Pandas Jobs

Project Requirement (Step-by-Step Implementation) DATASET - Requirements - (Fully clear implementation with explanation and workings) Step 1 — Business Question & Topic Framing The notebook should clearly define the business problem: Question: “In a given city, which business categories look most promising to start a new business, and what factors correlate with high customer ratings?” Expected outputs: Top 10 “high-opportunity” business categories in the selected city A short checklist of key success drivers derived from data A simple predictive model to classify businesses as High Rated vs Not Step 2 — Input: Import Data from Web Dataset Files Use the public Yelp Open Dataset Programmatically read: optionally a sampled Parse JSON...

I need a smooth bridge between my Kotak Neo account and Microsoft Excel so I can watch live option prices and fire orders without leaving the sheet. The core of the job is a Python backend that speaks to the Kotak Neo API and an Excel front-end that shows real-time data and lets me trade. Here is exactly what I’m after: • A Python script (cleanly structured, commented) that authenticates with Kotak Neo, opens the WebSocket for live market data, and uses REST for order placement and cancellation. • An Excel workbook wired to that script. Option prices and Greeks should stream into dedicated cells instantly—no noticeable lag—while order status updates in place. • In-sheet controls (buttons, formulas, or minimal VBA, whichever is simpler) that let me subm...

I have a production database full of raw numerical records that first need to be cleaned and processed before any modelling work can begin. The task centres on building a repeatable Python pipeline that pulls the data directly from the database, applies rigorous quality checks, and outputs a tidy, analysis-ready table. I expect the script to connect via SQL (SQLAlchemy or similar), ingest the relevant tables, and then handle missing values, outliers, type casting, and duplicate detection. Once cleaned, the data should be written back to a new table in the same database and optionally exported to CSV so that downstream teams can verify the results in Excel if they choose. Key deliverables – Python script (Pandas, NumPy, SQLAlchemy preferred) that performs the full ETL/cleaning...

I need a Python-savvy content creator to produce a short course called “Introduction to Python Business Automation.” Scope • 5 tutorial videos, each 5–10 minutes. • Screen-recorded walkthroughs with clear voice-over narration (no on-camera shots or animation needed). Topics to cover 1. Data processing and analysis – simple CSV/Excel manipulation with pandas. 2. Web scraping – gathering data with requests and BeautifulSoup or similar. 3. Automating emails – generating and sending messages with smtplib. 4. Quick project that combines the above skills. 5. Recap and next steps for learners. What I expect • Well-structured scripts and live coding so beginners can follow line-by-line. • Clean, readable code and comme...

I am preparing an academic-style report that demonstrates the full predictive analytics pipeline on a genuine dataset. The scope begins with sourcing or recommending a high-quality public dataset, continues through cleaning, feature engineering, model selection, and evaluation, and ends with a polished narrative that translates numbers into clear, actionable insights. The analysis must centre on predictive techniques—think regression, classification, or time-series forecasting—whichever best suits the chosen data. Python with Pandas, NumPy, scikit-learn, and Seaborn/Matplotlib is welcome, but I am equally comfortable reviewing an R workflow if that is your forte; the key is reproducibility and clarity. Deliverables • A structured report in the required academic forma...

## Professional Summary Quantitative analyst with 3+ years experience in algorithmic trading. Proficient in Python with expertise in independently building end-to-end quantitative trading systems. Specialized in cryptocurrency and traditional financial markets. ## Technical Skills - **Languages:** Python (Pandas, NumPy, scikit-learn, PyTorch) - **Quantitative:** Statistical arbitrage, Mean reversion, Factor modeling, Risk management - **Trading:** Crypto exchanges (Binance, OKX), Backtesting, Strategy development ## Work Experience **Quantitative Developer** | Shanghai Quantitative Capital | 2021.09 - Present - Independently developed cryptocurrency trading system managing $3M AUM - Designed 15+ trading strategies with live performance of 18-35% annual returns - Built multi-factor stock...

I want my intraday index-option opening-range breakout strategy coded end-to-end in Python, wired directly to the Dhan API for both live data and order execution. The logic is simple but must be battle-ready: • Opening range: the first 5 minutes. Once price breaks the range high or low, the script fires the corresponding trade. • Three sequential profit targets, each booked automatically. • Dynamic risk: a fixed-point stop-loss that moves to cost or better once a target is hit. I already hold a Dhan account and will handle API activation myself, so you can concentrate purely on clean, well-documented code. The script should: • Authenticate and stream tick data in real time. • Place, modify and exit orders per the rules above, including partial exits at e...

Starting from zero coding knowledge, I want to master Python well enough to design, back-test, and deploy my own algorithmic stock-market strategies. The learning path has to begin at absolute basics—installing Python, understanding syntax and data types—and progress methodically toward advanced topics such as object-oriented programming, vectorised analysis with pandas and NumPy, back-testing frameworks like Backtrader or Zipline, broker/API integration (yfinance, Alpaca, Alpha Vantage), and finally live execution. The format that suits me best is a blend of short live sessions (Zoom, Google Meet, or similar) and hands-on homework. After each module I’ll submit code for review so I can correct mistakes early and build solid habits. Key milestones I need covered: &bull...

