Custom Stock Algo Trading Bot

@AbhyudayaCodes

Hi, Warm Greetings ! We are a team of expert algo trading systems developers, having vast experience in algo trading field, mainly using Python. We have built the following types of algos: > NSE/MCX/NYSE/Nasdaq Markets > Algos trading F&O, stocks, cryptos & commodities > Indicator/price movement based algos > Multi client systems > Systems with Spreads, Straddles, Iron Flys, Heikin Ashi, Renko, etc. > Algos having extremely complicated logic and signal generation procedures > Have built custom indicators and timeframes > Brokers like IBKR/Oanda/Alpaca/Tastytrade/TOS, Binance/, Zerodha/Fyers/Upstox/Finvasia/XTS, and many more ... > US Backtesters and Stock Screeners The features we provide: > Multiple Instance dashboard > Orderbook and logs for tracking > User friendly UI to manage everything > Emergency Safety buttons > Detailed statistics of the algo as per logic > Paper trading as well as Live trading > Editable input boxes for modifiable parameters Zero technical knowhow will be required for operating the algos developed by us. We will handle the complete process from development and testing till deployment. We focus on delivering quality to our clients, keeping in mind safety, security, aesthetics, scalability. Considering our skill set and experience, we think we will be a perfect fit for your project. Looking forward to working with you ... The broker's API and time to reach paper milestone will depend on the exact strategy logic. Message me to discuss further

@vishal1145

Your biggest risk is slippage and latency eating into returns before you even validate the strategy. If your execution layer adds 50ms of delay per trade, a momentum strategy that looks profitable in back-tests will bleed money in production because you're chasing stale prices. Before I recommend an architecture, I need clarity on two things. First, what's your average holding period - are we talking intraday scalping (sub-minute exits) or swing trades held for days? That determines whether we need sub-millisecond execution or if 200ms latency is acceptable. Second, what's your capital allocation per position - $5K or $50K? This dictates whether we optimize for commission costs or build redundancy into the order-routing layer. Here's the architectural approach: - INTERACTIVE BROKERS API: Use their native C++ gateway for sub-100ms order execution, then wrap it with a Python control layer for strategy logic. IB supports NYSE/NASDAQ direct routing and provides tick-level historical data for realistic back-tests. - BACKTESTING ENGINE: Build a vectorized event-driven simulator in Python using Pandas and NumPy. I'll model bid-ask spreads, commission structures, and order-fill probabilities so your Sharpe ratio doesn't collapse when you go live. You'll get HTML reports showing drawdown curves, win rates, and slippage impact. - CONFIGURATION PANEL: Create a JSON-based parameter file controlling stop-loss percentages, max position size, and strategy toggles. No code changes needed - just edit the config and restart the process. I'll add schema validation so invalid inputs can't crash the system. - C++ EXECUTION CORE: Handle order placement, position tracking, and risk checks in a multithreaded C++ module. This runs independently from the strategy layer so a Python crash won't leave orphaned positions. - DEPLOYMENT STACK: Package everything in Docker with systemd auto-restart. You'll deploy to AWS EC2 in the us-east-1 region (closest to NYSE servers) with CloudWatch monitoring for latency spikes. I've built three similar systems for prop traders - one processed 12K orders per day with 99.7% fill accuracy. The typical failure mode isn't the code; it's untested assumptions about market microstructure. Let's schedule a 20-minute call to walk through your risk tolerance and capital constraints before I draft the technical spec. I'll need two weeks to reach a paper-trading milestone with Alpaca's sandbox API, then another week to migrate to IB once the strategy proves stable.

@Shrutibimpl

You want an automated algorithmic trading system with a config panel, historical backtesting reports, and live order execution connected directly to leading US exchanges. This algorithmic system eliminates emotional trading by executing structured strategies with high discipline. By configuring your own risk rules, position sizes, and trade triggers through a clean interface, you maintain complete operational control without writing a line of code. You get full peace of mind by running historical backtests first, verifying the performance of your strategy in past market conditions before exposing any live capital. We will build this platform in Python utilizing the Interactive Brokers API or Alpaca for zero fee stock execution. The backend will integrate WebSockets for low latency feed ingestion and use pandas and numpy to process market data and calculate trade signals. The architecture will separate the execution engine from the strategy parameters, saving runtime settings in a secure PostgreSQL database accessed through a lightweight web interface.

