Python Trading Algorithm Development

@Milvetti

Hi, we can help you with your Python automated trading system (real-time data, day/swing engine, risk mgmt, backtesting, paper/live). We offer lifetime bug fix guarantee. As Milvetti, we help traders automate their strategies. Price is an estimate and may vary by scope.

@sdqtrades

As an experienced and multi-skilled Python developer, I am confident that I can help you achieve your goal of building a robust, fully automated trading system. With solid expertise in algorithm development and backtesting in finance, my core competencies align seamlessly with your project requirements. I have a proven track record of creating successful trading scripts and bots for various platforms including Tradingview, MetaTrader, Thinkorswim, and more. Not only do I have extensive knowledge of Python 3.x and all the mentioned libraries such as pandas, numpy; I also have hands-on experience with strategies like WebSocket handling for streaming quotes that sync perfectly well with the stack you've listed. Additionally, I ensure that every codebase I develop is clean, well-commented and designed to be easily understandable for long-term maintainability. This aligns with your preference for tweaking key parameters effortlessly without rewriting code. My commitment doesn't stop at delivering the complete source code and setup instructions but goes beyond by providing you with a comprehensive video/written walkthrough that demonstrates the algorithm performing successfully in both backtest and paper-trade modes. Let's turn your trading ideas into profitable realities together!

@iglobalvn

I can build a Python trading system that reliably ingests live market data, executes rule-based day/swing strategies, and protects capital with disciplined risk controls from day one. I’m a strong fit for this project because I’ve worked on Python-based automation where clean architecture, low-latency data handling, and testable strategy logic matter. For your system, I’ll design separate modules for data streaming, signal generation, order execution, risk management, and backtesting so each part can evolve without breaking the rest. Key strengths I’ll bring: • Real-time WebSocket/API integration for live equity and forex feeds • Backtest-ready strategy engine with configurable day-trade and swing rules • Risk framework covering sizing, SL/TP, max drawdown, and paper-trade safeguards I’ll use Python 3.x with pandas/NumPy and a reliable trading framework such as backtrader, plus configuration files so you can tune parameters without code changes. I’ll also provide clean comments, setup steps, and a walkthrough showing both backtest and paper-trade modes running end to end. My approach: confirm broker/API details first, wire live data ingestion, implement the strategy and risk layers, validate with historical data, then harden the paper-trading flow before handoff. If you’d like, I can outline the milestone plan and architecture before starting.

@joomextensions

With over a decade of experience in web and mobile application development, Harmis Technology is the perfect choice for your Python Trading Algorithm project. Our team's expertise in Python, coupled with our strong understanding of different APIs and websockets, allows us to seamlessly integrate data from brokers and APIs into complex algorithms like the one you're envisioning. Not only do we have an excellent command over relevant libraries such as pandas, NumPy, TA-Lib/ta, and backtrader, but we also provide clean and well-commented codebase to ensure ease of extension and simple configuration when tweaking parameters. Moreover, our proficiency in UI/UX design ensures your final product won't just be highly functional but also visually appealing. Our investment in modern technologies such as Flutter can aid in developing a comprehensive walkthrough video to show your algorithm functioning efficiently in both backtest and paper-trade modes. To maintain utmost transparency, we don't outsource projects and you can count on us for premium quality work right from the initial milestone. Choose Harmis Technology to redefine your trading system with intelligent algorithms powered by cutting-edge technology!

@tnt42010

Hey there, I have solid experience building end-to-end Python trading systems including real-time data ingestion, multi-strategy engines (day + swing), risk management layers, and backtesting frameworks, and I can deliver a clean, modular solution using tools like pandas, NumPy, TA-Lib, and Backtrader with WebSocket-based low-latency data feeds; I’ll structure the system so strategies are easily extendable, parameters configurable via files, and execution stable with proper logging, safeguards, and paper/live modes—everything documented and demonstrated clearly so you can run and evolve it confidently; let’s connect and get your trading system built the right way, Taher

