Nifty Options & Equity Backtesting and Simulation

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@mayankinnovative

Engineering a leak-free backtesting framework and paper-trading engine for NIFTY/SENSEX options requires deterministic execution logic, realistic friction modeling, and shared signal modules. my approach: In Python, I will develop a vectorized/event-driven backtester modeling 1-min/tick NSE F&O options data. It will incorporate dynamic ATM straddle strike selection, Heikin-Ashi, Bollinger Bands, VWAP filters, and strict cost modeling (slippage, STT, exchange turnover, brokerage) with walk-forward validation and Sharpe/drawdown metrics. For paper trading, I will build a lightweight FastAPI execution engine receiving TradingView webhook alerts via Pine Script, maintaining a shared configuration file (YAML/JSON) for SL/target toggles, live simulated position books, and FIFO P&L tracking, fully extensible to Indian broker APIs (Zerodha/Shoonya/Fyers). 1. What data format and vendor (e.g., TrueData, GDFL) are you using for historical NSE options? 2. Which broker API do you plan to target for live execution later? I am ready to build your end-to-end quant pipeline.

@michaelbukachi

Hi, I specialize in building systematic trading infrastructure and quantitative backtesting engines in Python. Here is the architectural approach for your NIFTY/SENSEX options pipeline: 1. Backtesting Engine (Leakage-Safe & Realistic Microstructure): - Core: Vectorized Python engine (pandas / NumPy / vectorbt) handling historical 1-min/5-min NSE options chains. - Indian F&O Mechanics: Automated ATM strike selection, lot sizing, expiry rollovers, realistic bid-ask slippage models, and statutory costs (STT, exchange turnover, GST). - Strategy Analytics: Equity curve, drawdown duration, Sharpe/Sortino, win rate, and comprehensive trade-level audit logs with walk-forward out-of-sample validation. 2. Paper Trading & TradingView Integration: - Webhook Server: Async FastAPI listener receiving structured JSON alerts triggered by your TradingView Pine Script strategy. - Simulated Execution: Maintains a virtual order book, tracks open positions, evaluates dynamic stop-loss/take-profit triggers, and logs real-time simulated P&L. - Shared Logic: Both the historical backtest and simulated paper trading share the same core strategy execution module to guarantee zero discrepancy. 3. Extensibility: - Strategy parameters (SL, target, indicators) exposed via clean YAML/JSON configs. - Architecture is pre-structured to hook into Indian broker APIs (Zerodha Kite, Shoonya, Dhan) for live execution in the next phase. I am open to beginning with an initial milestone covering the backtesting engine and options chain parser. Ready to discuss your strategy indicators.

@TeraByte26

Hi, I will deliver a modular backtesting engine for intraday NIFTY/SENSEX ATM straddles with Heikin‑Ashi, Bollinger and VWAP handling NSE F&O data, plus a TradingView‑linked paper‑trading interface that logs P&L, trades and allows parameter tweaks. I can complete both components within three weeks for 30,000 INR. I can share a short demo now; shall we start? Waiting for your response in chat! Best Regards.

@prashanpatel557

Hello, I can build the integrated NIFTY/SENSEX options backtesting and paper-trading system with shared strategy logic, so historical results can be compared consistently with simulated live execution. I have experience with Python algo-trading, Indian F&O/options data, Pandas/NumPy, TradingView/Pine Script, WebSockets, paper trading, and strategy backtesting. I can implement: - Intraday options backtesting with ATM straddle groups - Heikin-Ashi, Bollinger Bands and VWAP logic - Strike/expiry/lot-size handling, slippage, brokerage and margin - SL/target and realistic execution simulation - P&L, drawdown, Sharpe/Sortino, win rate and trade-level reports - Walk-forward/out-of-sample validation and leakage-safe processing - Paper-trading engine using live/delayed market data - TradingView chart integration - Dashboard for positions, P&L, trade history and strategy status - Configurable strategy parameters without code changes - Modular architecture prepared for future broker/live execution I recommend milestone-based development, starting with the backtesting engine and then building paper execution around the same strategy core. Estimated timeline: 15–20 working days Budget: ₹30,000–₹45,000, depending on data format/API availability. Regards, Prashant Patel

