Nifty BankNifty Python Algo

@LordAskar

I am a professional, experienced, and forward-thinking developer. I’ve solved similar challenges in many startups, so I’m confident I can help you too. Let’s connect and discuss the details. I’m sure we’ll find the right solution together.

@vishal1145

Your backtest will fail if you're not accounting for NSE's 3:20 PM square-off rule and the impact of slippage during volatile expiry days. Most algo traders I've worked with lose 15-20% of paper profits in live execution because their backtests assume perfect fills at mid-price. Before I architect the solution, I need clarity on two things: What's your current data granularity - are you working with 1-minute OHLC or tick-level data? And second, are you planning to trade options on these indices or just futures, because the Greeks calculation changes the entire risk model? Here's the architectural approach: - PANDAS + NUMPY: Build a vectorized backtesting engine that processes 5 years of historical data in under 30 seconds, with proper forward-fill handling for NSE holidays and muhurat trading sessions. - BACKTRADER FRAMEWORK: Implement position sizing with Kelly Criterion and drawdown controls so you don't blow past risk limits during March 2020-style volatility spikes. - TA-LIB INDICATORS: Code modular signal generators (RSI, MACD, Bollinger) with parameter optimization loops that test 10K+ combinations without hardcoding magic numbers. - PERFORMANCE METRICS: Output Sharpe ratio, max drawdown, win rate, and profit factor with Monte Carlo simulation to stress-test against different market regimes. - CSV INGESTION: Write a config-driven data loader that maps your existing NSE CSV schema to the backtest engine without touching core logic. I've built 7 algo trading systems for prop desks and retail traders, including one that's been running live on NSE F&O for 18 months with 2.1 Sharpe. I don't take on projects where the data quality is questionable - let's do a quick 15-minute call to review your historical data structure and discuss realistic slippage assumptions before I start coding.

@Shrutibimpl

You want backtesting logic. I will build a robust Python framework to test your Nifty and Bank Nifty strategies against historical data. 1) What is the specific frequency of your historical data, such as 1-minute or 5-minute candles? 2) Do you have the data already stored in CSV files or should I integrate a specific API for fetching NSE records? 3) What key performance metrics like Sharpe ratio or drawdown limits are most important for your evaluation? We will create a straightforward system where you can drop in your historical files and immediately see how your strategy would have performed. You will get a clean report showing your profit and loss, how much money you might have lost at the worst point, and a visual graph showing your account balance over time. The setup is designed so you can easily change your buy and sell rules, adjust your stop-loss, or swap to different time periods without needing to be a coding expert. I will develop the engine using pandas and NumPy for efficient data manipulation and backtrader for managing the trade simulation and portfolio lifecycle. The code will be modular, separating the data ingestion, signal generation, and performance reporting components to allow for easy updates or experimentation. I will use standard technical analysis libraries for calculating your indicators and ensure the logic accounts for realistic market slippage and transaction costs. Thanks, Bharat

@abdf2010

Hey, I noticed your project, Nifty BankNifty Python Algo and believe I can help. My work in C Programming has prepared me well for this kind of project. Looking forward to hearing your thoughts.

@prashanpatel557

Hello, I can build your Nifty & BankNifty Python backtesting algo with clean structure, accurate results, and easy customization. Approach: • Python (Pandas, NumPy, TA-Lib) • Modular pipeline: Data → Signals → Execution → Metrics • Works fully on historical data (CSV/API-ready) Strategy: • Intraday/positional logic (EMA/VWAP + momentum + price action) • Clear entry/exit + SL/TP rules Backtesting Output: • Trade logs (CSV) • Equity curve (Matplotlib/Plotly) • Metrics: PnL, drawdown, win rate, Sharpe Deliverables: • Python script (well-commented) • Configurable parameters (strategy, risk) • README (data mapping + usage) Timeline: 5–7 days I have experience with trading strategies, backtesting systems, and Python automation, ensuring accuracy and clarity. Question: Do you prefer intraday (1–5 min) or higher timeframe strategy for this build?

@UzeeTech

With my extensive 7+ years in full-stack development, I am uniquely positioned to bring your Python-based trading algorithm to life. I have a strong command of Python and commendable experience working with pandas, NumPy, TA-Lib, backtrader, and similar libraries, guaranteeing that your code will be clean, well-commented and meticulously organized. With these key skills, I'll not only ingest the historical market data but generate trade signals effectively while ensuring modular design and maximum readability. Given that accuracy is paramount in this project, my experience with building production-grade AI-powered solutions becomes invaluable. I'm proficient in TensorFlow, PyTorch and familiar with OpenAI API which can enhance the predicting power of your algorithm. I'll implement a comprehensive testing strategy to ensure reliable back-testing results. Furthermore, I promise not just an effective trading system but also detailed performance metrics and equity curve visualizations. With me on board, you can rest assured in receiving a robust algo-trading system that perfectly aligns with your needs. Remember: Pitch: Whether you need an MVP built from scratch or want a reliable developer for long-term collaboration—I'd love to partner up to build something that truly works and achieves results for you.

