Automated Python Stock Screener

@ashinnd84

Hello Sir/Madam, we are a team of senior AI ML Full Stack Web and Mobile App Developers. Please, send me a message to discuss the work and finish in no time. Thanks Ashish Kumar.

@durgesh8527

As an experienced full-stack developer with a focus on automation and data management, I believe I'm the perfect fit for your project. Having worked extensively with Python, I have developed an innate understanding of your project's technological requirements. My proficiency in Python, accompanied by my grasp of advanced data manipulation and API integration (GraphQL and REST), will ensure a smooth implementation of public APIs for fetching price and fundamental data, along with locally storing raw and derived figures. My expertise extends to trading and FinTech, as well. I am well-versed in Pine Script, MetaTrader 4/5 and their MQL4/MQL5 scripts, which can be leveraged for pattern recognition such as double top, head-and-shoulders, triangle- three classic patterns specified in your acceptance criteria. Above all, I take pride in solving intricate challenges efficiently. Bringing your vision of an integrated Python stock screener to reality would be nothing short of rewarding to me.

@sarimshoaib1425

100% doable. I build backend automation in Python regularly, and this is really a data pipeline problem, pulling data, computing indicators, ranking results, so it maps well onto the kind of scripted automation I do for clients. I'd pull price and fundamental data via yfinance or Alpha Vantage, store raw and derived values in SQLite for easy querying later, then run the indicator logic (MA, RSI, MACD, ATR, VWAP, Bollinger Bands, Stochastic, Super Trend, and the pattern checks for double top, head-and-shoulders, and triangle) on a schedule or on demand. Output would be a ranked ticker list plus a simple command-line or lightweight web dashboard, with clean modular code so you can add new indicators later without touching the core logic. Well-scoped script, so I can have this built, tested, and running end to end within the week. Let's do it.

@NEHABHAT92

With over a decade of python development experience, I am your go-to professional for creating the automated Python stock screener you need. In my extensive career, I have worked on diverse projects not only limited to web development but also mobile app development which gives me a broader perspective to better understand and address your needs. My deep understanding of E-commerce and CMS based websites also enable me to create efficient, accessible and user-friendly systems that can best serve your technical and fundamental requirements. Your project requires a well-structured Python application that can fetch and store data, run complex calculations and provide results in real time while staying flexible enough to incorporate new indicators. This is where my expertise with APIs such as yfinance and Alpha Vantage becomes invaluable. Furthermore, as someone who has served some of the top finance clients, I am well-versed in equity analysis using technical indicators like MA, RSI, MACD as well as leveraging fundamental data like earnings, growth rates etc. Lastly, my commitment to excellent quality work ensures perhaps one of the most important aspects of this project - robust documentation. Something that will not only ease the current task but also help extend the codebase later into trading or automated alerts. My unique ability to combine technical expertise with meticulous documentation makes me an ideal fit for your Automated Python Stock Screener project.

@larsenm2020

Hi i have already worked on many webscrapping projects have done webscraping with requests+bs4 on the ones that dont have js protection. Along with it i have done webscraping on websites that have Js protection with selenium thank you

@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.

@azwa04

Your screener needs a modular pipeline that combines market data collection, technical analysis, and fundamental ranking into a single, extensible application rather than a collection of scripts. I'll build a Python-based system that fetches data from public APIs, calculates all requested indicators, detects chart patterns, stores results locally, and outputs ranked opportunities through a configurable rule engine, making it easy to add new indicators or alerts later. Everything will be documented, well-structured, and reproducible with sample configurations and example runs. I can start immediately and deliver the first working version within 2 days.

@borse1012

I have already developed similar scanner for Bollinger Bands and Smart Money Concept. I can showcase sample demo.

