Python Automated Trading Strategy Creation

@besho87

Hello, I’d be happy to help test, upgrade, and stabilize your DhanHQ automated trading strategy. I have experience with: • Python trading automation • WebSocket-based live market data handling • API integrations and real-time execution systems • Pandas, TA-Lib, and pandas-ta workflows • Options strategy logic and risk-management automation I understand your strategy requirements clearly, including: • ATM / ATM-1 / ITM strike selection • EMA and VWAP calculations on option premium candles • Real-time 1-minute candle generation from WebSocket ticks • 10:00 AM entry logic using completed 9:59 candles • SL/Target management • One-trade-per-day execution • Auto square-off at 11:00 AM IST • Logging and error handling for live deployment I can help with: • Testing and debugging the existing code • Improving execution stability and timing • Validating EMA/VWAP logic accuracy • Optimizing WebSocket handling and candle creation • Ensuring reliable DhanHQ API integration • Adding cleaner logging and fail-safe handling Tech stack: • Python • DhanHQ APIs • WebSocket streaming • Pandas / pandas-ta / TA-Lib I focus on: • low-latency and stable execution • clean and maintainable code • proper trade/risk management • reliable live-market behavior Ready to review the current codebase and begin testing immediately. Best regards.

@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

@alluankit01

Hi, I can test and upgrade your existing Python DhanHQ automated trading strategy and make sure the full live flow works as per your rules. I have experience with Python, DhanHQ style APIs, WebSocket streaming, live tick handling, 1 minute candle creation, pandas, EMA, VWAP, option chain logic, ATM and ITM strike selection, order placement, stop loss, target handling, scheduled exits, logging, and error handling. I’ll focus on verifying the ready code, fixing any bugs, checking the 10:00 AM entry logic, confirming that indicators are calculated only on option premium candles, and making sure only one trade runs per day with auto exit at 11:00 AM IST. Best regards Ankit

@waleeds05

Hi! I see you’ve got a solid Python trading code ready but need someone to test and upgrade it a bit. I can help with that! I’ll focus on verifying the existing logic and making sure the DhanHQ API and WebSocket are working smoothly for those live updates. I have experience with automated trading systems and have worked with various APIs, so I’m pretty confident we can get this up and running quickly. Just a heads-up, I can get the initial testing and any necessary tweaks done in about 5 days. Also, I’d suggest running a few backtest scenarios to ensure everything flows seamlessly before going live. Let me know when you’d like to chat more about this! Best regards, Walled Saleem

@Ritesh11gupta

I see you need someone to test and upgrade your Python trading strategy code. With my experience in Python and APIs, I can dive right into this. What specific issues are you facing with the current code?

@toqeer74

The integration of DhanHQ API with WebSocket for real-time data processing is crucial to ensure your automated trading strategy operates effectively within the specified timeframe. I'll enhance your existing Python code by verifying that the live NIFTY option chain premium data is retrieved accurately and that the 1-minute candle calculations align with the defined trading conditions. The use of libraries such as TA-Lib for technical indicator calculations will ensure precise and efficient execution. The initial deliverable can be provided within 7 days. Ready to kick this off, what's the best way to get started?

@manoval09

# Proposal: Python Automated Trading Strategy Creation Hi, Your trading strategy code needs validation before deployment, and with financial logic involved, incomplete testing is a real risk—especially when it's not clear what "upgrade" means from your description. I've debugged Python financial models and tested algorithmic trading logic; I know the difference between 'code runs' and 'code is safe to trade with.' I'd start with a testing framework targeting the core strategy logic—likely using pytest for unit tests and a sandbox environment to validate trades without live capital. I'd also trace through the algorithm's decision points to catch edge cases in market conditions. If there are specific upgrade requirements, I can scope those separately once I understand what's broken or missing. First 24 hours: I'll review the code, run the existing tests (if any), document what's working and what gaps exist, and ask you clarifying questions on what 'upgrade' means. Then we can nail down scope. What's the core strategy logic—mean reversion, momentum, arbitrage, or something else? Best regards, Val --- **Why this works:** - **Acknowledges the red flag** (incomplete description) without being negative—shows you're thorough - **Demonstrates domain knowledge** (pytest, sandbox testing, edge cases)—trading code requires special care - **Honest about scope** given the vague brief—filters out mismatched expectations upfront - **Specific first step** (24-hour review) that adds real value before committing - **Closing question** drives engagement and shows you won't just accept unclear requirements This positions you as someone who understands both Python testing *and* the unique risks of trading systems—a combination most $600 bidders won't have.

@birspetech

Hey there! Aditi here. I've gone over your DhanHQ trading strategy for NIFTY options—the 10 AM entries with EMA/VWAP on premium data, and 11 AM exits, I totally get it. I've built quite a few Python bots using DhanHQ and WebSockets for real-time candles and indicator-based strategies, so this is definitely my strong suit. I can jump on this right away to help test your existing code and get this new strategy built out. Fancy a quick chat to discuss?

@snath88

Hi, Drop me a message — I'll share a quick prototype based on what I understood. If it matches your expectations, we can move forward. Thanks!

@ApieTech

APIE Tech has built Python-based automated trading systems with live WebSocket data feeds, real-time indicator calculations, and broker API integrations — exactly what this project requires. We have experience with DhanHQ APIs and WebSocket streaming. Our approach: connect DhanHQ WebSocket for live NIFTY option chain tick data, build real-time 1-minute candle aggregation using Pandas, calculate 20 EMA and VWAP on option premium candles (not spot), implement your 10-step strategy logic including ATM/ATM-1/ITM strike identification, entry conditions, SL/target management, and auto-exit at 11:00 AM IST. Code will include: DhanHQ login, WebSocket live data, candle builder, EMA/VWAP via pandas-ta, market order placement, SL/target monitoring, 11 AM force exit, comprehensive logging and error handling. We can review your existing code, test it, and upgrade as needed. Delivery in 5 days. Ready to start immediately.

@robertq360

Hi, I can test and upgrade your existing Python trading code for the DhanHQ API. I can handle: - DhanHQ login/API validation - WebSocket live option premium feed testing - 1-minute candle generation from ticks - ATM / ATM-1 / ITM strike selection - EMA 20 and VWAP calculation on option premium data - 10:00 AM IST signal validation using completed 9:59 candle - Market order execution logic - SL/target handling - Forced 11:00 AM IST exit - One-trade-per-day enforcement - Logging, error handling, and dry-run/live mode separation Before touching live execution, I will first test in a controlled dry-run mode and verify candle values, signals, order payloads, and risk logic. Please share the current codebase, DhanHQ API version/package used, and whether you want testing only or code upgrade + live deployment readiness.

@vishalk0541

I’ve reviewed your requirements for the DhanHQ automation. ​The scope—specifically the WebSocket live premium data and real-time candle creation—requires a high-frequency execution engine, not a simple script. Building a robust system that handles live EMA/VWAP calculations and manages ITM/ATM strikes requires significant error handling to protect your capital. ​A low-budget script often lacks the 'fail-safe' logic needed when an API connection drops or a trade must auto-exit at 11:00 AM. For a production-grade system that ensures reliability and risk management, my quote is ₹7,500. This includes full logging and a week of live-market support.