Stock Day-Trading Algo Development

@rohitpatidar001

Hello, I have 11 years of experience developing enterprise-grade backend applications, API integrations, and high-performance systems using Java, along with experience working with Python for data processing and automation. Your project is interesting because it combines multiple domains—market data ingestion, trading strategy execution, sentiment analysis, risk management, and analytics—into a scalable algorithmic trading platform. My proposed approach would include: * Real-time market data ingestion from broker or market data APIs. * Modular strategy engine that combines configurable technical indicators (RSI, Moving Averages, VWAP, MACD, etc.). * Integration of news sentiment APIs to enrich trading decisions. * Configurable weighting system so indicator values can be adjusted without changing source code. * Risk management module including position sizing, stop-loss, take-profit, and trade validation. * Broker API integration for automated order execution. * Monitoring dashboard/CLI for live positions, parameter adjustments, and emergency pause functionality. * Comprehensive trade logging and post-trade performance analytics. * Clean architecture with well-documented source code and installation instructions. My strengths include: * REST API development * Java & Python backend development * Microservices architecture * Database design * Performance optimization * Clean, maintainable, and production-ready code

@mubeenm5026

With over a decade of experience, specializing in AI and SaaS platforms, my company Web Crest is well-equipped to tackle your high-frequency trading project. Our expertise in the fields of API Development, Java and Python - which is a widely supported language and your preferred choice - ensures not only a proficient development process but also an efficient one. We value flexibility in our approach, so I can assure you that the code structure will be such that you can easily tweak indicator weights without rewriting functions. Backtesting is a critical component of developing any trading strategy, and this is a practice we strictly adhere to at Web Crest. An extensive walk-forward back-test covering at least one year of 1-minute data would be conducted and the results provided to solidify the algorithm's performance prior to deployment. In addition, as a client-centric organization, we prioritize open communication channels and full transparency throughout project duration - from installation instructions to successful deployment walkthroughs. Lastly, with Web Crest, technical support doesn't end after project delivery. We commit ourselves to providing long-term assistance for all our developed solutions.

@gowthamsarathy

Hello, I'll develop a modular algorithmic trading platform that integrates real-time market data, technical indicators, fundamental events, and news sentiment into a unified signal engine. The architecture will separate data ingestion, strategy evaluation, risk management, execution, monitoring, and analytics, making it straightforward to adjust indicator weights, replace data providers, or extend the strategy without modifying core components. The system will support configurable indicator weighting, walk-forward backtesting on 1-minute historical data, automated order execution through your preferred broker API, and a lightweight dashboard for monitoring positions, adjusting parameters, and pausing trading. Since I actively trade myself, I understand the importance of execution speed, position synchronization, and reliability in live trading environments, which helps shape the overall system design. The final delivery will include fully documented source code, configuration files for strategy tuning, comprehensive testing of execution and risk controls, a backtest report with key performance metrics, deployment guidance, and a hand-off session covering the complete workflow. Relevant Work: https://www.freelancer.in/portfolio-items/11493278-algorithmic-trading-bot-for-mt5 https://www.freelancer.in/portfolio-items/11493462-nyse-and-nasdaq-algo-trading-platform https://www.freelancer.in/portfolio-items/11493491-mt4-mt5-trade-copier Kind regards, Gowtham

@mahads31

I understand the great importance of having a robust, real-time stock trading algorithm capable of combining various data streams for optimal decision-making —this is a crucial aspect in the financial market. Employing my advanced skills in API Development and Python, I am well-positioned to develop such an algorithm for you. I have a firm grasp on utilizing technical indicators, fundamental metrics, and real-time news sentiment data as per your requirement. Moreover, my experience working in the finance industry with international clients ensures that I understand the sensitivity and complex nature of your project. My primary goal is to create an efficient, high performing system that suits your long-term maintenance needs. Using well-documented and adaptable code structures, I'll provide you with ample flexibility to tweak indicator weights without rewriting entire functions. Finally, I'll employ thorough backtesting methods to ensure both performance optimization and migration readiness. This will involve a step-by-step walk-through session to equip you with comprehensive knowledge during the deployment process. I assure you this project will be approached with precision and meticulousness resulting in powerful Python code, reliable deliverables, and continuous support post-completion. Let's build something impactful together!