@Hrolgar

When the paper trader gets a webhook from TradingView, should it read TradingView's Heikin-Ashi and Bollinger values, or recompute them itself? If it trusts the alert, forward results will drift from the backtest the first time Pine repaints a bar or a candle closes a tick late. I'd compute everything in Python, in one strategy core that both the walk-forward backtest and the webhook paper trader import. The alert just tells it a bar closed, and the same code decides entries, VWAP and Bollinger conditions, SL and target for each of your groups. Indian costs (brokerage, STT, exchange charges, GST, stamp duty) and NIFTY/SENSEX expiry handling live in that core too, so both sides produce the same numbers. M1: data loader, HA/Bollinger/VWAP, ATM straddle entry and exit logic for one group, 18150 INR, 4d. M2: multi-group engine, costs, expiry mechanics, walk-forward runs and reports, 19000 INR, 5d. M3: webhook endpoint and paper-trading dashboard on the same core, 17850 INR, 5d. That's 55000 INR over 14 days. It's a starting number off the brief and I'll firm it up once I know how much option-chain history you already have, since sourcing expired-contract data is the part I can't price yet. Send me the strategy rules and a sample of the data you have and I'll come back with a tighter number tomorrow.

@prabhathk5

Hi, I’m an algo-trading developer with hands-on experience in **Python, MT5/MQL5, TradingView/Pine Script, broker APIs, WebSockets, and automated trading systems**. I can build both the backtesting engine and paper-trading interface as one integrated system. For the backtesting engine, I can implement: • NIFTY/SENSEX options strategy logic • ATM/strike selection and expiry handling • 1-min/5-min/tick historical data • Heikin-Ashi, Bollinger Bands and VWAP • SL/target, position sizing and margin logic • Slippage, brokerage and transaction costs • P&L, drawdown, win rate, Sharpe/Sortino and trade logs • Walk-forward and out-of-sample testing with leakage-safe logic For paper trading, I can build a clean Python dashboard with **TradingView webhook integration**, simulated order execution, live positions, running P&L, trade history and configurable strategy parameters. The key point is that **both backtesting and paper trading will use the same core strategy engine**, ensuring consistent results between historical testing and forward simulation. I’ll also design the architecture so a broker API can be added later for live execution without rebuilding the system. I’m open to a **paid initial milestone/POC** before the complete implementation. I can provide a modular, documented and deployable codebase with setup and architecture documentation. Regards, Prabhath

@UzeeTech

As a seasoned Full-Stack Developer and AI/ML Engineer, I understand the crucial importance of creating a well-designed, thoroughly tested solution that delivers on its promises. And that's exactly what you're looking for: a robust backtesting engine and simulated trading interface for your systematic options strategy, right? Well, let me assure you that my 7+ years of professional expertise building similar solutions make me the perfect fit for your project. Not only have I been extensively using Python (including pandas and NumPy) in my work, but I'm also well-versed with backtesting libraries like backtrader and custom-built frameworks — all skills that are central to this project. Additionally, I have an in-depth understanding of Indian market data (NSE/BSE F&O, options chain data) alongside ample knowledge of TradingView's Pine Script and connecting it with external systems using webhooks/alerts — which are all key components of your envisioned solution. On top of all this, I highly value clear communication and regular updates in order to keep clients fully informed every step of the way. This comes alongside my ability to write clean and well-documented code ensuring easy runtime maintenance not just by me but also by anyone you might entrust your solution with going forward. So let's take your strategy from concept to execution together and build something that indeed works.

@Algokage

You can check my profile to see that I've just completed jobs on backtesting engines and paper trading systems. I am an experienced quant developer with a strong background in Python and a deep understanding of the Indian market, including NSE/BSE F&O and options chain data. I have successfully built backtesting frameworks and paper trading interfaces, ensuring accurate handling of Indian options-specific mechanics and delivering clear performance metrics. My expertise lies in creating modular, leakage-safe code with proper walk-forward validation. Additionally, I have experience with TradingView, Pine Script, and integrating signals with external systems. I am confident in my ability to deliver a robust backtesting engine and a seamless paper trading interface that meets your requirements. I deliver clean, well-documented code and am open to sharing relevant samples or a quick demo upon request. I look forward to discussing this project further and providing a tailored solution to your needs. Thanks, Adegoke M.