@Jennycreation

With over 15 years of experience in the world of algorithmic trading and software development, my team and I have a wealth of expertise to bring to the table for your project. We specialize in creating custom, high-performance stock trading software tailored to your specific needs. Our skills in Algorithm design, API integration, Python, and Software Architecture means we can fulfill all your specifications with precision and finesse. In terms of structuring the back-testing process, we will create a comprehensive system that enables you to validate and fine-tune your trading strategies effectively using robust historical data. We understand the importance of transparency and reproducibility in this domain; our aim is to equip you with a solution that not only executes trades but also provides detailed back-test reports for performance evaluation. To ensure flawless execution and minimal latency, we propose utilizing an optimum brokerAPI tailored specifically to your requirements. Building on languages such as Python or C++, we will provide clean, well-documented code with easy deployment guidelines for VPS or cloud platforms, ensuring a seamless integration into your trading infrastructure.

@prashanpatel557

Hello, I can build a fully automated stock trading bot with broker API integration, real-time monitoring, configurable risk controls, and a robust backtesting framework for U.S. equities markets (NYSE/NASDAQ). I have experience developing Python-based algorithmic trading systems, broker integrations, live dashboards, and low-latency execution engines. Recommended Architecture: • Broker API: Interactive Brokers API or Alpaca API • Live market data via WebSocket streams • Python execution engine with modular strategy support • Web dashboard for positions, P&L, analytics, and configuration • Paper/live trading toggle with detailed logging Backtesting Approach: • Historical OHLC and tick-data simulation • Walk-forward testing and slippage modeling • Performance metrics: Sharpe, drawdown, win rate, expectancy Deliverables: • Fully documented source code • VPS/cloud deployment setup • Backtest reports and sample strategies • Risk management and position sizing controls Timeline: • Initial architecture + paper-trading milestone: 7–10 days • Full live-trading system: 2–3 weeks depending on feature scope Budget: ₹25,000 – ₹75,000 depending on dashboard and strategy complexity. I can also help design momentum or mean-reversion strategies based on your preferred risk profile and trading style.

@letswork92

I understand that you’re looking to automate your stock-market activities with a custom trading algorithm, which can be quite complex given the need for real-time monitoring and position management. With over 12 years of experience in full-stack development, I can create a robust solution that meets your specifications. For this project, I would recommend using the Interactive Brokers API due to its extensive functionality and reliability. The back-testing framework will be structured to leverage historical data, allowing us to assess various strategies like momentum or mean-reversion effectively. The configuration panel will be developed using React.js for a user-friendly interface, while the backend can utilize Node.js for seamless communication with the broker API. We can achieve a paper-trading milestone within 6-8 weeks, ensuring all features are thoroughly tested before going live. Could you clarify if there are specific risk parameters or trading strategies you want to explore initially? This will help tailor the solution better to your needs.

@KrisuTechkul

Hi, we are a team of 20+ AI/ML Engineers based in Delhi - have completed 300+ projects with 100% client satisfaction & long term association. Leveraging on my extensive experience in software development & AI, I am well-positioned to offer a tailor-made solution to automate your stock-market activities. Having built numerous AI-driven solutions that delivered measurable impact, I understand the nuances of this project profoundly. My expertise in using Python, a language which you're open to, can be instrumental in structuring and deploying your algo-trading system. One of my crucial proficiencies lies in comprehensive AI solutions like machine learning and deep learning, which will be vital in creating back-testing models to ensure reliable performance. Furthermore, I have experience with broker APIs such as Interactive Brokers API and Alpaca API, enabling me to select the best-fit for your needs. Apart from technical skills, I take a strong customer-oriented approach. Providing you with a streamlined configuration panel where you can easily tweak risk limits or entry/exit parameters without touching the code aligns perfectly with my approach. In addition, ensuring clean, documented code with clear deployment instructions is something I strictly adhere to.

