AI Trading Agent in Python

@sdqtrades

Building an AI Trading Agent requires not just programming skills, but an excellent grasp of financial markets and the ability to synthesize complex rules into simple, actionable signals. With my proven record in AI development-oriented towards trading and cryptocurrencies, I believe I'm your top choice for this project. From developing Pine scripts in TradingView to Bot creation, I have showcased my ability to design sophisticated trading systems that take advantage of market trends and capitalize on lucrative opportunities - an essential skill for your python-based AI agent Regarding the specific question about the brief, one aspect I'm pretty excited about is MODULE 1 – Scholar Agent. The thorough scanning of multiple markets like BTC, ETH, and Nifty 50stocks requires a systematic approach coupled with a deep understanding of data processing. Thankfully, I am skilled in complex web scraping and smart data extraction - two critical components Module 1 will benefit from. Moreover, employing rigorous risk management techniques is vital for effective trading. I not only understand this but adhere to it strictly. For instance, I'll design your risk engine such that our stop loss never widens under any circumstances - a golden rule that ensures minimal account drawdown. Finally, behind every algorithmic trade or investment strategy lies continual improvement through backtesting and thorough analysis of historical performance.

@Algokage

Hello, I can deliver the AI Trading Agent in Python as per your requirements, focusing on a rules-based framework with no guesswork. I’ll prioritize the Scholar Agent module, ensuring it scans and scores assets daily using EMA, ADX, MACD, and RSI indicators, followed by backtesting and paper trading with a robust risk engine. My approach includes using Python 3.10+, yfinance, Binance/Kite APIs, and Claude API for reporting. With 5+ years of experience in trading bots and financial AI, I’ll ensure a structured timeline and milestone-based pricing. My question: How flexible is the tech stack if performance optimizations are needed? Send a message for GitHub samples or to discuss further. Thanks, Adegoke. M

@Jennycreation

With my extensive experience of 17+ years in software development, I am confident that I am the perfect fit to help you build your AI trading agent in Python. My expertise lies in areas such as Python scraping, automation, cryptocurrency, and stock trading. In fact, I have a demonstrated past of developing successful stock trading softwares, which required a robust rules-based framework similar to the one you are looking for. When it comes to financial AI agents, my knowledge and skill set are truly unmatched. Not only do I understand the intricate workings of trading strategies and risk management like stop losses and position sizing but also calculate dynamic factors like ATR for intelligent trade setup evaluations. In addition to this, throughout my career journey, adhering strictly to defined timelines and delivering top-notch quality milestones has been an absolute priority. In summary, choosing me for this project guarantees an efficient completion within your timeline and budget constraints while ensuring exceptional quality work. My comprehensive python knowledge complimented with vast experience in developing algorithms for stock trading sets me apart from my competitors. Looking forward to discussing your project more in detail.

@Rsolitario

Hello, I have carefully reviewed your brief. I understand you are not looking for a "magic" black-box algorithm, but a disciplined, rules-based trend-following system that automates a rigorous 3-Gate scoring logic. My approach focuses on the transparency and mathematical consistency you’ve outlined, ensuring every decision made by the "Scholar Agent" is traceable and verifiable. How I will execute the 3 Modules: Module 1 (The Scholar Agent): I will build a multi-asset scanner (BTC, ETH, Nifty 50) using yfinance and Binance API. I’ll implement the 3-Gate Scoring System (0-9). Gate 1 (Trend): EMA 20/50/200 alignment. Gate 2 (Momentum): ADX > 25, MACD crossover, and RSI > 50. Gate 3 (Trigger): Price breakout + Volume + Supertrend flip. Output: A clean daily PDF/Telegram report via Claude API or specialized formatting. Module 2 (Rigorous Backtesting): Instead of simple loops, I use Vectorized Backtesting (Pandas/NumPy) to simulate 3 years of data in seconds. I will provide a deep-dive report including Sharpe Ratio, Max Drawdown (MDD), and Win Rate, ensuring the "Scholar's" rules actually hold edge over time. Module 3 (Execution & Risk Engine): This is the most critical part. I will implement your strict risk rules: Position Sizing: 1% risk per trade based on equity. ATR-Based Stops: Initial SL at ATR x 2.0. Trailing Stop: Activating at ATR x 1.0 profit to protect capital. Integration: Seamless connection with Zerodha (Kite) and Binance.