@ParasTech7

Dear Hiring Team, Thanks for the job posting! I am a Computers grad and have worked for companies like Microsoft. I have 21 years of experience and I have done many projects in Python/NumPy/Pandas with some of them in this profile, kindly check it. If you like, I can do it :) More once you revert. Regards, Anurag.

@MKCHAND

I am a senior trading systems developer with over 12+ years of hands-on experience in designing, developing, and deploying professional-grade automated trading bots and financial tools. I have successfully built and maintained trading systems for major Indian brokers including Zerodha, Fyers, Upstox, ICICI Direct, Kotak Neo, XTS API, Stoxxo, and Shoonya, as well as Forex and international platforms such as MT5 (MetaTrader 5) and Interactive Brokers. My expertise covers: End-to-end algorithmic trading system design High-performance trading bots (equity, options, futures, and forex) Real-time data handling, order execution, and risk management Custom professional-grade GUIs for live monitoring and control Scalable, reliable, and broker-compliant architectures Before any project commitment, I am happy to provide a live demonstration of my previous work, allowing you to evaluate the quality, stability, and performance of my systems. I also offer free lifetime technical support for all delivered solutions, ensuring long-term reliability and continuous assistance. I am confident in my ability to design and deliver a fully customized, robust, and production-ready trading system tailored exactly to your requirements. Let’s connect on chat to discuss your project details in depth.

@sandipa25

Reviewed your requirement — it maps closely to work I've already built: a leakage-safe backtesting engine with proper walk-forward/OOS validation (I've caught and fixed a lookahead bug before it reached production), full options mechanics for NSE F&O (lot sizes, expiry rollovers, slippage/brokerage cost models, SL-target logic), and a specific prior backtest of an intraday ATM-straddle sell strategy on NIFTY. I also run live paper-trading deployments today, and in my architecture the backtest and live engine share the exact same strategy logic end-to-end — the backtest/simulation consistency you're asking for already exists by design. One gap I'll flag upfront rather than oversell: no prior TradingView/Pine Script webhook integration — the execution, paper-sim, and dashboard side I can deliver confidently, but the signal-ingestion bridge from TradingView would be new work for me, though it's a well-understood pattern. Open to a paid trial on just that piece first if you'd rather de-risk it before committing to the full scope.

@alfatrade2

Бэктест интрадей-опционов на NIFTY упирается не в движок, а в данные: без тиковых или минутных цен по конкретным страйкам и экспирациям результат будет красивой ложью, а бесплатных исторических данных по опционам NSE почти нет. Пейпер-трейдинг через TradingView тоже не бесплатный: алерты придут вебхуком, а не потоком цен. Я писал парсеры и пайплайны обработки данных на Python (FastAPI). Откуда у вас данные по опционам и какой таймфрейм?

@rakhshanz3

With nearly two decades of experience in academia, artificial intelligence, and software engineering, I'm confident that I can deliver the high-quality quantitative work you're seeking. Throughout my career, I've built impactful solutions around complex data problems using cutting-edge technologies. My skills in Python (pandas, NumPy), backtesting (backtrader, vectorbt), and interfacing with TradingView via Pine Script have equipped me to meet your project's demands seamlessly. To guarantee consistent and comparable results between historical backtests and forward simulations, I'll ensure that both systems - the backtest and paper trading interfaces - share the same core strategy logic. Moreover, as your project progresses towards live execution integration via broker API which isn't part of the present scope but might be future-proofed,I'll ensure my deliverables are extensible to meet that need. My breadth of experience across various stages in technical development, from architecturing to documentation is especially valuable for this project as well. Overall, my skill set combined with a collaborative approach would yield the successful outcome you desire. I look forward to sharing more details on my anticipated timeline and pricing structure after our initial trial/milestone period if we do proceed

@kanwaljeetk56

Hi, i have expertise writing strategy/backtesting and sending same systematic signals to broker already developed many algos with faster execution and linked with trading view as well. I can demo some of them and we can discuss about your requirements in details.

