Real-Data Trading Bot Optimization & Validation

@ZohaibRoy

⭐⭐⭐⭐⭐ Options Trading Bot - Real Data Validation and Fix ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project requirements and see you are looking for help with your options trading bot. Look no further; Zohaib is here to assist you! My team has successfully completed 50+ similar projects for trading bots. I will focus on validating real historical data, conducting rigorous backtests, and ensuring the bot meets the necessary criteria to go live again. ➡️ Why Me? I can easily handle your options trading bot project as I have 5 years of experience in Python programming, quantitative finance, and options pricing. My expertise includes data analysis, algorithm development, and backtesting strategies. Additionally, I have a strong grip on market microstructure and ensuring data integrity. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Python Programming ✅ Quantitative Finance ✅ Options Pricing ✅ Algorithm Development ✅ Data Analysis ✅ Backtesting Strategies ✅ Market Microstructure ✅ Risk Management ✅ Code Debugging ✅ Statistical Analysis ✅ Look-Ahead Bias Testing ✅ Transaction Cost Analysis Waiting for your response! Best Regards, Zohaib

@ShaikhAneesa

Hi there, I understand this is a real-data validation and backtesting engagement for a halted Schwab options trading bot, where the priority is an honest statistical conclusion rather than forcing profitability. I can rigorously test the strategy using actual historical options-chain data, realistic execution assumptions, and strict out-of-sample validation while preserving the existing safety controls and live-server isolation. My approach is to first audit the existing Python backtest, strategy logic, position sizing, and look-ahead audit. Next, I’ll source and normalize 3–5 years of historical options-chain data with bid/ask, fills, spreads, and underlying prices, validating selected contracts against Schwab data. Then, I’ll run a pre-registered train/test backtest using time-series splits, realistic commissions/slippage, account-equity sizing, Profit Factor, expectancy, average R, drawdown, and robustness tests. Finally, I’ll independently verify every signal uses only information available at decision time and run the existing 47-check look-ahead audit plus anti-overfitting/parameter-stability tests. You will receive the validated dataset, reproducible Python backtest, audit results, performance statistics, and a clear go/no-go conclusion without any live orders or deployment. Do you already have a licensed historical options-chain data source, or should I identify a suitable source with historical bid/ask and trade data? Warm Regards, Aneesa.

@intelliresponse

Hello, I have experience developing and analyzing algorithmic trading systems, quantitative models, and data-driven strategies using Python, statistical analysis, and financial data processing. I understand the importance of validating trading strategies with realistic market data instead of relying only on backtests with assumptions. For this project, I can help audit the existing options trading bot, review the strategy logic, identify potential sources of false performance, and rebuild the validation process using proper historical options data, realistic transaction costs, and unbiased testing methods. I will focus on finding the truth of whether the strategy has a real edge rather than forcing a positive result. My approach includes reviewing the current codebase, checking for look-ahead bias, overfitting, data leakage, incorrect position handling, and statistical weaknesses. I can build rigorous backtests with proper train/test separation, risk analysis, performance metrics, and realistic execution assumptions. I have experience with Python, quantitative finance, algorithmic trading, statistical modeling, and backtesting frameworks. I can provide an honest, data-backed assessment and help improve the system only where the evidence supports it.

@devendrathakur12

Hi, I’m a Senior Quantitative Software Engineer with 20+ years in Python and trading systems. I have gone through your specific requirement for real data options validation. I built something like this for trading clients, with 20+ years across quantitative software, including WorldQuant, where research depends on strict out of sample testing. I would use real OPRA options quotes rather than Black Scholes estimates because modeled prices cannot expose actual spreads, fills or slippage. I will build a Python backtest around the historical chain data and preserve your existing look ahead audit. Walk forward testing will keep the hypothesis fixed before evaluation, while position sizing uses the account equity and actual option liquidity. And I’ll report negative expectancy plainly if it remains negative, with every assumption and failed test recorded, at least that is where I would start. Relevant Python, quantitative and trading samples I can send. Which historical options data provider are you currently considering or already have access to? Which Schwab ledger fields should be used for the contract level spot checks? Can you provide the existing backtest and 47 check look ahead audit? Free for a quick call this week? Or answer those three and I’ll map out the validation first. Dev Singh