@jaroslavcaprata

Hello, You need a structured Python stock screener that combines market data processing, technical analysis, and fundamental ranking into an extensible application rather than a one-off script. I would build this with a modular architecture separating data collection, indicator calculations, pattern detection, screening rules, storage, and reporting. The system can integrate market data sources such as yfinance or another suitable API, store raw and processed data locally, and run scheduled scans or manual executions. For the technical layer, I would implement the required indicators (MA, RSI, MACD, ATR, VWAP, Bollinger Bands, Super Trend, etc.) with validated calculation logic and add pattern recognition modules for formations like double top, head-and-shoulders, and triangles. The fundamental module would handle valuation metrics and configurable thresholds so rules can be adjusted without modifying the code. I would also include clear logging, configuration files, and documentation so the screener can later be extended with alerts or automation. One point I would like to clarify: which market(s) and data source access do you plan to use, since real-time data availability and API limitations can affect the architecture? Regards, Jaroslav Caprata

@himanshus642

As a seasoned Full Stack Developer with a strong command over Python, I believe I bring the perfect blend of skill and experience to your project. Throughout my 5+ years in the industry, I have honed my craft in building automation-driven applications which aligns well with your requirement of a fully-fledged Python stock scanning solution. My specialization in FinTech systems and Payment integration, coupled with my expertise in API Development will be invaluable in pulling real-time stock values and financial data using readily available public APIs and storing them securely. I am an ideal candidate especially because of my focus on delivering clean architecture whilst ensuring performance is not compromised. My solutions such as MongoDB can instantaneously grant access to significant pieces of information when queried from larger databases helping speed up the backtesting process. Overall, I promise transparent communication throughout the process and a highly structured solution customized to your unique requirements. Consider us together for an impeccable product!

@nexvative

Hi, I can build this as a modular Python equity screener combining real-time technical analysis, fundamental filtering, pattern recognition, configurable screening rules, and a complete backtesting module. I have experience developing Python-based financial and ML systems, including trading strategies, technical indicators, market-data pipelines, and automated screening logic. I can structure the application so you can modify thresholds and strategy parameters through a configuration file without touching the core code. The solution will include: • Market/fundamental data integration via yfinance, Alpha Vantage, or the most reliable API for your requirements • MA, RSI, MACD, ATR, ADR, VWAP, Volume, Bollinger Bands, Stochastic, SuperTrend and swing/breakout calculations • 52-week-high distance and range-breakout detection • Double Top, Head & Shoulders, and Triangle pattern recognition • Fundamental metrics including earnings, revenue growth, P/E, dividends and configurable valuation filters • Local SQLite/CSV storage for raw and derived data • Ranked screener output based on your technical + fundamental rules • Configurable YAML/JSON settings for indicators, thresholds and watchlists • CLI or lightweight dashboard for screening and reviewing results • Structured logging and error handling for reliable scheduled execution

@bizvivo

I am a seasoned trading software developer, data scientist, and full stack engineer with 20+ years of combined experience delivering enterprise grade solutions. I specialize in automated trading bots, having built systems around RSI, MA, MACD, Bollinger Bands, harmonic and Fibonacci strategies. Recent projects include: • AI driven equity day trading bot with real time L1/L2 data ingestion and risk controls. • Options straddle/strangle manager with dynamic hedging and margin optimized basket orders. • Multi asset scalping bot tuned for ultra low latency execution. • Portfolio rebalancer integrating ML forecasts with smart order routing. • Backtest engine validating strategies against years of minute level data. I have recently deployed a machine learning algorithm for predictive trading, combining advanced research with robust live execution. Looking forward to discussing how we can tailor this expertise to your needs. Best Regards, Radhika

@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

@JoseRCarvas

The tricky part isn't the indicators — MA/RSI/MACD/ATR/VWAP/Bollinger/Stochastic/SuperTrend are all deterministic once the data pipeline is clean. It's two things: reliable near-real-time data without a paid feed, and pattern logic (swing-high breaks, range breakouts, distance-from-52w-high) that doesn't fire false signals on noisy ticks. How I'd build it as one Python app: - Data: yfinance for OHLCV + fundamentals, Alpha Vantage as fallback, cached to SQLite so you don't hit rate limits. - Indicator engine: pandas / pandas-ta computing every indicator you listed, plus swing-high / breakout / 52w-distance / SuperTrend logic. - Fundamentals: P/E, earnings, revenue growth, dividends per ticker, ranked against the thresholds you supply in an editable config. - Output: a ranked screen (CSV + simple table) you re-run on demand or on a schedule. For this budget I'd deliver the full technical + fundamental screener on polled data (e.g. 1-min refresh). True tick-level streaming pattern recognition I'd flag as a clear phase 2, so the price matches the scope. I run and test everything before handoff. What's your ticker universe — a fixed watchlist, an index, or a whole exchange? That decides the data strategy. 5.0 stars on my recent work. — Ricardo