@prabhathk5

Thanks for sharing the details. I’ve reviewed your requirement and would be glad to discuss it further. I’m Prabhath, an experienced MQL4/MQL5, Pine Script, Python, and C++ developer specializing in automated trading systems and institutional-grade algorithmic solutions. I develop Expert Advisors, indicators, dashboards, data tools, and custom trading utilities for MT4/MT5, TradingView, and standalone platforms. Along with MQL5 systems, I also build fully automated trading software in Python and C++ for Indian stock markets and global exchanges (US, EU, and others). These solutions can be tailored for stocks, indices, futures, forex, and crypto based on project needs. As an active trader, I work with ICT, SMT, market structure, liquidity models, order blocks, FVGs, VWAP, and volume-based logic, ensuring each strategy follows the client’s trading methodology. My expertise includes institutional-grade EA and indicator development, ICT/SMT-based trading systems, Pine Script automation, Python and C++ systems for Indian and global markets, backtesting, paper trading and live trade integration, strategy optimization, and low-latency execution. I also fix, optimize, and enhance existing trading systems to make them stable and production-ready. Where permitted, I can share demos or walkthroughs of previously completed projects while respecting client confidentiality. Thank you for your time and consideration.

@Algokage

Hello, I can deliver your custom stock algo trading bot efficiently by connecting via Alpaca API for U.S. exchanges with low-latency data, including a config panel for risk and position tweaks, and reproducible back-testing with Backtrader for performance metrics. With 5+ years in algo trading, I aim for a paper-trading milestone in 2-3 weeks. Please message me to see samples or discuss details. Thanks, Adegoke. M

@loxtic

Hello, how are you doing? I’ve built automated trading systems that connect to broker APIs for order placement, risk controls, and real-time monitoring, and I’ve used both Python and C++ for clean, well-documented code. I would use a broker API with low-latency data feeds (for example Alpaca or Interactive Brokers) and structure back-testing with a separate simulator that mirrors live execution, including walk-forward validation and metric dashboards. The config panel would expose risk limits, position sizing, and entry/exit rules without touching the source. I can deliver a paper-trading milestone in 2–3 weeks with reproducible back-test reports and a clear deployment guide. Let me know further if interested

@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

@Visharaa2024

We are experienced algorithmic trading developers with strong expertise in building fully automated stock trading systems for U.S. markets. For this project, we would recommend using Interactive Brokers API or Alpaca API due to their reliability, low-latency execution, strong documentation, and support for NYSE/NASDAQ trading. The system will include automated order execution, real-time monitoring, position management, configurable risk controls, and a user-friendly dashboard where trading parameters, risk limits, and entry/exit settings can be adjusted without modifying the source code. We would structure the back-testing engine using historical market data with detailed performance analytics including win rate, drawdown, Sharpe ratio, and trade logs to ensure strategy transparency and reproducibility before live deployment. The platform can be developed in Python using FastAPI, WebSockets, PostgreSQL, and cloud/VPS deployment for scalability and stability. Clean, well-documented code with deployment instructions and monitoring tools will be provided. We can deliver the initial architecture and paper-trading milestone within 2–3 weeks, including broker integration, strategy framework, back-testing module, and simulated live execution environment. After validation, we can further optimize execution speed, strategy logic, and portfolio management features for production-ready live trading.

@techmadigital

Hi, We have already built trading systems with Zerodha Kite, Upstox, Angel One, Fyers, and TradingView alert integrations including live execution, risk management, and monitoring dashboards. Come to chat, I’ll show you what we have built so far and we can discuss your strategy, broker setup, and execution flow in detail.

@chigomezgob

I’ve reviewed your requirements for the automated stock trading system. Building a low-latency execution engine with a transparent config panel is very doable, but because it handles real capital, the priority has to be safety, error handling, and robust risk controls. ​I will build this in Python, leveraging the Alpaca SDK (or your preferred broker API) for reliable streaming, and Streamlit for the configuration UI. This keeps the code fast, clean, and modular. ​Here is the 5-day delivery plan: • Phase 1: Core Engine – Setting up live WebSocket data feeds, strategy logic, and strict error handling so the bot fails gracefully if a connection blinks. • Phase 2: Risk Management UI – A clean dashboard to adjust position sizes, stop-loss %, and daily drawdown limits on the fly without touching code. • Phase 3: Backtesting & Reports – Running the strategy against historical data to generate clean performance reports (Sharpe ratio, max drawdowns). ​Safety Note: The system will be delivered pre-configured for the broker's Paper Trading (Sandbox) environment. You can safely test execution and UI controls in real-time market hours without risking real money. Moving to live trading is as simple as swapping your API keys in a .env file. ​I can have the sandbox core ready in 48 hours. Let's connect to discuss your entry/exit rules! ​Best regards, Tumelo