@vishal1145

Your biggest risk isn't the algorithm - it's the execution layer. If your WebSocket connection drops mid-trade or your stop-loss logic fails during a flash crash, you'll lose money before you even know there's a problem. I've built 4 production trading systems where uptime and failover handling mattered more than strategy alpha. Before I architect this, I need clarity on two things: What's your broker API (Interactive Brokers, Alpaca, OANDA?) because each has different rate limits and data structures that affect how we handle reconnection logic. And what's your target latency - are we talking sub-100ms execution for scalping or is 500ms acceptable for swing entries? Here's the architectural approach: - WEBSOCKET + ASYNCIO: Build a non-blocking data pipeline with automatic reconnection and heartbeat monitoring so you never miss a tick during network hiccups. - BACKTRADER + CUSTOM RISK MODULE: Extend backtrader's position sizer to enforce max drawdown limits and dynamic stop-loss adjustment based on ATR, preventing catastrophic losses during volatile sessions. - NUMPY VECTORIZATION: Pre-compute indicator arrays (RSI, MACD, Bollinger) in batches instead of recalculating on every tick, reducing CPU load by 70% during live execution. - CONFIGURATION-DRIVEN STRATEGY: Store all parameters (timeframes, thresholds, position sizes) in YAML files so you can A/B test strategies without touching the codebase. - PAPER TRADE VALIDATION: Run parallel shadow execution against live data to catch edge cases before risking capital. I've built similar systems for 2 prop trading firms where execution reliability was non-negotiable. Let's schedule a 15-minute call to walk through your broker's API docs and confirm the data feed structure before I start wiring connections.

@mayankinnovative

Hi, As per my understanding: You are building a high-performance, modular automated trading system in Python for equity and forex markets. You need a dual-strategy engine capable of handling both intraday and swing trading, backed by rigorous risk management, real-time WebSocket data ingestion, and a backtesting framework. The goal is a production-ready, configurable system that minimizes latency and allows for seamless transitions from backtesting to paper trading. Implementation approach: I will architect the system using a modular class-based structure, separating data handling, strategy execution, and risk management into distinct modules. I will utilize Backtrader or a similar event-driven framework to ensure consistency between backtesting and live environments. WebSocket integration will handle live data streams using high-efficiency event loops to maintain low latency. I will use YAML configuration files for all strategy parameters, risk limits, and API settings, ensuring you can tweak the system without touching the logic code. Finally, I will conduct a structured walkthrough demonstrating data flow, trade execution logic, and the paper-trading safety mechanisms. A few quick questions: 1. Which specific broker or data provider API are you planning to integrate for market feeds and order execution? 2. Are you looking to store historical data locally in a database (like InfluxDB or SQL) for faster backtesting?

@KarthikTechCon

Hi, I’m Karthik from Resonite Tech with 15+ years of experience in Python, algorithmic trading systems, automation, and data engineering. Your project is a strong fit. I can build a clean, modular Python trading platform that supports both day-trading and swing-trading strategies, with live market data ingestion, strategy execution, risk controls, and backtesting in one extensible codebase. I can deliver: Real-time broker/API integration with WebSocket streaming Separate strategy engine for intraday and swing rules Risk management layer with position sizing, SL/TP, and drawdown limits Backtesting and paper-trading workflow before live deployment Config-driven setup for easy parameter tuning Clean, well-documented Python code using pandas, NumPy, TA/TA-Lib, backtrader or a suitable framework I focus on building stable, testable systems with clear logging, modular architecture, and easy future enhancements. I can also provide setup instructions plus a walkthrough showing both backtest and paper-trade execution. Happy to discuss your broker/API, preferred framework, and milestone plan so the first build connects to live data smoothly. Warm Regards, Karthik B Resonite Tech