@pandaymano8

With a Full-Stack Web Development background, specializing in Python, I am ready and equipped to assemble the core components of your project: the Backtesting Engine and the Paper Trading/Simulated Live Execution Interface. My proficiency in technologies such as pandas, NumPy, backtrader and my well-earned experience with simulating paper trading systems make me an ideal candidate to employ. Additionally, I have prior knowledge dealing with exchange-specific features as well as options strategy mechanics which affords me the ability to attend to the intricacies of NIFTY/SENSEX options of the Indian market with complimentary ease. Operating around concepts like strike selection, lot sizes, expiry rollovers, slippage, brokerage/transaction costs, and margin/SL-target logic are not new to me. I would construct an integrated system that aligns both backtesting and paper trading functionalities sharing a consistent core strategy logic resulting in European-quality deliverables that'll ensure consistent results between historical backtests and future simulations. Choosing me is the right step toward liberation from discretionary / rule-based strategies and advancement into a fully systematized pipeline that you envisage

@michela542

Hi The most important and difficult part of this kind of project is keeping the strategy logic shared and consistent between the backtesting engine and the paper trading interface, so historical results and forward simulation stay comparable, while making sure the backtest itself is leakage-safe and reflects real execution mechanics like slippage and costs. ✅ I faced a similar challenge building a backtesting framework for a rule-based strategy that later needed to run in a simulated live environment using the same logic, the core problem was that keeping the strategy consistent across both systems while avoiding lookahead bias in the backtest required careful separation between signal generation and execution logic. I solved this by structuring the strategy as a shared core module used by both the backtester and the paper trading interface, implementing proper walk-forward validation instead of relying on in-sample results, and connecting TradingView alerts through webhooks to trigger simulated order execution, logging every trade with entries, exits, and running P&L in a clean dashboard. ✅ As a result, the backtest results and the paper trading simulation stayed consistent with each other since they shared the same core logic, the walk-forward validation gave a much more realistic view of strategy performance, and the setup left a clear path to extend toward live broker execution in a future phase without restructuring the core system. Best regards, Michel.

@abhilashr53

You want to systematize a discretionary ATM straddle strategy on NIFTY/SENSEX F&O into a testable, simulation-ready pipeline before risking live capital. That is exactly the right sequence, and I can build both deliverables cleanly. My approach: Python core using Backtrader or a custom event-driven engine, with pandas/numpy for data handling, vectorbt for rapid parameter sweeps, and TA-Lib for Heikin-Ashi, Bollinger Bands, and VWAP computation. Historical NSE F&O data ingested from Quantiacs, Unofficed, or your preferred vendor at tick/1-min granularity. Options mechanics handled properly including strike selection, lot sizing, expiry rollovers, and realistic cost modeling. Walk-forward validation baked in from day one to prevent curve fitting. For the paper trading interface, I will connect TradingView webhooks to a FastAPI layer that mirrors strategy signals into a simulated order book, tracking fills, margin, and P&L in real time against live NSE feeds via Zerodha Kite or Breeze API. Key risks I handle proactively: data survivorship bias, Greeks-aware position sizing, and signal-to-execution latency simulation. Timeline: backtesting engine in three weeks, paper trading layer in two additional weeks. One question before I scope this precisely: do you already have a historical F&O data subscription, or should I factor that sourcing into the plan?

@MrShortFlow

The core deliverable is one shared strategy module driving both the backtest engine and the paper-trading dashboard, so a straddle SL/target rule tested on history behaves identically when TradingView webhooks trigger simulated fills. That single-source-of-truth design is what prevents backtest results from diverging from paper results. Delivery in three steps: (1) data loader plus strike/expiry/lot-sizing logic and cost model, (2) backtest engine with walk-forward split and full metrics (P&L, drawdown, Sharpe, SL:target ratio, trade log), (3) paper-trading dashboard consuming TradingView alerts, with config-file toggles for SL/target/indicator thresholds. v1, the backtest core running on a sample NIFTY dataset, within 3 working days. Here is the ATM strike and lot-sizing helper, done now: def atm_strike(spot, step=50): return round(spot / step) * step def lots_required(capital, margin_per_lot, max_lots=10): return min(max_lots, int(capital // margin_per_lot)) I do not promise profitable signals, only that backtest and paper-trade rules stay consistent and leakage-safe. Can you share the historical NIFTY options data source (or existing feed access) so strike/expiry logic is built against real granularity from day one? Price: 15500 INR fixed · Timeline: 5 working days from access/brief Konrad