@GobindSawarn

I will develop a modular Python application that automatically collects market price and fundamental data, analyzes securities using configurable technical and fundamental factors, ranks stocks based on customizable scoring rules, and validates those rules through historical backtesting before live deployment. The application will include automated data ingestion from reliable market data sources, data storage using CSV and SQLite, and a clean modular architecture to support future enhancements such as alerts, automated trading, or additional indicators. Technical analysis modules will calculate Moving Averages (MA), RSI, MACD, ATR, ADR, VWAP, Bollinger Bands, Stochastic Oscillator, SuperTrend, volume analysis, breakout and swing-high detection, and distance from the 52-week high. It will also identify chart patterns including Double Top, Head & Shoulders, and Triangle formations. The system will export ranked screening results in CSV format, maintain comprehensive logs for debugging and monitoring, and include clear documentation, configuration files, and well-structured modules to simplify maintenance and future feature development.

@shayankhan333

Chart-pattern recognition is where most stock screeners fall apart, especially head-and-shoulders detection on noisy intraday data. I've built similar screening tools and the key is getting the pivot-point detection right before layering pattern logic on top. I'll structure this as modular Python with a data layer (yfinance for price, fundamentals pulled via their quarterly financials endpoint), a signals engine covering all your indicators plus the three pattern types, and a config-driven rule ranker that outputs results to both CSV and an optional Streamlit dashboard. SQLite for local storage keeps things lightweight but query-friendly when you extend into alerts later. One thing worth flagging: yfinance's real-time streaming is unreliable for true tick-level pattern recognition. Best regards, Shayan

@bojans9

Hi there I can build the automated Python stock screener you need You mentioned API development and financial analysis so I will connect to the chosen market data APIs, pull historic and realtime quotes, run the statistical filters you specify and generate the charts you want My approach is to set up a modular data pipeline, write reusable analysis functions and use matplotlib or plotly for the visual output This project can succeed because I have delivered a similar Android arcade game and a Java Minecraft mod on tight budgets and I am currently handling complex firmware and Unreal Engine projects I can deliver a solid working screener within the budget and timeline you set Will you need multi‑broker support or a single data source Do you prefer static reports or an interactive web dashboard

@thiagofoz87

Your project requires more than a simple indicator script — it needs a modular screening engine capable of combining market data ingestion, technical analysis, scoring logic, and historical validation in a maintainable way. I would structure the application into independent modules for data collection, indicator calculation, pattern detection, ranking/scoring, persistence, and backtesting. This approach makes it easier to evolve the system later with alerts, broker integrations, or automated trading features. For the technical layer, I can implement the requested indicators (MA, RSI, MACD, ATR, ADR, VWAP, Bollinger Bands, Stochastic, SuperTrend), breakout/swing-high logic, 52-week distance calculations, and configurable weighted ranking rules. Pattern recognition for Double Top, Head & Shoulders, and Triangle formations can be added using rule-based detection over OHLC data. The backtesting module would evaluate historical performance with metrics such as cumulative returns, win rate, drawdown, and Sharpe ratio, while keeping strategy parameters configurable for experimentation. Delivery would include: - Modular Python codebase - CSV and SQLite persistence - Exportable screener results - Structured logging - Clean configuration system - Documentation for setup and execution I can deliver a first stable version focused on reliability, readability, and future extensibility rather than a monolithic script.

@muhammadaqib53

Hello, I can build a modular Python application that screens equities using both technical and fundamental analysis. Approach: Fetch price & fundamental data via yfinance / Alpha Vantage APIs. Compute all listed indicators (MA, RSI, MACD, ATR, VWAP, Bollinger Bands, Stochastic, Super Trend, etc.). Implement pattern recognition (double top, head‑and‑shoulders, triangle). Pull valuation metrics (P/E, earnings, revenue growth, dividends) and rank tickers against your thresholds. Store results in SQLite/CSV, with clear logs. Output ranked tickers plus a summary dashboard (CLI or minimal web UI). Deliverables: Clean, commented Python modules. README + sample config file for thresholds. Example run showing results for a watch‑list. I estimate 5–7 days for a complete, tested end‑to‑end solution.