@Shrutibimpl

Hello There, You want a versatile automated trading system for equity and forex markets. I will build a live Python engine that handles fast data streaming and manages both day and swing strategies with strong risk controls. 1) Which broker or data provider API will we use for the live WebSocket feed? 2) Do you prefer PostgreSQL or local files for storing historical data and trade logs? 3) How many concurrent symbols must the system monitor? We will build a reliable partner that executes your strategies without error. You will gain a clear view of your performance while the system protects your capital through strictly enforced risk rules. This setup allows you to scale across markets simultaneously, giving you more time for research instead of manual order entry. I will build the framework using an asynchronous architecture to minimize latency during market hours. The system will integrate pandas and TA Lib for indicator calculations, while the strategy engine will use a modular design to swap between day and swing logic. I will implement a local backtesting suite to ensure your rules are statistically sound, and the final code will include YAML configuration files for easy parameter tuning. Best regards, Bharat Joshi

@MqlcoderHub

Hi there, this is exactly the kind of Python-based automated trading system I’ve built before for clients running both intraday and swing strategies. You can review their feedback attached to this proposal. I can build your system with real-time data ingestion, a dual strategy engine (day + swing), and a robust risk management layer. I’m experienced with Python trading stacks (pandas, NumPy, TA-Lib, backtrader, WebSockets), so the system will be clean, scalable, and reliable in live conditions. Quick questions: • Which broker/API will you be using for live data and execution? • Do you already have defined strategies, or should I help structure the day vs swing logic? Let’s connect in chat to discuss further.

@prashanpatel557

Hello, I can build your end-to-end Python trading system (intraday + swing) with a clean, extensible architecture and reliable live execution. Approach: • Python (Pandas, NumPy, TA-Lib) + WebSocket streaming • Event-driven pipeline: data → strategy → execution → monitoring • Broker/API integration (Alpaca / IBKR / Forex APIs) System Design: • Separate engines for day trading & swing strategies • Shared risk module (position sizing, SL/TP, max DD) • Config-based parameters (no code edits needed) Core Features: • Real-time data ingestion • Multi-strategy execution engine • Order management (place/modify/exit) • Logging + audit trail Backtesting: • Integrated framework (Backtrader/VectorBT) • Metrics: PnL, Sharpe, drawdown • Same logic reused for live Reliability: • Auto reconnect + error handling • Safe execution checks Deliverables: • Full modular codebase • Backtest + paper trading setup • Config files + documentation • Demo walkthrough Timeline: 15–20 days I have experience building trading systems, automation pipelines, and backtesting frameworks, ensuring practical deployment. Question: Which broker/API do you want to prioritize first for integration?

@UzeeTech

With over 7 years of professional experience, I have a deep understanding of Python and a knack for building high-performing algorithmic solutions. I have successfully developed numerous end-to-end trading systems that align precisely with your requirements. My proven foundation and expertise in back-end architecture using Python will expedite the process of integrating external data sources like brokers/APIs and streamlining them into real-time data ingestion systems. Moreover, my familiarity with libraries such as pandas, NumPy, TA-Lib/ta, backtrader and WebSocket handling will furnish your project with the necessary tools to create a strategy engine capable of executing diversified rule-sets for both day-trading and swing-trading. Additionally, my proficiency in risk management techniques ensures that position sizing, stop-loss, take-profit, and max drawdown guardians align effectively within your trading algorithm. Above all, I pride myself on delivering clean code combined with thorough documentation. A codebase that is not just understandable but can also be easily extended by you in the future is what I stand for. Lastly, if we embark on this journey together, you can count on detailed setup instructions and comprehensive insights on running the algorithm both in backtest and paper-trade modes. Let’s breathe life into your trading vision together!

@Data78enthusiast

Hello, I am a Algo trader, by profession. I have helped many clients develop their personalized trading systems. I have just wrapped up a similar work for a clint. I can help you with this work and showcase some of my previous work if you wish to connect. Let me know if you would be interested. Thanks.

@mahads31

With over a decade of experience in Python and software architecture, I am the perfect match for your Python Trading Algorithm Development project. I have a comprehensive understanding of the financial market and years of working with similar projects, enabling me to anticipate potential obstacles and deliver a robust and efficient solution that meets your exact requirements. One of my key strengths lies in developing high-performance, real-time systems while keeping latency to an absolute minimum. This aligns perfectly with your need for low latency data ingestion which is absolutely vital for effective intraday decision making. Moreover, my expertise in risk management strategies, position sizing, stop-losses, and take-profits will ensure the safety and success of your trades. I also bring significant experience in building clean, well-commented codebases with simple configuration files, allowing you to easily tweak parameters without re-writing the entire code – saving you valuable time and resources. As part of the deliverables, I will provide you a complete source code along with setup instructions, and a video or written walkthrough showcasing the functioning algorithm through both backtest and paper-trade modes.

