build a reproducible Python backtest that detects highs/lows, sweeps, and reclaims, computes statistical extensions from session VWAP, and runs chronological out-of-sample testing while tuning nearby parameters to reduce overfitting. I’ll deliver trade-level CSV plus a clear performance report with commissions, slippage, and month/time-of-day breakdown. Let’s discuss here now.

@mubeenm5026

As a software development company with a strong grounding in Python, Web Crest offers a unique and comprehensive approach to quant research that aligns perfectly with your project needs. Our long-standing experience in areas such as SaaS platforms, automation systems, and cloud technologies equips us to deliver the kind of reproducible Python code you're looking for. We also have the necessary expertise to transform complex trading concepts into objective and testable rules - a key requirement for your project. Additionally, we understand the significance of accuracy and granularity when working with financial data, particularly with regards to VWAP, VWAP deviation, and z-scores. Our meticulous attention to detail during calculations will provide you with reliable statistical outputs for each session and market you require. What sets us apart is our blend of technical proficiency and business-focused approach. We don't just treat this task as a mathematical exercise but are driven by the larger question of how these signals impact real-world decision-making. With Web Crest, you don't just get an analysis; you get actionable insights. Trust us to skillfully navigate through all variables like commissions, slippage, entry timing, and trading time frames in order to provide an accurate reflection of your strategy's performance. Furthermore, we're open to any specific instructions or preferences on how you'd like your CSV and report to be structured for an in-depth analysis.

@Hubready

Hello!, I am a US-based senior software engineer(frontend, backend, ecommerce, etc) and I read your CRT + VWAP futures strategy validation project carefully. You are not looking for a generic backtest, but for a real quant research pass that shows whether the edge is valid, where it fails, and how the risk behaves. I have about 15 years of experience in Python, statistical analysis, financial markets, risk management, and backtesting. I’ve built trading tools, data pipelines, and research systems where the goal was not just “does it work?” but “does it survive costs, slippage, and out-of-sample testing?” My approach: 1. Validate the data and strategy rules 2. Rebuild CRT and VWAP logic precisely 3. Run a realistic backtest with execution assumptions 4. Measure expectancy, drawdown, win rate, Sharpe, and robustness 5. Deliver a clear summary of what is tradable and what needs work Relevant work includes Python market research tools, futures signal validation scripts, and statistical backtesting dashboards for small trading teams. Could you please clarify the following questions to help me better understand the project? 1. What exactly does CRT mean in your setup, and are the rules already defined? 2. Which futures markets and timeframe are you targeting? 3. Do you already have historical data, or should I help structure the source/format? If helpful, I can start with strategy validation first, then move into a full backtest and concise research report.

@sufyanasmat38

Hi, I understand the objective is to rigorously validate a futures trading strategy by quantifying the relationship between CRT session sweeps/reclaims and VWAP statistical extensions across multiple instruments and trading sessions. The focus is on evidence-based research, robust backtesting, out-of-sample validation, sensitivity analysis, and determining whether the combined signal provides a measurable edge over CRT-only or VWAP-only approaches. My experience includes Python-based quantitative research, statistical analysis, backtesting frameworks, market data processing, algorithmic trading research, and performance analytics. I have developed reproducible research pipelines that incorporate realistic assumptions such as commissions, slippage, walk-forward testing, parameter sensitivity, and detailed trade-level reporting to minimize bias and overfitting. I can translate the strategy into objective rules, build a clean and reproducible research framework, generate comprehensive performance reports, and deliver well-documented Python code that allows future testing and refinement. The goal will be to provide statistically meaningful conclusions, not just backtest results, so you can confidently evaluate whether the strategy has a genuine and repeatable edge.

@topcreator100

Hello, With my strong background in Computer Science and extensive experience working with Python, I am confident that I possess the necessary quantitative research and analysis skills to successfully execute this project. Developing the strategy into objective and testable rules, calculating session VWAP and VWAP deviation, identifying key market indicators like session highs, lows, sweeps, and reclaims are just some of the areas where my proficiency lies. Moreover, I understand the importance of accurate and credible analysis when it comes to trading strategies. From accounting for commissions, slippage, and realistic entry timing to conducting chronological out-of-sample testing and evaluating sensitivity and overfitting, I am dedicated to ensuring a comprehensive and meticulous approach to the task. Additionally, being adept at building reproducible Python backtests and providing detailed performance reports, I guarantee a transparent record of the results achieved for each instrument and session. In conclusion, my skill set combined with my commitment to delivering clean code solutions on time aligns perfectly with your project requirements. Let's connect to discuss your goals in more detail! Thanks!