@toriquldev123

Hello There! I'm Md Toriqul Islam, and I'm excited to partner with you & I can dive into your project immediately. I'm a Python developer with a quantitative finance background, experienced in rigorous backtesting and real market microstructure data validation for options strategies. I understand you need real historical options-chain data sourced and validated against live Schwab quotes, a pre-registered backtest with a defined hypothesis, train/test split, real transaction costs, and honest position sizing, and a demonstrated positive expectancy that passes your existing look-ahead-bias audit before any code touches the live server, with a clean negative result treated as a fully valid outcome. I've handled similar rigorous, honesty-first quantitative validation projects before. I am skilled in Python, options pricing and quantitative finance, backtesting methodology, and market microstructure data analysis including bid-ask spreads, fills, and slippage. I can walk you through my approach to avoiding overfitting and look-ahead bias, including train/test discipline and pre-registration of hypotheses, once we connect. I'm ready to start right away and happy to discuss any details. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@PINESCRIPTERR

Hi, I would keep the live bot completely untouched and treat this as a quantitative validation project, not a strategy-tuning exercise. I’d source real historical options-chain quotes, validate spot contracts against Schwab records, and pre-register the hypothesis, parameters, costs, sizing, and train/test split before running results. The backtest would model bid/ask spreads, liquidity, slippage, commissions, expired contracts, and account-level risk. To prevent false positives, I’d use strict time-based out-of-sample and walk-forward testing, preserve your existing 47-check look-ahead audit, and report every tested configuration. A negative result would be delivered plainly rather than optimized away. Which historical options-data provider and exact Schwab trade ledger fields are currently available? Regards, Houssame

@ImranAli01

Hi there, I went through your project description and understand you need rigorous Python and quantitative analysis to validate an options strategy against real historical options-chain data, without deploying or placing any live trades. I will source and validate 3–5 years of historical options-chain data, including bid/ask, fills, slippage, and contract-level pricing, then spot-check it against Schwab records where available. I will establish the backtest methodology before reviewing results, maintain strict train/test separation, model realistic transaction costs and account-level position sizing, and document every assumption. I will use Python, Pandas, NumPy, and appropriate statistical methods to reproduce the strategy and evaluate profit factor, expectancy, average R, drawdown, and out-of-sample performance. I will explicitly test for look-ahead bias, leakage, overfitting, and sensitivity to market conditions. No live files or orders will be touched. Could you share the existing backtest and look-ahead audit scripts so I can preserve the established validation framework? I will share a project blueprint within 24–48 hours of award. All communication will take place directly through the Freelancer platform. Kind Regards, Imran Ali

@shrec

Hi, I appreciate how clearly you’ve defined the goal here. I would approach this as a validation exercise, not as an attempt to force the strategy into profitability. The first priority would be obtaining genuine historical options-chain data and validating it against the Schwab account data for representative contracts. From there, I’d establish the backtest methodology before evaluating results, with strict train/test separation, realistic bid-ask spreads, slippage, transaction costs, and position sizing based on the actual account equity. For overfitting, I’d keep the hypothesis and evaluation rules fixed before reviewing performance and avoid changing parameters based on out-of-sample results. I’d also run the existing look-ahead audit and investigate any failures rather than working around them. If the honest result is negative, I’d report that clearly with the supporting numbers. Best, Ivane

@TeraByte26

Hi Alex, I will obtain 3‑5 years of actual options‑chain data, run a pre‑registered train/test backtest with real transaction costs and position sizing, and deliver the unbiased profit factor, expectancy and bias‑audit results. I’ll complete this within 10 days, matching your £250 cap. I can send a small sample data extract now to confirm format; shall we begin? Waiting for your response in chat! Best Regards.