@nidhuzalavadiya

Hi there, I will build you a clean, automated trading bot using an event-driven Python framework. It will integrate with a low-latency U.S. broker API (like Alpaca or IBKR) for real-time order placement and position management, paired with a secure config file to adjust risk limits and entry/exit parameters without touching the source code. I will structure the backtesting using historical data to provide clear performance metrics before you deploy. The final code will be fully modular, Dockerized, and ready for your cloud VPS. Let's connect to get started. Best Regards, Nidhi Zalavadiya

@sjohn117

Hi there, I’m Sean, an AI & Full-Stack Developer with over 10 years of experience in building robust algorithmic trading systems. I understand you want to automate your stock-market activities with a reliable and flexible solution, which is essential for effective trading. I have previously developed a similar trading bot that leveraged real-time data from leading U.S. exchanges and included a customizable configuration panel for user-defined risk management. For this project, I suggest using the Alpaca API for a well-rounded trading experience, as it offers robust back-testing features and low-latency connectivity. I will structure the back-testing using a combination of historical data analysis and performance metrics to ensure that your trading strategy is thoroughly evaluated before live deployment. My coding practices ensure clean, documented code that includes deployment instructions tailored for cloud setups. I can deliver the paper-trading milestone within one week. What specific metrics or KPIs are you most interested in tracking for the performance of the trading bot? Thanks, Sean

@techotd

Hello, Your project aligns very well with my experience in automated trading systems, broker API integrations, and quantitative strategy development. I can help you build a production-ready algorithmic trading platform with both robust back-testing and fully automated live execution. My recommended setup would be: • Python-based architecture for flexibility and rapid strategy iteration • Interactive Brokers API (preferred for reliability and U.S. market access) or Alpaca for simpler cloud deployment • Modular system design separating: – market data – strategy engine – execution layer – risk management – reporting/dashboard Key features I would implement: • Fully automated order placement and position management • Real-time monitoring and logging • Configurable risk controls: – position sizing – stop-loss/take-profit – exposure limits – max daily loss • Back-testing engine with: – historical market replay – slippage simulation – commission modeling – detailed performance reports • Simple admin/configuration panel so parameters can be adjusted without editing code • VPS/cloud deployment documentation and automation scripts For strategy development, I’d recommend starting with: • Momentum + trend confirmation models I also emphasize: • clean, maintainable code • reproducible research workflows • strong logging/error recovery • scalable architecture for future multi-strategy expansion Best regards, Arun

@prachia77

Hi, I have carefully read your requirement and I am confident that I can help you. Please initiate a chat so that we can discuss more about feature list and time line. I have 6+ years of experience in Python-based automation, trading systems, API integrations, and real-time monitoring platforms. ✔ Recommended Setup: • Python + FastAPI backend • Interactive Brokers or Alpaca API for U.S. stock trading • PostgreSQL for trade/history storage • Web dashboard for risk settings & strategy controls ✔ Features I can deliver: • Fully automated order execution • Real-time monitoring & position management • Configurable risk management panel • Backtesting engine with detailed reports • VPS/cloud deployment with documentation ✔ Backtesting Approach: Historical market data testing with metrics like win rate, drawdown, Sharpe ratio, and risk/reward analysis before live deployment. ✔ Timeline: • Core architecture & broker integration → 4–5 days • Strategy + backtesting module → 4 days • Dashboard & paper trading setup → 3 days Paper-trading milestone can be ready within 10–12 days. Available to start immediately. Regards Prachi

@sabatali

Hi, I carefully reviewed your requirements and this is exactly the kind of automation/system-building project I enjoy working on. I’ve worked on AI-driven automation, API integrations, trading-related workflows, and backend systems where reliability, monitoring, and scalability are critical. I understand you need more than just a trading bot you need a configurable, production-ready trading infrastructure with solid backtesting before going live. For the broker/API layer, I’d recommend Interactive Brokers or Alpaca depending on your preferred market coverage and execution style. I’d structure the system with separate modules for strategy logic, risk management, live execution, and historical backtesting using Python with PostgreSQL for trade/state tracking. The dashboard would allow you to adjust risk, sizing, and strategy parameters without touching code. I can also build paper-trading validation with reproducible reports before live deployment. I’d be ready to reach a paper-trading milestone within 1–2 weeks depending on final strategy scope. Regards, Sabat