Nifty Options Backtesting Strategy

@masummsnr

Hi, You need a clean, reusable framework to replay your Nifty option-buying rules on a year of historical data, with exact entry/exit points, R:R settings, and a trade log that reconciles to the plotted equity curve. I'd build it in Python (pandas), so results stay reproducible. Parameters for entry, exit, and risk-reward exposed as inputs you can tweak. It ingests historical Nifty options data, handles every expiry without gaps, matches official closing prices within tolerance, plots trades, and exports the trade-by-trade log plus a summary (win rate, avg R:R, total P/L) to CSV/Excel. First step: send me a sample of your data format and your rough trade logic. I'll confirm the expiry coverage and map the rules before writing code, so no surprises at delivery. I can deliver a runnable script with install notes in 4 days. Regards, Nurullah Al Masum

@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

@Ritesh11gupta

I see you're looking for a backtesting setup for your Nifty option-buying strategy. I have solid experience with Python and pandas, which should fit your needs well. What key features are you hoping to include in the script?

@rocks081

Hi! I've built several backtesting frameworks using Python (pandas, backtrader, and custom implementations) and this Nifty options project is right up my alley. Here's my approach: 1. **Data Pipeline**: I'll source Nifty options historical data (NSE bhavcopy or reliable data providers) and build a clean ETL pipeline to handle all expiries without gaps. 2. **Backtesting Engine**: Python-based framework using pandas/NumPy with: - Configurable entry/exit rules (price thresholds, Greeks, time-based) - P&L calculation with brokerage/tax modeling - Risk-reward ratio per trade - Trade-by-trade log exportable to Excel/CSV 3. **Visualization**: Matplotlib/Plotly charts showing equity curve, drawdowns, and trade markers overlaid on price data 4. **Report**: Summary stats — win rate, average R:R, Sharpe ratio, max drawdown, total P/L I have experience with financial data processing and options. I can deliver a clean, well-documented script within 5-7 days. A couple of quick questions: - Do you have specific data sources in mind or should I handle data acquisition? - Is there a preferred strike selection method (ATM, OTM by delta, etc.)? Looking forward to building this for you!

@enricos0

Hi there, Imagine having a backtesting setup that not only meets your expectations but exceeds them. I specialize in creating robust frameworks that allow traders to analyze their strategies with precision. With extensive experience in options backtesting, I can ensure your Nifty approach is clean, professional, and user-friendly. I noticed you need a solution that can replicate precise entry and exit points while seamlessly integrating with historical data. Whether it’s Python or Excel VBA, I can build a system that exports performance metrics to CSV or Excel for thorough analysis. I create solutions that are automated and efficient, allowing you to focus on refining your strategies. My commitment to speedy communication and fast turnaround has led to many returning customers. I am available for a quick chat! Regards, Enricos0

@abishek129

Hi, this is a well-scoped backtesting project that fits my Python and financial data pipeline experience. I'd build this in Python using pandas for data handling and a custom backtest engine rather than Backtrader, since Nifty options have specific expiry logic (weekly and monthly) that generic frameworks handle awkwardly. Entry/exit logic, P&L calculation, and R:R tracking all go in a config-driven module — you define the rules in a YAML/JSON file, the engine replays them against historical data without touching the core code. For historical data, I'd integrate with NSE's official data or a provider like Sensibull/Opstra/TrueData for options chain history (strike prices, premiums, volumes) going back at least one year including all expiries. The engine handles expiry rollover automatically so you don't get gaps at weekly expiry boundaries. Deliverables include a runnable Python script with installation notes, parameter config (entry conditions, exit triggers, stop-loss, target, R:R), chart visualization of trades on the options price series using Plotly, and export to CSV/Excel with trade-by-trade log reconciling to the equity curve. Performance summary covers win rate, average R:R, total P&L, max drawdown, and per-expiry breakdown.