@WebXcellance

Hi, I’ve reviewed your requirement and I understand you need a clean, reliable Python-based trading algorithm for Nifty & Bank Nifty, focused on historical data backtesting, accuracy, and modular design — not live trading at this stage. This is a solid foundation phase, and I can help you build a well-structured, extensible backtesting system. What I will deliver: Clean, well-commented Python script / Jupyter Notebook Works with historical data (CSV or API-based) Generates trade signals + executes strategy logic Outputs detailed performance metrics + equity curve Quick Question: Do you already have: Specific strategy logic in mind (e.g., RSI + MA crossover), or Should I design a solid baseline strategy for you to build on? I can start immediately and deliver a clean, testable trading system you can iterate on confidently. Best regards, Sachin

@orfejzarada

I am an AI and quantitative trading expert with a PhD in Machine Learning and over 17 years of experience building production-grade algorithmic trading systems, and I specialize in designing clean, robust, and fully testable Python-based strategies. I will develop a modular Python solution that processes historical Nifty and Bank Nifty data, generates precise trading signals, executes backtests, and produces detailed performance analytics including equity curves and risk metrics. The code will be structured for clarity and extensibility, using libraries such as pandas, NumPy, and backtrader, with strict focus on accuracy and reproducibility of results. I will also provide clear integration instructions so you can easily connect your existing CSV or API-based historical NSE datasets without friction.

@souvikbasu

Hi! A Python backtesting engine for Nifty + BankNifty with pandas/NumPy/TA-Lib/backtrader is right in my wheelhouse — I've built three similar systems, including one intraday options-buying strategy on BankNifty that's still live with a prop desk. My plan for your milestone: 1. Data layer: ingest OHLCV CSVs or Parquet (minute/daily — your pick) with a clean loader that handles adjustments, splits, expiries. I'll include a small historical dataset for Nifty + BankNifty so you can run end-to-end from day one. 2. Strategy framework: pluggable strategy class so you can swap logic without rewriting. Built on backtrader (battle-tested, cleaner than writing a loop yourself). Risk management hooks: position sizing, stop-loss, trailing SL, max daily drawdown. 3. Indicators: TA-Lib for standard (RSI, EMA, ATR, Supertrend, Bollinger) + custom (ORB, VWAP, OI-based if you add options data later). 4. Output: trade log CSV, equity curve chart, Sharpe/Sortino/CAGR/max-DD metrics table. 5. Clear README + one sample strategy (e.g., EMA-crossover with ATR SL on BankNifty) so you can see the full loop working before extending. Two questions to sharpen the bid: - Which strategy do you want as the "first" one — do you already have one in mind, or should I propose a simple one and iterate? - Daily or intraday timeframe for this first milestone? Can deliver in 10 days with a working demo mid-way. Souvik

@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 I can deliver any project in Trading. Readymade setups for Python available

@CyberSasu

I can build algo for you. Please tell me which provider you have? dhan or angleone? Do you have your own logic?

@vishala81

Hello, I can build a clean, modular Python backtesting system for Nifty and Bank Nifty using historical data, with a strong focus on accuracy, readability, and flexibility. The script will handle: – Data ingestion from CSV/API – Signal generation based on your strategy – Trade execution logic (entry/exit, position sizing) – Performance metrics (PnL, drawdown, win rate, etc.) – Equity curve visualization I’ll structure the code using libraries like pandas, NumPy, and optionally backtrader, keeping everything well-commented and easy to modify. I have already built a similar trading/backtesting system, so I understand how to handle data alignment, edge cases, and realistic performance calculations. You’ll also get a clear README explaining parameters and how to plug in your own NSE data sources for quick experimentation. Timeline: 1–2 days Ready to start immediately and implement your strategy logic efficiently. Best regards, Vishal

@rahulb617

hi, i have done all my projects on algo tradings only in apps like shoonya,dhan,fyers and binance recently i have made bot in dhan(future/options) with proper backtesting , paper mode and auto execution of buy/sell when it meets the criteria u also want in dhan app thats great i can fulfill all your requirements lets get in touch

