Optimize My Options Trading Strategy

@prashanpatel557

Hello, I can help stress-test and optimise your existing equity-options strategy using your current Python backtester and historical trade data. My expertise includes Python algorithmic trading, options strategies, backtesting, Pandas/NumPy, risk management, broker integrations, and quantitative strategy development. I’ll review your entry, exit, sizing, and existing backtest logic, then test data-driven improvements such as volatility filters, dynamic position sizing, hedging/multi-leg adjustments, drawdown controls, and regime filters where justified by the data. I’ll compare the original strategy against each modification using CAGR/returns, win rate, Profit Factor, Sharpe, maximum drawdown, expectancy, monthly consistency, and equity curves. I’ll also include walk-forward/out-of-sample validation and parameter-sensitivity analysis to reduce the risk of overfitting. The final deliverable will include the optimised Python implementation, reproducible backtests, comparison results, and a concise report explaining which changes genuinely improve robustness. One question before starting: Do you already have the 10+ years of tick/minute options data, including strikes, expiries, and sufficient data for realistic fills/slippage, or will the data sourcing also be part of the project? I’m ready to review the repository and data and start with a baseline-vs-optimised strategy assessment.

@Algokage

Hello, I hope you're doing well. I am an experienced quantitative trading professional with over 5 years of expertise in developing and optimizing trading strategies, including options trading. I have successfully delivered projects that involve back-testing, risk management, and performance optimization, using Python, pandas, NumPy, and similar frameworks. I understand the importance of robust and consistent performance, and I am confident that I can help you refine your strategy to achieve tighter risk management and more consistent month-to-month returns. I will review your existing rules and propose precise tweaks or new modules to improve expectancy, such as volatility filters or dynamic hedging. I will then implement these changes in the same Python environment or suggest a superior stack if needed. I will back-test your strategy against at least 10 years of tick or minute data, providing clean performance metrics and equity curves. I will also supply a short report explaining the rationale, parameter sensitivity, and next steps for live deployment. Please send a message to discuss the project further and share examples of past optimization or quantitative trading work. Thanks, Adegoke M.

@pravalika1995

Hi, I am algo trading for over 8 years and have created and deployed several algo strategies after extensive back and forward testing. I have also work on ML approaches to optimizing strategies across market regimes. With my background in algo trading and expertise in data science in that field, i believe i can assist you on this project. Happy to answer any questions on chat.

@shoaibkhan754400

Hello Sir This is the type of project I enjoy working on. I have experience building and improving systematic trading strategies in Python, with a focus on robust research rather than curve fitting. My work includes backtesting frameworks, walk-forward testing, Monte Carlo analysis, parameter sensitivity studies, and market microstructure research. For your project, my approach would be: * Review your entry, exit, and position-sizing rules to identify where performance degrades. * Analyze the strategy across different market regimes instead of optimizing for a single period. * Test improvements such as volatility filters, dynamic position sizing, trade filters, and risk management modules only if the data supports them. * Validate every change with out-of-sample testing, walk-forward analysis, and Monte Carlo simulations to measure robustness. I can work with your existing Python environment if it is well structured. If there is a better framework for a specific part of the research, I'll explain why before making any changes. The final deliverables will include the updated Python code, detailed backtest results, equity curves, performance metrics, parameter sensitivity analysis, and a report explaining why each modification was made and how it affects the strategy. Once we agree on the plan, you can share the repository and data, and I'll start with a full review before proposing any changes.

@Sahyadri96

Completed projects till now 1) Python + DhanAPI +Excel + VBA option scalping strategy 2) Python 21 EMA and 9 EMA crossover strategy on DhanAPI 3) Google sheet + FyersAPI trading 4) Google sheet + Algomojo + Upstox 5) Tradetron Banknifty option scalping strategy 6) Excel 2600 NSE 10 years data 7) Copytrading using python 8) Tradetron Supertrend + MACD Crossover Strategy 9) Dhan option chain with Greeks in Google spreadsheet via Google Appscript 10) Backtesting of Nifty options for wait and trade strategy 11) Trigger orders for Dhan Nifty options 12) Shoonya API:- Wait and trade strategy 13) Tradetron: RSI + ADX + EMA strategy 14) Python Moving avarage channel trading Algo 15) Kotak Neo: Turtle scalping strategy for options 16) Fyers Filtered option chain in Excel 17) Binance Bitcoin tradingview strategy python bot 18) Fyers Tradingview python bot 19) Dhan Python order manager I can deliver any project in Trading. Readymade setups for Python available

@adrianldata

My Strategic for Your Model: 1. Performance Audit & Pattern Recognition: Before changing any code, I will analyze your current transaction history to identify underperformance patterns. We need to isolate the specific market regimes. 2. Logic & Risk Optimization: Based on the data, we will decide whether to adjust your existing rules or inject new dynamic boundaries. I specialize in mapping elastic volatility filters to protect capital and adjust risk sizing dynamically rather than statically. 3. Stress Testing: To ensure the improvements are robust and not just "curve-fitted" to past data, I will run strict Monte Carlo simulations across the logic, proving mathematically that the expectancy is tangibly improved. 4. The "Clearly Superior Stack": You mentioned processing 10 years of tick/minute data using Pandas. Pandas is highly inefficient for this volume. I propose refactoring the heavy data-processing core to Polars. My Portfolio & Proof of Competence: I recently built and open-sourced my own real-time quantitative trading engine. It features strict modular architecture, asynchronous timeframe crossing without future contamination, and zero-latency analytical storage using DuckDB. You can audit my architectural style and algorithmic logic Let's discuss your current drawdowns, and we can define the exact parameters for the audit.

@Rafiul471

Ok then let's try..i will help you to find exact market movement position entry and exit.. If i can't give you exact positions what i told. you don't have to pay me nothing. Give a try

@toptruelancer

Hi, I’m an experienced quantitative developer with 7+ years in algorithmic trading, Python, Pine Script, MT4/MT5, and options strategy development. I can review your existing strategy, transaction history, and backtester, then systematically optimize **entry/exit logic, position sizing, volatility filters, hedging, and multi-leg adjustments** based on data rather than curve-fitting. I’ve worked on **options strategies, Greeks/risk modelling, Monte Carlo analysis, walk-forward testing, backtesting, and automated trading systems** across Indian and global markets. I can implement the improved strategy in Python using Pandas/NumPy and your existing framework, or recommend a better stack if required. Deliverables: * Strategy audit & optimization plan * Robust 10+ year backtesting * Walk-forward/out-of-sample validation * Drawdown, Sharpe, CAGR, expectancy, win rate & risk metrics * Equity curves and parameter sensitivity analysis * Optimization report with live-deployment recommendations * Clean, maintainable source code My focus will be **robustness and risk-adjusted consistency rather than simply maximizing backtest returns**. I can also share relevant quantitative trading/option automation work and examples of previous optimization projects. Once you provide the repo and data, I can start with a detailed strategy audit.

@nandezgarcia

Hi, I can help optimize your options trading strategy using Python. I will analyze historical data, backtest different parameters and provide performance metrics. I can also suggest improvements based on statistical analysis and risk management principles.