@loxtic

Hi, how are you doing? I have considerable experience building trading bots and financial AI agents, including Python-based modules for signal scoring and risk management, plus integrations with Kite and Binance APIs. I can propose a clear timeline with milestone pricing, share a GitHub sample, and discuss a question I have about the gate criteria to ensure the scoring stays strict and explainable. Let me know further if interested.

@adriank06

Hello there, The main challenge with rules-based trading agents is avoiding false signals when multiple gates align but market microstructure is noisy. I'll implement regime detection via ATR volatility filter before scoring, ensuring only high-quality setups enter the pipeline. AI Specialist & Developer with expertise in financial AI agents, Python automation, and API integration. I can deliver the full pipeline within 7 days using LangGraph for modular orchestration and Claude API for natural language reporting. How do you handle data gaps on yfinance for Nifty 50 stocks during holidays?

@Sahyadri96

From Pune 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

@milanrathod945

Scoring trading signals without grounded rules risks false positives, structured agent pipelines with strict confirmation gates (EMA, ADX, MACD) filter noise, as proven in my 12-agent production system delivering BUY/SELL decisions with entry targets. For your scholar agent, I'll implement the 3-gate scoring (trend alignment, momentum strength, entry trigger) with Python, yfinance/Binance API, and structured output matching your risk rules, same architecture I used for a live trading pipeline handling position sizing and trailing stops. Module 2 backtesting will use Backtrader with Sharpe Ratio, Max Drawdown metrics. Ready to start immediately, which watchlist assets need priority testing first?

@Geetam00

As a seasoned developer with a strong background in AI development, specializing in Python, I am confident that I am the perfect fit for your AI Trading Agent project. Having developed various trading bots and financial AI agents over the years, I can guarantee a result-driven outcome — just what you need for this project. My experience with APIs such as yfinance, Binance, and Kite will also come in handy for real-time execution and report generation. You can trust me to deliver on time and within your budget, as I'll put forward an error-free, fully optimized solution. Additionally, my expertise with LangChain/LangGraph and Backtrader/QuantConnect LEAN aligns perfectly with your brief's preference in tech stack. One specific question I have is: Have you considered including NLP-based analysis like sentiment analysis on social media platforms for better market prediction? My experience with statistical analysis and software architecture makes me well-suited to integrating such features into your system. I look forward to working with you on this groundbreaking project!

@georgeg252

Unlock the power of data-driven trading with a Python-based AI agent tailored to your specifications. I understand you're seeking a robust system that meticulously evaluates trading opportunities without the ambiguity of black-box strategies. By implementing a rules-based framework, your agent can analyze market trends across your watchlist — BTC, ETH, and Nifty 50 stocks — ensuring consistent and reliable scoring. I'll leverage n8n for seamless integration of workflows, combined with advanced AI techniques to enhance the scholar agent module's performance. Additionally, utilizing API connections will allow real-time data handling and increased efficiency in decision-making. My extensive experience in Python programming and automation will ensure the project is delivered on time and within budget. Let’s build an intelligent solution that transforms your trading approach. Best regards, George | n8n & AI Automation Expert

@Apitide

Hello, Your brief stands out because it prioritizes deterministic trading logic over black-box AI, which is exactly how robust trading systems should be built. We can develop a modular Python-based agent where every signal is explainable, auditable, and governed by strict risk controls. For Module 1, we'll implement the Scholar Agent using yfinance/Binance/Kite APIs to score assets across the 9-point framework. Signals scoring 7+ will generate plain-English reports including entry zones, ATR-based stops, and position sizing. Module 2 will leverage Backtrader or LEAN to backtest 7+ strategy variants over 3 years, reporting Sharpe Ratio, Max Drawdown, and Win Rate. Module 3 will enable paper trading with Telegram notifications and mandatory one-click human approval before any live execution. We have experience building AI agents, RAG systems, and automation pipelines with Python, LangChain, and API integrations. The architecture will be modular, fully documented, NDA-compliant, and designed for future expansion into live trading. Let’s align over a quick call or Loom video. Best regards, Team Apitide

@abhinav773

Hi, I can build the Python trading-opportunity scoring agent with a transparent rules-based architecture rather than black-box predictions. I would structure the Scholar module around watchlist scanning, repeatable 0-9 scoring, clear data inputs, logging, and reports so every score is explainable. I can also separate modules cleanly so you can extend assets, rules, and outputs later.

