Backtest Nifty Straddle Strategy

@prashanpatel557

Hello, I can build a fully reproducible backtesting framework for your Nifty weekly straddle strategy with configurable entry time, stop-loss percentage, re-entry logic, and end-of-day square-off rules. I have experience developing Python-based options backtesting systems for Nifty, Bank Nifty, and other derivatives strategies, including intraday straddles, strangles, and multi-leg option setups. My Approach: • Use Python with Pandas for transparent and reproducible backtesting • Parameter-driven configuration for: - Entry Time - Stop-Loss % - Re-entry Cut-off Time - Market Close Exit Time • Weekly Nifty option selection logic • Intraday-only trading (no overnight positions) • Unlimited same-day re-entries until cut-off time • Trade-by-trade logs and detailed analytics Performance Report Includes: • Net P&L and CAGR • Maximum Drawdown • Win Rate • Profit Factor • Average Trade Statistics • Re-entries per Day Analysis • Equity Curve and Drawdown Charts Deliverables: • Well-commented Python source code • Backtest report for the last 3+ years • Excel/CSV trade logs • Setup guide and parameter documentation Timeline: 12–15 days, depending on data availability. Please share: • Your preferred entry time • Stop-loss logic (combined premium or individual legs) • Historical options data source • Re-entry cut-off time I can start immediately once the strategy rules and data source are shared.

@techmadigital

Hi, I can build this backtest in Python with all parameters configurable from a single settings section, including entry time, stop loss percentage, re-entry cut-off time, and market close exit. The backtest will: • Enter nearest weekly Nifty ATM straddle at the specified time • Apply percentage-based stop loss to the entire straddle • Re-enter automatically after stop loss until the cut-off time • Exit all positions before market close • Ensure no overnight positions Deliverables: • Well-commented Python code • Complete trade log • Performance report with P&L, max drawdown, win rate, and re-entry statistics • Setup and parameter modification guide Before starting, I'd like to know which options data source you have available (Opstra, TrueData, GDFL, Sensibull, etc.) since backtesting option strategies requires historical option chain data. I can start immediately once the data source is confirmed.

@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

@KaleRiddhesh

I have checked your requirement about Nifty Strategy and I can complete this project at best price. You can use your Custom Strategy or Sensibull Ready to Use Strategy. Please message me so that we can discuss and get this project done before Monday

@jaspinder019

the code can be absolutely done by me. I can build a robust Pine Script indicator or strategy with multiple indicator logic, structure detection, retest conditions—basically anything you want in Pine Script can be coded. I just need a clear and detailed description of your concept and full requirements. Once we discuss everything, I can start the work and deliver clean, well-commented code with user-friendly inputs and proper documentation, exactly as per your needs.

@sakibh178

Hello, I have reviewed your Nifty straddle backtesting requirements and understand the complete workflow, including timed entries, percentage-based stop loss, same-day re-entries, and mandatory end-of-day exits. I can develop a clean and well-documented Python backtesting solution with all key parameters configurable from a single location. The final delivery will include source code, trade logs, performance metrics (P&L, drawdown, win rate, re-entry statistics), and setup instructions so you can easily reproduce and modify the results. Please let me know your preferred historical data source and whether you already have the data available. Looking forward to working with you.

@kobeyb

Hi — quick confirmation I've understood the strategy, then how I'll build it: Entry: one ATM straddle on the nearest weekly Nifty expiry at a fixed clock time each day. Combined-premium stop — a single % SL that exits both legs together. Re-entry: if stopped before your cutoff time, re-enter a fresh straddle under the same rules, repeating until cutoff. Hard square-off at market close, never carry overnight. Deliverable: a single clean, well-commented Python script with every parameter (entry time, SL %, cutoff time, lot size) in one config block at the top — you change one place and rerun. Output: a trade log (CSV) plus a 3-year performance report covering cumulative P&L, max drawdown, win rate, and re-entry frequency. The trade log itself proves zero overnight positions. On data: tell me what you have — a CSV of weekly Nifty option-chain / minute data is ideal. If you don't have a set, I'll recommend a source and flag any cost upfront, no surprises. Everything is built to reproduce on your machine with the same dataset. I can start today. Want me to share the config-block layout first so you can confirm the parameters before I run the full backtest?