@chiragm078

As a seasoned full-stack developer with a deep passion for data analysis and algorithm development in Python, I am thrilled by your project description. Over the past 5 years, my obsession with detail, modular design, and efficiency have yielded top-notch digital solutions tailored to meet unique business objectives, like yours. With the skill set that involves Java and Python, I have worked on projects that span across various industries - all of which gave me the responsibility of providing sustainable solutions based on historical data. In terms of developing a Python-based trading algorithm for Nifty and Bank Nifty indices, my expertise in using libraries such as pandas, NumPy, TA-Lib, and backtrader is an undeniable asset. For me, code cleanliness, detailed commenting system, and exemplary performance are non-negotiable aspects. I can ensure that you get a clean codebase with well-documented instructions to map your existing CSVs or API endpoints. To bolster your confidence in choosing me, I proudly tout an impressive track record of 1000+ completed projects across 42+ countries. Additionally, I provide quick turnaround times (response time <2-3 hours) and support clients across various time zones. Let's collaborate – lean on my expertise to build an algo that delivers solid back-testing accuracy alongside a flexible foundation for future enhancements. Count on me to be responsive to your inputs throughout the project duration.

@emanz03

Nifty and BankNifty backtest engine with clean signals, equity curve, and CSV mapping is exactly what you described. Built a similar offline backtesting setup for index futures where strategy iterations dropped from hours to minutes and uncovered drawdown issues early before live deployment. I’ll structure this around pandas with modular signal functions and a backtesting layer using backtrader style execution so metrics stay accurate. CSV mapping will be flexible so your NSE data plugs in without rework. Do you want trade execution modeled with slippage and brokerage or keep it ideal fills for now?

@nikhily209

Hi, I've built Python-based trading systems using pandas, TA-Lib, and backtrader, and I can deliver exactly what you've described. I'll build a clean, modular script that ingests historical Nifty & Bank Nifty data (CSV or API), generates trade signals using a proven strategy, and outputs key metrics — Sharpe ratio, max drawdown, win rate, and an equity curve. The code will be well-commented, easy to configure, and comes with a README so you can tweak parameters independently. Back-testing accuracy and readability will be my top priority. Looking forward to discussing further. When can we connect?

@ViktorUmanskyi

I am ready to start immediately. What specific technical indicators are you planning to use for your initial signal logic? Let's discuss in the chat. Hi. I am a Python backend / Data automation Engineer. While many developers struggle with financial data manipulation, I specialize in building clean, modular data pipelines using Python, Pandas, and NumPy. Since you require a robust local backtesting environment without live-feed complexities, here is my exact execution plan for your Nifty/Bank Nifty project: 1. Data ingestion and preprocessing: I will write a modular data-loader using Pandas that perfectly maps to your existing historical NSE data (CSV or API dumps), ensuring timeframes and OHLCV data are correctly synchronized. 2. Backtesting engine setup: I will utilize the backtrader library to construct the core engine. I will implement your specific signal logic (using TA-Lib for indicators) into an isolated strategy class. 3. Analytics and documentation: The script will output a comprehensive tearsheet: equity curve, max drawdown, win rate, and Sharpe ratio. I will also provide a detailed, step-by-step README file explaining how you can tweak configurable parameters (like moving average periods or stop-loss limits). Why me? I am a senior-level Python engineer with a mostly 4 years of experience, but I am new to this specific freelance platform. I am willing to deliver this highly optimized code perfectly to earn my first 5-star review.

@artur1992123

The main challenge isn't just writing the algo, it's ensuring the backtesting setup accurately reflects real market conditions and slippage for Nifty/BankNifty. I've built Python trading systems using pandas, NumPy, TA-Lib. backtrader for algorithmic strategies. In the last 2 years - I've delivered 6+ Python automation and data analysis projects, including real-time trading bots and backtesting engines. I'll deliver a clean, modular Python script that imports historical data, runs your strategy logic. outputs backtest results with performance metrics, all running locally. Since I'm building my Freelancer profile, I can offer this at ₹12 500 INR for a solid review. Fast delivery, quality code, and full documentation included. Check my portfolio for relevant Python work: https://www.freelancer.com/portfolio-items/11324084 Ready to start immediately.

@RH1066

Hello, I have carefully read your project description and I am confident that I can complete this task efficiently and with high quality. I have experience in similar projects and I understand what you need. I can deliver accurate and reliable results within the given time. Why choose me: - Fast and clear communication - High-quality work - On-time delivery I am ready to start immediately and complete your project within 5 days. Looking forward to working with you! Best regards.