NumPy Jobs

I need a Python-based trading bot that executes a clean trend-following strategy and feeds its output to a lightweight web dashboard. The trading logic should automatically detect and ride upward or downward trends, handle position sizing, manage risk with configurable stop-loss / take-profit rules, and run with as few external dependencies as practical (NumPy, Pandas, TA-Lib are fine). Exchange connectivity is flexible: as long as live orders and historical price data can flow reliably, I’m happy to integrate through CCXT or a direct API of a major venue such as Binance, Coinbase, or Kraken—let me know which you can implement fastest. The dashboard is just as important as the core bot. Through it I want to see: • Real-time performance metrics (open PnL, equity curve, ...

Google Search Trends Analysis (Python) ## Project Overview This project analyzes **Google Search Trends data** using Python to understand how a keyword's popularity changes over time and across regions. The analysis covers **15 countries**, compares **time-wise interest**, and explores **related keywords** to uncover search behavior patterns. ## Objectives * Analyze time-wise search interest of a keyword * Compare keyword popularity across 15 countries * Identify and analyze related search keywords * Visualize trends for better insights ## Tools & Technologies * Python * Pytrends (Google Trends API Wrapper) * Pandas * NumPy * Matplotlib * Seaborn * Jupyter Notebook ## Project Structure ``` ├── google data analysis # Main notebook ├── # Proj...

I have a set of finance-related CSV files that need to be explored, cleaned, and summarised. The goal is strictly descriptive analysis—think clear statistics, trends, and visual snapshots—without venturing into predictive modelling or prescriptive optimisation. All raw data will arrive as comma-separated files. You are free to use Python (pandas, NumPy, Matplotlib, Seaborn), R, Excel Power Query, or a comparable toolkit, as long as the workflow is reproducible and well-documented. Deliverables: • A concise cleaning script or notebook that imports each CSV, handles missing or inconsistent entries, and outputs a tidy dataset • A written summary (PDF or Markdown) of key descriptive metrics—averages, distributions, correlations, outliers—tailored to a fina...

QuantConnect / LEAN Python Algo Trading Developer (IBKR) We are looking for an experienced algorithmic trading developer to build and optimize trading strategies for US stocks and ETFs using QuantConnect (LEAN) and Interactive Brokers (IBKR API). This is a hands-on development role focused on implementing and deploying systematic trading strategies. Scope of work Develop algorithmic trading strategies in Python (QuantConnect / LEAN) Backtesting and performance optimization Integrate strategies with Interactive Brokers (IBKR) Implement risk management and position sizing logic Work with historical and live market data Deploy and maintain strategies in live trading environment Analyze performance (PnL, Sharpe, drawdown) Requirements Proven experience in algorithmic trading / quan...

I’ll hand over three raw datasets—sales transactions, customer demographics, and product inventory—spanning several stores. Your task is to stitch them together, clean inconsistencies, and delve into them with Python. Using Pandas, NumPy, and Matplotlib (feel free to add Seaborn or Plotly if that speeds insight), uncover how buying behaviour shifts: • weekday versus weekend • month by month I’m interested in concrete, data-backed stories: which products spike on Saturdays, whether certain customer segments shop more mid-week, seasonal category swings, ticket size trends, and anything else you spot that helps me fine-tune promotions and staffing. Deliverables • A merged, tidy dataset ready for future modelling • A well-commented Jupyter n...

I am working on a graduate-level project that involves mixed data types and I need one-on-one guidance to reinforce my skills—especially in data cleaning and preprocessing with Python. The focus will be on the practical, step-by-step application of Pandas, NumPy and Matplotlib while staying fully compliant with academic integrity guidelines; no AI-generated work is permitted. My most urgent challenges include: • Handling missing values • Removing duplicates • Dealing with outliers Beyond cleaning, I will also ask for advice on choosing suitable descriptive statistics, selecting the right statistical tests, and presenting results clearly. Expect questions on relationship analysis and time-series concepts as the project evolves. What I’m hoping for: cl...

This homework requires a working Python program that respects every constraint my instructor set. Only the libraries covered in class may be imported—specifically NumPy, Pandas, and Matplotlib. A few lightweight SQL queries are acceptable, but no external packages beyond that list can appear in the final code. The assignment brief and its “AI usage” rules will be shared right after we start; the code must follow them to the letter. I need mid-level, object-oriented design, clear function separation, and comments in English so the grader can follow the logic. Deliverables • A single, well-commented .py file (or notebook, if advised in the brief) using only NumPy, Pandas, Matplotlib, and basic SQL. • A short README explaining how to run the script and where ...

I need an expert in Python to help with data analysis and processing tasks, specifically focused on unstructured data such as text or images. The ideal candidate should be proficient in Python libraries and tools designed for handling unstructured datasets. Your role will involve creating scripts or solutions to extract insights, patterns, or other relevant analyses from the data provided. If you have a strong command over libraries like Pandas, NumPy, or specialized tools for text and image processing, I’d love to hear from you.

Project: Titan-X Prime (Quantitative Options Scalper) Target Environment: Python 3.13 (Free-Threaded/No-GIL) Primary Goal: To capture high-conviction "Gamma Bursts" in Nifty Options while neutralizing the 2026 0.15% STT tax drag through microstructure-based precision. 1. System Architecture (Concurrency Model) The system must utilize a Multi-Threaded Producer-Consumer model to leverage Python 3.13’s true parallelism. • Thread 1 (Ingestion): WebSocket feed for Nifty Spot, Futures, and ATM/Near-OTM Option Chain (±10 strikes). • Thread 2 (Microstructure): Real-time calculation of Weighted Order Book Imbalance (WOBI) every 100ms. • Thread 3 (GEX Engine): Recalculation of Dealer Gamma Exposure (GEX) and Zero-Gamma Level every 60 seconds. • Thread 4 (Ex...

I need help with a Python exercise focused on data analysis. Key tasks include: - Data cleaning/extraction - Visualization/plotting - Statistical analysis Ideal skills and experience: - Proficiency in Python, especially with libraries like Pandas, NumPy, and Matplotlib - Experience with data cleaning and preparation - Strong data visualization skills - Background in statistical analysis

The project is to build a Python-based bot that trades equities on NYSE, NASDAQ, and AMEX. It must run end-to-end without manual intervention: gathering live market data, analysing it in real time, placing orders, and continuously enforcing risk limits. Core functionality • Automated trade execution driven by a configurable strategy engine • Real-time market analysis with fast data ingestion (websocket or streaming API) • Built-in risk management: position sizing, max draw-down and stop-loss rules Technical notes Python is mandatory; common libraries such as pandas, NumPy, TA-Lib, and asyncio are expected. The code should be clean, modular, and ready to plug into broker APIs like Interactive Brokers, Alpaca, or Tradier. A lightweight front-end (CLI or simple dashbo...

I need a seasoned quantitative developer who can turn my Iron Condor options strategy into a fully automated trading BOT for Indian markets. The system must trade exclusively in listed options and operate through the NSE NOW interface, handling everything from order placement and modifications to position monitoring and exit. Core workflow • Pull live option chain data, calculate real-time Greeks, and identify entry prices that satisfy my Iron Condor rules (delta-neutral, predefined distance between strikes, and minimum credit per leg). • Submit simultaneous multi-leg orders via NSE NOW, confirming fills and retrying intelligently if partials occur. • Track open positions tick-by-tick, updating P/L and Greeks so risk controls—max loss, max profit, time-based exits...