@csrishavjaiswal

Hi, I can build a fully reproducible backtest for your Nifty weekly straddle strategy in Python. What I will deliver: • Clean, well-documented Python code with all parameters configurable from a single settings section. • Daily time-based straddle entry on nearest weekly Nifty options. • Percentage-based stop loss with easy optimization capability. • Automatic same-day re-entry whenever the stop loss is triggered before the specified cut-off time. • Mandatory end-of-day square-off with zero overnight positions. • Detailed trade log showing entries, exits, stop-loss events, and re-entries. • Performance report including Net P&L, Max Drawdown, Win Rate, Profit Factor, and average re-entries per day. • Exportable CSV/Excel reports for further analysis. Recommended stack: Python + Pandas + Backtesting Framework (Backtrader/vectorbt) using high-quality historical Nifty options data. Before starting, I would need: 1. Preferred entry time. 2. Stop-loss percentage. 3. Re-entry cut-off time. 4. Historical data source (or I can recommend one). I have experience working with quantitative trading systems, options strategies, and backtesting workflows, and I will ensure the results are fully reproducible on your machine using the same dataset. Looking forward to discussing the project.

@vivekb016

Hello, I can build a clean and reproducible backtest for your Nifty straddle strategy with all key parameters kept adjustable in one place. I would prefer using Python with Pandas because it is transparent, easy to rerun, and suitable for detailed trade-log generation and performance analysis. The backtest will include: • Daily entry at user-defined time • Nearest weekly Nifty options selection • Percentage-based stop loss applied to the full straddle • Same-day re-entry after stop loss until cut-off time • Mandatory square-off at market close • No overnight positions • Full trade log with entry, exit, SL hit, re-entry count, and P&L • Performance report with cumulative P&L, max drawdown, win rate, and re-entries per day I will keep parameters such as entry time, stop-loss %, re-entry cut-off, lot size, and exit time in a single config section so you can easily optimise them later. For data, I recommend using clean historical Nifty options intraday data. If the data source is paid or requires an API key, I will clearly flag it upfront before implementation. Deliverables will include well-commented Python code, setup instructions, parameter-change guide, trade logs, and a concise performance report. Best regards, Vivek Bonagiri

@Armansingh1145

Hi, I can build a clean and fully reproducible backtest for your Nifty weekly straddle strategy in AFL (AMIBROKER). The solution will support: • User-defined daily entry time • Nearest weekly Nifty straddle selection • Adjustable percentage-based stop loss • Automatic same-day re-entry after stop loss • Multiple re-entries before a configurable cut-off time • Mandatory end-of-day square-off with no overnight positions Deliverables: ✓ Well-commented AFL code ✓ Centralized configuration for all parameters ✓ Detailed trade log for verification ✓ 3+ years backtest report including: * Cumulative P&L * Max Drawdown * Win Rate * Trade Statistics * Re-entries per day I will ensure the results are fully reproducible using the same dataset and provide clear instructions for modifying entry time, stop loss, re-entry cut-off, and exit settings. Before starting, I would like to confirm: 1. Do you already have historical Nifty options data? 2. Should the stop loss be applied on the combined straddle premium or on individual legs? I have experience developing rule-based trading and options backtesting systems and can deliver a reliable, transparent solution matching your exact specifications. Looking forward to working with you. Best regards AJAY KUMAR

@hanshengh

Hi, I can build this as a small reproducible Python backtest with parameters in one config section: entry time, stop-loss %, re-entry cutoff, square-off time, lot size, and fees/slippage assumptions. The output would include a trade log with no overnight positions, daily P&L, cumulative P&L, max drawdown, win rate, re-entry counts, and a short report/notebook. I’d keep the logic easy to audit so your spot checks can map directly to individual trades. Quick question: do you already have 3 years of Nifty option chain data, or should I structure the code around the data vendor/export format you plan to use?