@prashanpatel557

Hello, This project requires exactly the right approach: rigorous validation using real options data, not optimisation until a backtest looks profitable. I have experience with Python-based trading systems, quantitative analysis, broker APIs, backtesting, risk management, and strategy validation. My approach would be: - Review the existing codebase and previous validation methodology - Source and validate suitable historical options-chain data with bid/ask information where available - Build a reproducible, event-driven backtest using realistic fills, spreads, slippage, commissions, and account-level position sizing - Define hypotheses and evaluation rules before testing to avoid result-driven optimisation - Use strict train/test separation and out-of-sample validation - Apply walk-forward testing and parameter sensitivity analysis - Run and preserve your existing look-ahead-bias audit on all new logic - Check survivorship bias, data leakage, timestamp alignment, and unrealistic fill assumptions - Compare results against appropriate benchmarks and report expectancy, profit factor, drawdown, average R, and robustness I fully agree that a negative result is valuable if it is methodologically correct. I will not guarantee profitability or modify the live trading system during this engagement. The final outcome will be a transparent, reproducible analysis showing whether the strategy has a genuine edge on real historical options data.

@usmanbashir12100

Hi, I understand the objective is rigorous validation, not optimization toward a desired result. I would build a reproducible research pipeline using real historical options-chain data, pre-register hypotheses and evaluation rules, enforce chronological train/test separation, model bid-ask spreads, fills, slippage and realistic sizing, and run the existing look-ahead audit against every revision. I would report the results transparently, including a clean negative conclusion if no statistically defensible edge exists. A few questions: Which historical options-data vendors or datasets are available or preferred for obtaining 3–5 years of contract-level bid/ask data? Can the existing backtest framework and look-ahead audit script be isolated from all live-trading code for a fully offline research environment? What exact strategy parameters are currently considered fixed versus legitimate hypotheses that may be tested under the pre-registered research plan? Best regards, Muhammad Usman

@huonghy89

Hi, I hope you are doing well. Very happy to bid your project because my skills are fitted in your project. I have strong experience in Python-based quantitative trading, options analytics, backtesting, market-data processing, risk management, and diagnosing automated trading systems using real execution constraints. I am comfortable working with historical options chains, bid/ask data, slippage and commissions, Black-Scholes/Greeks, walk-forward validation, and bias/overfitting audits, and I fully agree that a rigorous negative result is preferable to an artificial positive one. I will first validate and normalize 3–5 years of real historical options-chain data, including spot checks against available Schwab records, without modifying or executing any live-trading code. I will pre-register the strategy hypothesis and evaluation criteria before testing, use strict chronological train/validation/test separation, realistic spreads, fees, slippage and account-level sizing, and prevent look-ahead leakage by ensuring every decision uses only information available at that timestamp. I will run the existing 47-check bias audit plus robustness, walk-forward and sensitivity tests, then deliver reproducible Python code, datasets, detailed performance metrics, and a clear conclusion showing whether positive out-of-sample expectancy actually exists. If you send the message, we can discuss the project more. Thanks.

@albertc1996

1. Can you provide access to historical real options-chain data or recommend reliable data sources for the last 3-5 years to validate against the Schwab account? 2. Is there an existing framework or codebase for the look-ahead bias audit script and backtesting environment that the freelancer should integrate with? I understand the critical need to validate your options trading bot using real historical options-chain data, moving beyond theoretical models to ensure profitability and robustness. My approach involves sourcing verified real market quotes for the past 3-5 years, performing a rigorous, pre-registered backtest with clear train/test splits, incorporating real transaction costs, and using your account's actual equity for position sizing. I will integrate the provided look-ahead bias audit script into the backtest to ensure statistical honesty. Throughout, I will avoid any live server changes or orders, strictly adhering to your non-negotiable rules. I am ready to collaborate closely to discuss data access, backtest parameters, and deliver a transparent report demonstrating whether positive expectancy can be achieved before any live deployment. Looking forward to your response to begin refining the bot's validation process.