@abhishek060

Hi, I've reviewed your requirements, and this project is about building a robust trading system—not just a strategy. The architecture needs to support real-time data processing, reliable execution, and flexible strategy tuning without changing the core code. I can develop the application in **Python**, with modular components for market data ingestion, technical indicators, news sentiment, signal generation, risk management, broker API integration, and post-trade analytics. Indicator weights and risk parameters will be fully configurable through a config file or dashboard, making optimization straightforward. The solution will include automated backtesting (1-minute historical data), detailed performance reports, trade logs, and a lightweight dashboard to monitor positions, pause trading, and adjust parameters in real time. Let's discuss your preferred broker API (Interactive Brokers, Alpaca, Zerodha, etc.) and data providers—I can start immediately. Best regards, Abhishek

@ashinnd84

Python is the right call. At intraday timeframes you don't need C++ latency, and you'd lose weeks of iteration speed for nothing. I'd build it as three processes talking over a queue — ingestion, signal engine, execution — so a broker disconnect or a news API rate limit doesn't take the whole bot down. Indicator weights, thresholds, sizing rules and stops all sit in a YAML config that reloads live, so changing a weight never means touching a function. Execution stays broker-agnostic behind one interface, so switching later is a single adapter file. Which broker are you on? If it's Zerodha, Fyers, Dhan or Angel One I already have the API patterns; IBKR is fine too. The backtest is event-driven rather than vectorised, run walk-forward over a year of 1-minute bars, with realistic slippage and brokerage charged in. I'd rather hand you an honest curve than a flattering one. It ships as a notebook so you can rerun it yourself. Trade logs go to Postgres — P&L, slippage, hit rate, and per-signal attribution so you can see which of the three streams is actually earning its weight. One question: does the news sentiment need a licensed feed, or are scraped headlines acceptable? Thanks Ashish Kumar.

@prabhathk5

Hi Sir Hope you doing well! I’ve reviewed your requirement and would like to discuss it further. I’m Prabhath — an MQL4/MQL5, Pine Script, Python, and C++ developer with strong experience in building trading systems, advanced studies, and institutional-grade algorithmic solutions. I develop Expert Advisors, indicators, dashboards, data tools, and custom trading utilities for MT4/MT5 and TradingView. As an active trader, I work with concepts like ICT, SMT, market structure, liquidity models, order blocks, FVGs, VWAP, and volume-based logic. I build EAs and indicators that follow precise rules and match the user’s trading methodology perfectly. My expertise includes: Institutional-grade EA and indicator development ICT/SMT-based trading systems Pine Script indicators and automated strategies Python tools for data analysis, signals, and external integrations C++ modules for high-performance components Backtesting, forward testing, and full strategy optimization Strategy development, refinement, and consulting Once we discuss your project and you select me as the developer, I prefer a short call to confirm all details before starting. This ensures perfect clarity and avoids misunderstandings. I fix and optimize existing trading code by removing errors, correcting logic issues, and making your EA/indicator/bot stable and production-ready. Thank you.

@TechizeBuilder

Dear Client, I read "Stock Day-Trading Algo Development" carefully and understand what you're looking to achieve with this Mern Stack Development project. My hands-on experience with Java, Python, API Development aligns directly with what you need. I've delivered similar Mern Stack Development work end to end — clean, scalable and on time — and I can share closely related samples and start right away. A few quick questions to get us started: 1. Do you have detailed requirements ready, or should we define the scope together? 2. What's your target timeline for the first working version? 3. Are there existing designs, code or systems I should build on? Thanks & Regards, Deepak

@pinkeegupta

Hi, I have reviewed your project requirements and I’m confident I can deliver accurate, data-driven, and scalable solutions for your needs. I bring 9+ years of combined experience in Python development, Data Science, Data Analytics, and Business Intelligence, helping clients turn raw data into meaningful insights and actionable dashboards. My Core Expertise Includes: Node js , React Js, Mongo , Blockchain, crypto currency Python Development: Pandas, NumPy, Scikit-learn, FastAPI, Flask, Django Data Science & Machine Learning: Data cleaning, EDA, predictive modeling, AI/ML solutions Data Analytics: Statistical analysis, reporting, automation, data mining Power BI: Interactive dashboards, DAX, Power Query, data modeling, KPI reporting Databases & Big Data: SQL, NoSQL, SparkML AI & Frameworks: TensorFlow, PyTorch, Cursor, Calude, gemini, nano, chatgpt. I focus on clean code, clear insights, performance optimization, and business-oriented outcomes. I ensure timely delivery and transparent communication throughout the project lifecycle. Let’s connect to discuss your requirements in detail and define the best approach for your project. Looking forward to working with you. Regards, Anju Logical Soft Tech Pvt Ltd, Indore(M.P)

@Algokage

Hello, I hope you're doing well. I am an experienced algorithmic trading developer with over 6 years of expertise in building and deploying intraday trading systems. My background includes integrating real-time data streams, such as technical indicators, fundamental metrics, and news sentiment, to create robust trading strategies. I’ve successfully delivered projects that handle tick-level data, implement dynamic risk management, and interface with brokerage APIs, ensuring seamless execution and monitoring. For this project, I’d approach the development by first setting up a Python-based framework using libraries like Pandas, NumPy, and TA-Lib for data processing and signal generation. The system would modularly combine technical, fundamental, and sentiment signals, allowing easy adjustment of weights. Risk management and order routing would be handled through a brokerage API, with a CLI or dashboard for real-time oversight. A walk-forward back-test using 1-minute data would validate performance before deployment. I deliver clean, well-documented code and ensure all deliverables meet your requirements. Let’s discuss this further to align on specifics and explore suitable libraries or APIs for your setup. Thanks, Adegoke M.

