Optimize Python Algo Trading System

@AhmedMohamedSZ

I have solid experience in Python based algo trading for intraday Nifty options using live broker APIs and real time data streams I will carefully review and fix the candle close and VWAP value handling so orders and stop losses are placed at exact intended prices without changing your core strategy

@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

@Rsolitario

I can correct the discrepancy in your algorithm's data reading to ensure that Stop Loss orders precisely match the 2-minute candle's high (like the 88.80 example) and not inaccurate approximations. I understand that closing accuracy and VWAP value are critical for your Nifty options strategy on the Angel One Smart API, so I will focus on debugging the synchronization between the real-time data stream and your processing logic. I will analyze your current code to identify whether the error stems from Pandas data resampling or latency in capturing the closing event, ensuring accurate execution. I will also implement rigorous validation for automatic market exit and pending cancellation at 15:14:59 with microsecond precision. I have advanced Python experience developing algorithmic trading systems on AWS and manipulating financial time series. I accept the payment terms after successful two days of validation in real-world transactions and estimate having the fix ready for testing within 24 hours. I look forward to reviewing the code and getting started.

@samimbusiness

I have thoroughly analyzed your project and I specialize exactly in Python algo trading systems with real time API integration, Pandas, NumPy, and live market execution. I understand the issue with incorrect candle closing rates and VWAP values causing wrong execution and Stop Loss orders. I can inspect your existing code and correct it while keeping the core logic intact. I will also implement the 15:14:59 square off functionality so all open positions exit instantly at market rate and pending Stop Loss orders cancel automatically. My approach ensures precise execution, zero delays, and reliable intraday trading. I am ready to deliver a tested solution that works flawlessly in live market conditions.

@cloudaxisltd

I’m confident I’m the ideal person for your project, as I understand your need for a clean, user-friendly solution that accurately reads candle closing rates and VWAP values from your broker’s API stream. Fixing the incorrect order placement and automating the square-off process at 15:14:59 to seamlessly cancel pending Stop Loss orders aligns perfectly with your requirements. While I am new to freelancer, I have tons of experience and have done other projects off site. I’m skilled in Python, Pandas, NumPy, and API integration, with hands-on experience in real-time data analysis for algo trading systems. I would love to chat more about your project! Regards, Henning Munnik

@Cassiusred

I understand the issue. Your algo is likely reading stale or mid-candle data instead of waiting for the actual 2-minute candle close. Here is what I will fix: 1. Candle Close Timing Issue: - Ensure the algo waits for the exact candle close time before reading the close rate - Add proper synchronization with the broker timestamp, not system time - Verify OHLC data is complete before processing 2. VWAP Indicator Synchronization: - Ensure VWAP is calculated on fully closed candles only - Add validation checks before placing orders 3. Stop Loss Precision: - Fix the order placement to use exact candle high values (88.80 instead of 88.05/88.20/88.50) - Add logging to track exactly what values the algo sees vs what it places 4. Exit at 15:14:59: - Implement precise market order for all open positions - Cancel all pending SL orders automatically - Use atomic operation to ensure both happen together I have experience with Angel One Smart API, Pandas for OHLC data handling, and Indian market intraday trading systems. I agree to the 2-day live testing before payment. Can you share the codebase so I can diagnose the exact issue?

@mjfind

Hi there, I’ve gone through your project details carefully. I’m an experienced Python developer and algo trader with 5 years of market experience, trading Nifty options intraday using custom strategies integrated via broker APIs I understand your issue with the algo not picking the exact candle close and VWAP values, leading to incorrect execution and stop-loss levels. I can inspect your current streaming and order routing logic, fix the precision/rounding or data-read issue without altering your core strategy, and also implement the auto square-off and stop-loss cancellation at 15:14:59 as requested. I’ve handled multiple live algo setups using Python, Pandas, NumPy, and AWS, so I can ensure your system runs accurately and smoothly in a real trading environment. Let’s connect and review your existing code — I can start immediately.