@sdqtrades

As a seasoned quant research specialist and Python developer, I bring an impressive range of skills that perfectly align with the complex demands of your project. My experience in backtesting and optimizing trading strategies will be instrumental in turning your concept into objectively measurable rules that can produce consistent results for all the instruments and sessions you've outlined. Furthermore, I have an acute understanding of the statistical underpinnings of trading strategies. I'll leverage this knowledge to calculate session VWAP and VWAP deviation, detect vital session highs, lows, sweeps, and reclaims, and build a reproducible Python backtest that accounts for commissions, slippage, and realistic entry timing. Drawing on my financial analysis expertise, I'll meticulously conduct chronological out-of-sample testing using nearby parameters, providing you with comprehensive insights on the sensitivity and any potential overfitting. In sum, choosing me for this project means choosing an efficient combination of in-depth knowledge on trading strategies validation with automation scripting skills ready to create a Python model matching your idea. I'm result-driven and adaptable as per user needs secure under NDA considerations and always provide clean code and timely delivery. Let's collaborate to unravel what works verifiably best among CRT alone, VWAP alone and combining signals!

@PINESCRIPTERR

Hi, I can convert the CRT/VWAP concept into explicit, testable rules and build a reproducible Python research pipeline without automated execution. I would use pandas/Polars, NumPy, and statistical libraries to construct five-minute signals from one-minute futures data while preserving realistic intrabar sequencing. Session definitions would use timezone-aware Eastern Time with DST handling. The research would explicitly define CRT reference ranges, sweep distance, reclaim confirmation, VWAP reset rules, z-score calculation, entries, exits, and invalidation conditions before testing. The framework would compare CRT-only, VWAP-only, and confluence signals consistently across every instrument/session. It would include commissions, configurable slippage, contract rolls, tick values, chronological train/validation/test splits, parameter-neighbourhood sensitivity, confidence intervals, and safeguards against look-ahead and survivorship bias. Deliverables would include documented Python source, configuration files, trade-level CSVs, validation checks, charts, and a report covering all requested metrics with evidence graded as supported, preliminary, inconclusive, or unsupported. Results would include losing periods and unstable parameter regions, not only favourable outcomes. A Pine Script indicator can then reproduce the frozen validated rules without changing the research logic. Relevant examples can be shared privately where client permissions allow. Regards, Houssame

@seefattechnologi

Hi, The strategy needs objective validation, not curve fitting. I will convert the CRT and VWAP concepts into deterministic rules, build a reproducible Python research pipeline, and validate each market with out of sample testing, slippage, commissions, and parameter sensitivity analysis. My Approach: • Build session based VWAP, CRT sweep/reclaim detection, and statistical deviation models in Python. • Compare CRT only, VWAP only, and confluence signals with trade level analytics and robustness testing. • Deliver reproducible source code, CSV outputs, performance reports, and an optional TradingView indicator. One question: Will you provide the historical one minute futures data, or should I source and normalize it as part of the project? Please ping me to get started and deliver outstanding results. Thanks!!!

@gowthamsarathy

Hello, I'm a futures trader myself and I trade GC, mgc, nq, mnq , below is my complete approach i trade only on NY session I look for liquidity sweep (PDH/PDL, PWH/PWL,)then I drop to 3min time frame look for fvg, enter into the fvg and i usually target 1:3 RR, if price shows good aggression I'll trail my SL based on the lastest fvg's that are formed. Here's how I'd approach the project: I'll first translate your discretionary concepts into objective, testable rules, then build a modular Python research framework to calculate session VWAP, deviation bands/z-scores, CRT sweeps and reclaims, and perform chronological out-of-sample testing for each instrument and session independently. The workflow will include realistic commissions/slippage, parameter sensitivity analysis, detailed trade-level statistics, monthly performance, MAE/MFE analysis, and a direct comparison of CRT-only, VWAP-only, and confluence signals. If the budget allows, I'll also deliver a TradingView indicator that mirrors the validated logic with alerts. Relevant Work: https://www.freelancer.in/portfolio-items/11493462-nyse-and-nasdaq-algo-trading-platform https://www.freelancer.in/portfolio-items/11493278-algorithmic-trading-bot-for-mt5 https://www.freelancer.in/portfolio-items/11493491-mt4-mt5-trade-copier Kind regards, Gowtham

@rabbiawebjuggler

As an experienced data analyst and Python developer, I am confident in my ability to effectively address the challenges of your project. I have a strong background in quantitative analysis, which I have applied to various domains, including financial markets and trading strategies like the one you are looking to validate. I can help you translate your trading concept into meaningful and objective rules using accurate calculations such as session VWAP, VWAP deviation or z-score, and make sure that the strategy is implemented consistently across all selected markets and sessions for accurate comparison.