@ammarahmed20

I see you need an algorithmic trading application for the stock market that incorporates several essential elements, and I am confident that I can deliver what you need. As a seasoned developer with over six years of experience, I have built AI agents and automation systems that run in complex production environments across various industries including finance. Consequently, I have skillfully navigated data ingestion, signal engines, risk and execution management, monitoring systems and even post-trade analytics. All these align perfectly with your proposed workflow. Moreover, my proficiency in API development and Python put me in a great position to build your application with flexibility in mind. Your stated requirement of allowing indicator weighting adjustments without massive code modifications will be seamlessly crafted into the system. This will not only provide a way to tweak indicator weights but also maintain the necessary clarity to ensure comprehension by any party henceforth.

@pablosilva91

Hi, One question first, because it changes the quote more than anything else: which broker and which market? Alpaca, IBKR and an Indian broker have very different order APIs, and intraday execution is where most of the real work lives. Two things that decide whether this works or quietly loses money: Backtest realism. A walk-forward on a year of 1-minute bars looks great and means nothing if it fills at the close of the signal bar. Real intraday P&L is dominated by slippage and partial fills. I model fills against the next bar's open with spread and slippage assumptions, and report results with and without costs so you can see the gap yourself. News timing. Sentiment is only tradeable if the headline timestamp is wire time, not the time your provider indexed it. Backtests built on indexed timestamps look profitable and do not reproduce live. Worth confirming what your source actually gives you. On structure: two milestones. First the data pipeline, signal engine and backtest report, so you judge the strategy on evidence before paying for live execution. Then risk, order routing and the dashboard. That way you keep an exit point if the backtest does not convince you. Two questions: which brokerage API, and do you already have the 1-minute history and a news feed, or should I price those in? Pablo

@Hrolgar

Tick data plus 1-minute bars plus news sentiment feeding into one weighted signal engine is three very different ingestion problems before a single trade even gets sized, and that's before the broker adapter and risk layer come into it. I'd separate paper and live trading at the architecture level from day one rather than bolt it on later, so the walk-forward backtest and the live system run through the exact same signal path and risk checks. That's the part that actually protects you once real money is involved. The weighting itself I'd make config-driven rather than hardcoded, so tuning session fundamentals against sentiment against price action doesn't need a redeploy every time you want to test an idea. Order of build: broker/data adapters and a paper-trading skeleton first, then the signal engine itself, then risk controls and kill switches wired straight into execution, then the full year of walk-forward backtesting with analytics exports, then the monitoring/override layer plus documentation and handoff. M1: broker/data adapters, paper-trading skeleton, 50000 INR, 5 days M2: signal engine, tick/1-min ingestion plus fundamentals plus sentiment scoring, configurable weights, 55000 INR, 6 days M3: risk layer, kill switches, execution routing in paper mode, 50000 INR, 6 days M4: one-year walk-forward backtest, analytics exports, 55000 INR, 6 days M5: monitoring/override UI or CLI, live cutover, documentation and handoff, 40000 INR, 5 days 250000 INR over 28 days split that way. That figure is off the brief as posted, and which broker API and data vendor you land on will shift it once scope is locked. M1 on its own proves the architecture, paper-trading skeleton and adapters working end to end, before anything downstream gets built on top of it. Worth confirming that piece first.

@PrateekP3003

Hi, Your project requires more than an automated trading bot—it needs a modular quantitative trading framework that fuses technical analysis, fundamentals, and real-time news sentiment into a robust intraday decision engine. I'd build a configurable, production-ready system with strong risk management, broker integration, and validated performance through walk-forward backtesting. Project Key Points: Real-time market data ingestion, technical indicators (MA, RSI, VWAP, ATR), live fundamentals, AI/news sentiment analysis, weighted signal engine, automated order execution, broker API integration, configurable risk management, monitoring dashboard, backtesting, and performance analytics. Execution Plan: I'll develop the platform in Python using a modular architecture where each strategy component (technical, fundamental, sentiment, execution, and risk) operates independently. Historical validation will be performed using a walk-forward framework on 1-minute market data, generating comprehensive metrics including P&L, Sharpe ratio, drawdown, win rate, and slippage. With 7+ years of experience in Python, algorithmic trading systems, financial data processing, AI integration, and quantitative analytics, I specialize in building scalable, research-driven trading platforms with clean, maintainable code. Looking forward to collaborating with you! Best regards, Prateek