@felixc21

Hi there, I am an algorithmic trading developer specializing in high-frequency and statistical arbitrage systems. While I build my core institutional bots in Rust for maximum speed, I have deep experience optimizing Python trading engines for latency and stability. How I will optimize your system: Execution Speed: I will profile your code to find bottlenecks and replace slow loops with vectorized operations (Pandas/NumPy) or async/await structures to reduce API lag. API Stability: I have extensive experience handling connection drops and rate limits with brokers like IBKR (TWS/Gateway) and Deriv. I will ensure your bot recovers automatically from disconnects. Logic Verification: With my background in Smart Money Concepts (SMC) and Arbitrage, I can review your strategy logic to ensure it isn't repainting or miscalculating entry prices. Why me? I don't just write code; I trade. I understand the difference between "clean code" and "code that executes orders fast." I am available to start immediately and can provide a free initial review of your slowest function to demonstrate my value. Best regards,

@kmlydv

I’ve carefully reviewed your issue and I clearly understand where the problem is coming from. In live market streaming, candle close, high and indicator values often differ from what is visually seen on charts if the stream aggregation or candle finalization logic is slightly off. This usually happens due to incorrect handling of tick-to-candle conversion, timing alignment, or using partial candle values instead of finalized candle data. I have hands-on experience debugging Python-based algo trading systems that consume live broker APIs, work with Pandas/NumPy calculations, and run on AWS. I will inspect the streaming logic, candle formation, VWAP calculation, and execution price mapping without changing your core strategy logic. I also understand the importance of precise execution prices and correct SL placement in intraday options trading. Regarding the square-off requirement, I will implement a reliable time-based exit at 15:14:59 that instantly closes all open positions at market price and cancels all pending SL orders safely. I’m comfortable testing this in live market conditions and will ensure stable performance before final delivery.

@LiamKJ

Your algo’s core issue is the inaccurate capture of candle close and indicator values, causing erroneous order placements and stop loss levels—undermining the execution of your intraday Nifty options strategy. If left uncorrected, these errors will continue eroding trade precision and profitability, while incorrect square-off timing risks unwanted overnight positions. With extensive experience diagnosing and refining real-time trading algos under live API conditions, I can isolate the data ingestion fault and implement the precise timing exit you require without altering your core logic. Having delivered similar projects successfully off-platform, I offer a strategic discounted rate to earn trust and demonstrate value here with minimal risk on your part. If you want, I can review your code and clarify next steps with a brief follow-up. Liam Jasson

@SamyCh24

Hello, I am a Python developer with experience in algorithmic trading, real-time market data processing, and API integration with brokers. I have worked on intraday trading systems for options and equities, including strategies based on candlestick patterns, VWAP, and precise order execution logic. I understand your issue: your algo is not reading candle closing rates and VWAP correctly, causing incorrect order and Stop Loss placements. My approach will be: Inspect your existing Python code that streams data from Angel One’s Smart API. Identify and fix the parsing or calculation errors that misread candle closing and VWAP values. Implement an accurate Stop Loss placement logic using exact high/low rates of the candles. Add the 15:14:59 square-off logic to close all positions instantly at market price and cancel all pending Stop Loss orders. I will not alter your core strategy—only correct the data handling and execution logic. I can deliver a fully tested version that runs correctly in live market conditions within 7 days. Questions for clarity: Should the 15:14:59 square-off run even if some orders are partially filled, or only on fully open positions? Do you want me to include logging for each executed order for debugging and audit purposes? I am confident I can optimize your algo to perform exactly as intended. Best regards, Samy C.

@sayarsinghseo

Drawing from my extensive experience as a Data Scientist, specializing in Data Analysis, Pandas, and Python, I am confident in my ability to successfully optimize your Python algo trading system. I have a comprehensive understanding of stock market trading systems and the intricacies of algo trading which uniquely positions me to address and resolve the issues plaguing your current algorithm. I have worked extensively with various APIs and data streams including AWS, all of which will prove invaluable in my efforts to identify the root cause and enhance the accuracy of your system. Moreover, my strong grasp of machine learning will greatly facilitate my inspection and correction work without compromising on your core logic. As an added advantage, I understand the significance of timing in trading; this is reflected in one of the core corrections you've tasked me with - placing exit orders for existing positions at exactly 15:14:59. This minute but crucial detail signifies that I understand not only how things work operationally but also how they work in context. As for payment, I believe in delivering results and ensuring your satisfaction before compensation - a testament to my dedication to each project's success. With me on board, you can rest easy knowing that your algo trading system is in experienced hands that genuinely care about its efficacy and performance.