@prabhathk5

Hi Sir Hope you doing well! I’ve reviewed your requirement and would like to discuss it further. I’m Prabhath — an MQL4/MQL5, Pine Script, Python, and C++ developer with strong experience in building trading systems, advanced studies, and institutional-grade algorithmic solutions. I develop Expert Advisors, indicators, dashboards, data tools, and custom trading utilities for MT4/MT5 and TradingView. As an active trader, I work with concepts like ICT, SMT, market structure, liquidity models, order blocks, FVGs, VWAP, and volume-based logic. I build EAs and indicators that follow precise rules and match the user’s trading methodology perfectly. My expertise includes: Institutional-grade EA and indicator development ICT/SMT-based trading systems Pine Script indicators and automated strategies Python tools for data analysis, signals, and external integrations C++ modules for high-performance components Backtesting, forward testing, and full strategy optimization Strategy development, refinement, and consulting I prefer a short call to confirm all details before starting. This ensures perfect clarity and avoids misunderstandings. I fix and optimize existing trading code by removing errors, correcting logic issues, and making your EA/indicator/bot stable and production-ready. Thank you.

@Algokage

Hello, I hope you're doing well. I am an experienced software developer with a strong background in quantitative finance and options pricing. I have successfully delivered projects that integrate complex financial models and real-market data, ensuring accurate and reliable results. My expertise lies in developing and optimizing trading bots, particularly those that require real-data validation and risk management. I understand the specific challenges you're facing with your options trading bot. The current shutdown is a result of a lack of real historical options-chain data and potential issues with entry selection. To address these concerns, I propose a rigorous approach. I will utilize real historical data for the last 3-5 years, validated against the live Schwab account, to conduct a pre-registered backtest. This backtest will include a defined hypothesis, a train/test split, real transaction costs, and real position sizing. By ensuring a statistically honest analysis, I can provide a clear and honest answer about the bot's profitability. I am committed to delivering a clean, well-documented solution that meets your requirements and avoids overfitting and look-ahead bias. Thanks, Adegoke M.

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in Risk Management, Python As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@richard031596

You need an honest Python/options backtest on real historical chain data, with no live trading and no forced positive result. I’ve built Python backtesting and financial-modeling workflows where the main work was data validation, transaction-cost modeling, train/test splits, slippage assumptions, and proving whether a strategy survives out-of-sample. Your shutdown logic makes sense: with 81% drawdown and negative model-priced tests, this should stay off until real options data proves otherwise. My approach would be to first validate the options-chain source against Schwab spot checks, freeze the hypothesis before testing, run the strategy with actual bid/ask, fills, sizing, and costs, then pass the existing look-ahead audit. I’ll report profit factor, average R, drawdown, streaks, and failed variants clearly. A clean negative result will be stated as such. Can you share the current repo, audit script, and preferred options-chain data source?

@mahads31

As someone who understands the importance of data analysis and strategic problem-solving, I believe I can offer unique value to your project. My experience in web and software development, automation, and statistical analysis provides me with a strong foundation to tackle complex challenges head-on, such as the optimization of your options trading bot. I am well-versed in multiple programming languages that should easily accommodate the scope of your project. My python expertise is robust and includes fluency in utilizing real data for in-depth statistical analysis, an essential skill for testing hypotheses and assessing performance expectancy. Finally, my successful track record of designing long-term digital products will especially benefit you: I understand the importance of producing code that not only functions effectively but also stands the test of time. By employing data-driven strategies and thorough code Review, I won't just fix bugs or implement new features but will ensure the overall competence of your trading strategy. Ultimately, I'm dedicated to providing real solutions through innovative approaches, and I'm confident this mindset would serve us well in livening up your bot safely and profitably.

@Georgeoustar

Hi, there! I understand this engagement is about proving or disproving the strategy honestly using real historical options data, not tuning a backtest until it looks profitable. I’ve worked on similar Python quantitative research involving options data, bid/ask spreads, slippage, fill assumptions, position sizing, and out-of-sample validation. My approach would lock the hypothesis first, use point-in-time option chains, separate train/validation/test periods, apply realistic costs, avoid any parameter tuning on the final test set, run walk-forward and stability checks, and pass your existing look-ahead audit before drawing conclusions. Best regards, George