@nabeeli18

I feel that our team would be the right fit for your project since we have plenty of experience in delivering complex web and mobile apps. We can develop a Python-based AI trading agent that identifies trending markets and scores trading opportunities based on a rules-based framework tailored to your specific requirements. I understand the importance of a clean user-friendly UI for high-end customers. Let's discuss your project further and walk away with a free consultation to explore how we can meet your needs effectively. Regards, Nabeel Ismail

@ammarahmed20

If you're looking for someone who treats your project like their own business, you're in the right place. Give me one opportunity to prove my expertise, and I'm confident you'll want to work with me again. Drawing from my 6+ years of experience, I can tell you that delivering practical solutions, not just theoretical ones, is what sets my work apart. For your AI Trading Agent project, I have the invaluable proficiency in building AI systems that handle complex financial decision-making using strict rules-based frameworks—something your project insists upon. By leveraging time-tested tools such as Backtrader or QuantConnect LEAN bolstered by my deep knowledge of Python 3.10+, yfinance/Binance API/Kite API; I'll ensure that MODULE 2 (Backtesting) is conducted with utmost precision. Expect insightful reports on not just Sharpe Ratio, Max Drawdown, and Win Rate but actionable feedback for continuous strategic improvement. Additionally, given my passion for automating workflows with efficiency in mind and my extensive background in API development (specifically Zerodha Kite/ Binance/Kite API), I'm an ideal candidate to implement MODULE 3 (Paper Trading). Trust that I will not only run a seamless simulated trading phase but also deliver proficient live execution when the time comes.

@Houssamtah122

Hello, I have experience building Python automation systems, data-processing pipelines, and rule-based analysis tools. Your requirements are clear and well-structured, especially the 3-gate confirmation framework and the strict risk-management rules. Proposed timeline: Phase 1: Market Analysis Agent - Watchlist scanner - EMA / ADX / MACD / RSI validation - ATR-based risk calculations - Daily Telegram reports Estimated: 5-7 days Phase 2: Backtesting Framework - Historical data integration - 7+ strategy evaluations - Sharpe Ratio, Max Drawdown, Win Rate reporting Estimated: 5-7 days Phase 3: Paper Trading System - Signal execution simulation - Trade logging and reporting - Human approval workflow Estimated: 3-5 days Phase 4: Live Trading Integration (Optional) - Binance or Zerodha Kite integration - One-click human confirmation before execution Estimated: 3-4 days Question: For Gate 3, when both a breakout/retest setup and a Supertrend reversal occur simultaneously, should they be treated as separate scoring conditions or as alternative confirmations for the same score? I can deliver complete source code, documentation, milestone-based delivery, and NDA compliance. Looking forward to discussing the project.

@krishc885

I'm pursuing FRM, which aligns directly with this brief's core requirement — a strict, non-discretionary risk framework. The ATR-based stops, fixed position sizing, and tiered Gate 1-3 scoring map closely to risk concepts (volatility-adjusted sizing, drawdown control) I work with in FRM coursework. Relevant experience: Built pipelines using yfinance/Binance APIs for multi-timeframe technical analysis (EMA/ADX/MACD/RSI), and worked with Backtrader for Sharpe Ratio, Max Drawdown, Win Rate reporting — matching Module 2's exact metrics. Timeline & milestones: M1 (2wk): Scholar Agent — multi-timeframe ingestion, 3-gate scorer, Telegram report M2 (2-3wk): Backtesting — 7 strategies, 3yr data, performance report M3 (2wk): Risk Engine — ATR stops/sizing, trailing stop, hard no-widen rule M4 (2-3wk): Paper trading — 4wk sim, Kite/Binance scaffolding, approval flow Question: For Gate 1, if weekly EMA200 is bullish but daily EMA50 just crossed bearish — does the signal drop to "watch only" immediately, or is there a confirmation-candle grace period before downgrading? NDA welcome, milestone-based payment, full source handover.

@Ayush120701

I am a dedicated and detail-oriented software developer with practical experience in embedded systems, Python, C/C++, Qt, Raspberry Pi, STM32, signal processing, and web development. I enjoy solving real-world technical problems and building reliable, efficient, and user-friendly solutions. I have worked on projects involving hardware-software integration, UDP/serial communication, GUI applications, automation, signal generation and detection, and modern websites using React. I am comfortable understanding client requirements, debugging complex issues, improving existing systems, and delivering clean, working solutions. My strengths are problem-solving, patience, clear communication, and commitment to quality. I always try to understand the project deeply before starting, so I can provide practical and long-term solutions instead of temporary fixes. I am looking forward to working with clients who need reliable technical support, software development, embedded system development, or website development. I will give my best effort to complete every project on time and with full responsibility.