@loxtic

Hi, how are you doing ? I have considerable experience with algo trading, using indian broker apis like, zerodha kite, kotak neo, angelone, dhan, etc.. as well as International exchanges including Interactive Brokers, robinhood, alpaca etc. Across different securities like, Stocks, Futures n Options, Crypto, Forex Have built multiple such platforms, including currently running, live ones, which executes trades for over 1000+ users at the same time. Can demo previous work if needed. let me know further if interested

@natanr28

Hi there, I like that you’ve defined this as a complete trading system with live execution, backtesting, and risk controls—not just a strategy script. That makes the architecture especially important. You need a Python trading system with real-time data ingestion, separate day/swing strategy logic, robust risk management, and a modular backtesting + paper trading framework. I have experience with Python, APIs, real-time data handling, and algorithmic systems, and I’d approach this with Python, pandas, NumPy, WebSockets, and Backtrader (or a custom execution layer depending on broker/API fit). My approach: 1. Build live data ingestion + broker connectivity with configurable architecture 2. Implement modular strategy engine for day/swing rules with risk controls 3. Add backtesting + paper trading mode for validation before live deployment 4. Deliver clean source code, setup documentation, and walkthrough of both modes First milestone: live data connection + framework skeleton in 5–7 days. Full MVP timeline depends on strategy complexity. I work milestone-based, can start immediately, and focus on making each stage testable before moving forward. Which broker/API are you planning to use (IBKR, Alpaca, OANDA, etc.) so I can suggest the best execution architecture from the start?

@Hrolgar

Two trading modes reading the same live feed means the data layer needs to be broker-agnostic from day one. I'd build it as a websocket consumer that normalizes ticks into a standard schema, so swapping brokers later doesn't cascade into strategy rewrites. Day and swing logic live as plugin classes implementing a common interface, both consuming the same normalized tick stream. Independently testable, and adding a third strategy later is a new class rather than an engine fork. Backtrader handles the backtest loop against historical OHLC with the same strategy classes running in replay mode, so what you test is what goes live. Risk sits as a decorator around order submission: position sizing, SL/TP targets, and a drawdown kill switch that halts order flow when the daily loss threshold is breached. Unit tests go heaviest here because a silent bug is a margin call, not a failed build. Parameters live in a YAML config so you tune thresholds and lot sizes without touching code. API keys via env vars only, never committed. M1: Data layer (websocket feed, tick normalization, persistence), INR 17000, 4d. M2: Strategy layer (day + swing engines, plugin interface), INR 17000, 4d. M3: Risk layer (position sizing, SL/TP, drawdown kill switch), INR 17000, 3d. M4: Backtester (Backtrader integration, historical OHLC replay), INR 17000, 4d. M5: Config layer, integration, unit tests, QA, INR 17000, 3d. Which broker API are you targeting for the live feed?

@souvikbasu

Your dual day/swing-trading system with a shared risk engine is the right architecture — most implementations treat them as silos and end up with strategy drift between backtest and live behavior. I've built exactly this kind of system. My approach: • Data layer: WebSocket streaming via broker API with in-memory OHLCV ring buffers for low-latency decision making • Strategy engine: configurable JSON rule sets separating intraday signals from multi-day swing filters • Risk layer: ATR-based position sizing, trailing stop-loss, take-profit targets, max daily drawdown circuit breaker • Backtesting: backtrader with walk-forward validation so results aren't curve-fitted to historical data Two quick questions to scope accurately: Which broker API are you targeting — Zerodha Kite Connect, Alpaca, or IBKR? And do you have initial strategy logic defined (e.g. EMA crossover, RSI divergence) or should I include starter strategies? Can deliver a running backtest + paper-trade demo within 7 days of kickoff.

@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