I have an existing algo that routes orders to Angel One through their Smart API, I trade intraday only in Nifty options by my predefined strategy, I run a Python algo trading system that streams real time quotes directly from my broker's API connected with AWS. My strategy is based on the closing rate of 2 minutes timeframe's candle and the closing value of Vwap indicator, but I am facing a problem, my algo not reading and picking candle's closing rate and indicator's closing value correctly, that's why algo not placing correct execution rate and not placing correct Stop Loss orders. For example – I need to place Stop Loss order of some closed candle’s high rate, if candle’s high is 88.80, so algo must place an order of exact 88.80, currently my ...

I have several years’ worth of raw Bloomberg CSV exports and supporting Excel sheets that must be transformed into clear, actionable trend insights. The work revolves around Python-based data analysis only—no scraping or automation tasks this time. The job I will hand over a structured folder of historical price, volume, and macro-indicator files. Your task is to import, clean, merge, and explore this data to spot medium- and long-term patterns that matter to portfolio allocation decisions. Accuracy and transparency are critical, so every step of the workflow should be well commented and reproducible. Preferred stack Pandas, NumPy, Jupyter (or a clean .py script), plus Matplotlib/Seaborn or Plotly for the charts. If you favour another mainstream analytical library that speeds...

I have several years’ worth of raw Bloomberg CSV exports and supporting Excel sheets that must be transformed into clear, actionable trend insights. The work revolves around Python-based data analysis only—no scraping or automation tasks this time. The job I will hand over a structured folder of historical price, volume, and macro-indicator files. Your task is to import, clean, merge, and explore this data to spot medium- and long-term patterns that matter to portfolio allocation decisions. Accuracy and transparency are critical, so every step of the workflow should be well commented and reproducible. Preferred stack Pandas, NumPy, Jupyter (or a clean .py script), plus Matplotlib/Seaborn or Plotly for the charts. If you favour another mainstream analytical library that speeds...

I need a seasoned Python developer to turn a rule-based intraday strategy (details shared after we sign an NDA) into a fully-production trading system for the Indian markets. The code must be clean, modular OOP with every parameter pulled from a single, human-readable config file—no magic numbers buried in the logic. You will be expected to: • Back-test the strategy on historical OHLC data, incorporating realistic slippage, brokerage and other frictions. • Generate clear trade logs plus P&L, drawdown and exposure analytics so I can validate the edge before we go live. • Integrate live and paper execution through Angel and Dhan APIs; prior hands-on experience with these brokers is essential. • Handle derivatives instruments confidently, from contract ...

I have several years’ worth of raw Bloomberg CSV exports and supporting Excel sheets that must be transformed into clear, actionable trend insights. The work revolves around Python-based data analysis only—no scraping or automation tasks this time. The job I will hand over a structured folder of historical price, volume, and macro-indicator files. Your task is to import, clean, merge, and explore this data to spot medium- and long-term patterns that matter to portfolio allocation decisions. Accuracy and transparency are critical, so every step of the workflow should be well commented and reproducible. Preferred stack Pandas, NumPy, Jupyter (or a clean .py script), plus Matplotlib/Seaborn or Plotly for the charts. If you favour another mainstream analytical library that speeds...

I have a mixed dataset with both text and numerical features that first needs to be scrubbed and structured in Python, ideally with pandas for the heavy-lifting. After the cleaning stage I want an exploratory dive—summary statistics, correlations, distributions, outlier checks—so we can truly understand what is driving the numbers (and the words) before modeling. The ultimate goal is a predictive analysis that not only trains a reliable model but also tells a compelling story through clear visualisations. Feel free to bring in scikit-learn, seaborn, matplotlib or any other Python libraries that will speed up the workflow, as long as the workflow is reproducible (Jupyter Notebook or .py scripts are fine). Deliverables • Cleaned dataset ready for downstream use • ...

(SOLAMENTE EN ESPAÑOL, SI NO HABLA ESPAÑOL NO LO SOLICITE POR FAVOR). Quiero reforzar mis bases de Python y python orientado al análisis de datos. Manejo lo esencial del lenguaje, pero todavía me considero principiante. Busco a alguien de habla hispana que domine la materia y sea capaz de explicar con claridad, paso a paso, para que cada sesión me deje conceptos y ejercicios aplicables de inmediato. Mi idea es que trabajemos con ejemplos reales; así, mientras resolvemos mis dudas, voy afianzando lógica, sintaxis y flujo de trabajo. Agradeceré si puedes compartir notebooks o pequeños retos que pueda revisar entre clases, y que luego comentemos juntos para pulir errores y comprender mejor el porqué de cada línea de...