@borse1012

I can help build your intraday algorithmic trading application. What interested me about your project is the combination of technical indicators, fundamental data, and AI-driven news sentiment into a configurable trading engine rather than a simple rule-based bot. I've worked on similar algorithmic trading and market data integration projects and would enjoy building this with you. I have 14 yrs of experience building enterprise applications & developing algorithmic trading systems, integrating broker APIs, processing real-time market data, implementing technical indicators, and automating trade execution. I would recommend Java because it provides excellent performance, scalability, and reliability for real-time market data processing and automated trade execution. I would use Spring Boot, TA-Lib (Java bindings), and a modular strategy engine, while integrating market data, news sentiment services, and your preferred broker API through REST or WebSocket APIs. The solution will include a configurable strategy engine, one-year walk-forward backtesting, comprehensive trade analytics, a lightweight monitoring dashboard, and complete deployment documentation. I'd be happy to discuss your preferred broker, market data provider, and news sentiment source to recommend the most suitable architecture.

@alluankit01

Hi, I can build your custom intraday stock trading algo in Python with live data feeds, technical indicators, fundamentals, news sentiment, broker API execution, risk controls, dashboard/CLI monitoring, and backtesting. The best solution is to make the system modular: data ingestion, signal engine, risk management, order execution, monitoring, and reporting as separate layers. Indicator weights, stop-loss, position size, and other parameters will be editable through a config file or simple UI without changing core code. I’m comfortable with Python, pandas, NumPy, TA-Lib/pandas-ta, WebSocket data, broker APIs, sentiment analysis, backtesting, order routing, logging, and trading dashboards. Deliverables include: * Fully commented source code * Live price/fundamental/news ingestion * MA, RSI, VWAP-based signals * News sentiment scoring * Configurable weighted signal engine * Broker API execution * Stop-loss and position sizing * Dashboard or CLI monitor * One-year 1-minute backtest report * P&L, slippage, and hit-rate logs * Installation guide and handover session No profit is guaranteed, but I’ll build a clean, testable, and risk-controlled system. Best regards Ankit

@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

@netswaptech2811

You're building more than a trading bot—you need a modular trading platform that can ingest multiple market data sources, generate explainable signals, execute trades with strict risk controls, and provide robust analytics for continuous strategy refinement. I recommend building this in Python because of its mature ecosystem for quantitative trading, data processing, and broker integrations. The architecture will separate data ingestion, feature engineering, signal generation, risk management, execution, and reporting into independent modules, with all strategy weights and thresholds configurable through a YAML/JSON configuration file or a lightweight web dashboard. This allows you to tune the strategy without modifying the core code. The solution will include: Real-time market, fundamentals, and news sentiment ingestion. Modular signal engine combining technical indicators, fundamentals, and sentiment with configurable weights. Risk management with position sizing, stop-loss, take-profit, daily loss limits, and broker-agnostic execution layer. Live monitoring dashboard to view positions, pause trading, and adjust strategy parameters. Walk-forward backtesting on 1-minute historical data with detailed performance reports covering P&L, drawdown, Sharpe ratio. At NetSwap Technologies, we design trading systems with a strong emphasis on modular architecture, reliability, and maintainability so strategies can evolve without requiring a complete rewrite.

@MKCHAND

Hi first of all see my work then provide your valuable project to me I am a senior trading systems developer with over 12+ years of hands-on experience in designing, developing, and deploying professional-grade automated trading bots and financial tools. I have successfully built and maintained trading systems for major Indian brokers including Zerodha, Fyers, Upstox, ICICI Direct, Kotak Neo, XTS API, Stoxxo, and Shoonya, as well as Forex and international platforms such as MT5 (MetaTrader 5) and Interactive Brokers. My expertise covers: End-to-end algorithmic trading system design High-performance trading bots (equity, options, futures, and forex) Real-time data handling, order execution, and risk management Custom professional-grade GUIs for live monitoring and control Scalable, reliable, and broker-compliant architectures Before any project commitment, I am happy to provide a live demonstration of my previous work, allowing you to evaluate the quality, stability, and performance of my systems. I also offer free lifetime technical support for all delivered solutions, ensuring long-term reliability and continuous assistance. I am confident in my ability to design and deliver a fully customized, robust, and production-ready trading system tailored exactly to your requirements. Let’s connect on chat to discuss your project details in depth.