@emilesd617

Hi, I am a Python quantitative developer with 8 years of rich experience in software development. I am familiar with Python, futures markets, statistical analysis, backtesting, risk management, pandas, NumPy, data modeling, and TradingView Pine Script. I can translate the CRT sweep/reclaim and VWAP deviation concepts into objective rules, construct five-minute signals from one-minute data, and compare each instrument and session using chronological out-of-sample tests. The analysis will include realistic costs, sensitivity testing, trade-level CSV data, the requested performance metrics, reproducible source code, and a clear evidence rating for every market and session. I'm an individual freelancer and can work in any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@ashinnd84

Hi, Your project "Validate CRT, VWAP Futures Trading Strategy" is a good fit -- Python backend and automation work is my main line of work. How I would run it: 1. Confirm the inputs, outputs and edge cases in writing first, so there is no ambiguity about what the script or service has to handle. 2. Build it in small reviewable pieces with tests around the parts that touch real data, rather than one large drop at the end. 3. Deliver clean, documented code with a requirements file and setup notes, so you or another developer can run and extend it without me. Matching your listed skills: Data Analysis, Backtesting, Financial Analysis, Statistical Modeling, Python, Statistics, Statistical Analysis, Software Architecture, Financial Markets, Risk Management. My bid is $638, within your $250-750 range. Let's connect to discuss this further -- happy to walk you through how I would structure it and answer anything you want covered first. Thanks for your time. Best regards, Ashish & Team

@mahads31

As a quantitative expert with a strong background in Python, I bring the valuable skills your project requires. My in-depth experience in statistical analysis and system design perfectly aligns with your needs of validating and conducting research on the CRT and VWAP futures trading strategy. I have worked extensively in Python to create backtests, process historical data, and validate various financial strategies. I fully understand the need for accuracy and detail while accounting for factors like commission, slippage, VWAP deviation, etc. Moreover, my ability to conduct out-of-sample testing and avoid overfitting will ensure that your strategy is robust enough to handle future market situations. With mastery over Python and deep knowledge of markets like YM/MYM, GC/MGC, and SI/Micro Silver during the London and New York sessions, I believe I can bring a unique perspective to your study. Furthermore, I’m confident to provide you with not only the highly sought-after analytical performance metrics but also well-commented source code for future reference.

@mdabdullahallm5

I have strong experience building reproducible Python research frameworks for quantitative trading and can translate discretionary CRT and VWAP concepts into objective, testable rules. I'll develop a robust backtesting pipeline using 1-minute data to generate 5-minute signals, evaluate CRT-only, VWAP-only, and confluence setups with realistic commissions, slippage, parameter sensitivity, and out-of-sample validation, then deliver fully documented Python source code, trade-level CSVs, comprehensive performance reports by instrument and session, and, if budget permits, a TradingView indicator implementing the validated strategy with alerts.

@mayr81

I’ll validate the CRT sweep/reclaim and VWAP-deviation logic with rigorous, reproducible Python research across YM/MYM (NY) and GC/MGC + SI/micro-SI (London/NY). The work will convert your ideas into unambiguous, testable rules: Eastern Time session windows, session VWAP, VWAP z-score/deviation bands, and algorithmic detection of confirmed CRT highs/lows, sweeps, and range reclaims. Signals will be generated from 1-minute data to form five-minute decision points, with realistic entry timing, slippage, and commissions. The backtest will produce chronological out-of-sample results and include sensitivity checks near key parameters to reduce overfitting. For every instrument and session, I’ll report trade-level CSV plus a written performance breakdown: trade counts, win rate, profit factor, net expectancy, average winner/loser, drawdown, MAE/MFE, holding time, monthly trends, long/short splits, time-of-day behavior, results by VWAP deviation threshold, and “return to VWAP” rates. I’ll also compare CRT-only vs VWAP-only vs combined confluence and conclude each market/session as supported, preliminary, inconclusive, or not supported. If desired, a lightweight TradingView indicator/alerts mirroring the validated rules can be delivered, prioritizing correctness over aesthetics.

@emanz03

The real risk here is producing attractive backtest results through session leakage, contract-roll errors, ambiguous sweep rules, or unrealistic fills. I’d first freeze objective CRT reclaim and VWAP-deviation definitions, normalize one-minute futures data into Eastern Time, construct five-minute signals without look-ahead, and model commissions, slippage, session boundaries, and contract transitions explicitly. The research would compare CRT-only, VWAP-only, and confluence setups by instrument and session, with chronological out-of-sample testing, parameter sensitivity, trade-level CSVs, and reproducible Python code. Do you already have continuous one-minute data with rollover handling, or should data preparation be included?