Pandas Jobs

I have a working Python script that reads an XLS sheet, logs in to Zerodha and fires trades; I now need the same workflow adapted for StoxKart. The new script must log in with a session token (no API key flow) and then: • Parse the XLS file row-by-row • Place the corresponding orders in StoxKart • Immediately fetch and record each order’s status so I can reconcile fills in the sheet Please keep the structure clean and modular so I can drop in different brokers later. If you have handled StoxKart’s session-based authentication before, that’s a plus. Deliver the fully-commented .py file along with any helper modules, and include a short README showing startup steps and the format the XLS parser expects.

I am preparing a quantitative study on “Inequality in participation versus visibility in online communities” . The raw material will come from one or more publicly-available Kaggle datasets; the challenge is to turn those data into a coherent, publication-level research project. Here is what I need from you: • Help me locate or combine the right Kaggle datasets, then document the download and preprocessing steps (Kaggle API, Python pandas, or R tidyverse are fine). • Define robust operational metrics for participation (e.g., post frequency, comment depth) and visibility (e.g., up-votes, follower counts, ranking on leaderboards). • Build the analysis pipeline—cleaning scripts, exploratory statistics, and the main inferential models (regression, GEE, or...

I need a reliable, repeatable way to run large batches of text data through a processing pipeline. The raw material typically lands in a folder as plain TXT or CSV files; once the script starts it should pick everything up, work through each file one after another, and write the processed results to a clearly named output directory. Core expectations • The workflow is fully automated: one command should launch the entire run. • Processing steps are modular so I can easily switch individual stages on or off later. • It must cope with thousands of lines per file without crashing or slowing to a crawl. • Clear logging to show each file’s status and any errors that occur. • Clean, well-commented source code plus a short README explaining setup and usag...

I need a Python-based trading algorithm that trades both the Nifty and Bank Nifty indices. The code should run locally on Python (feel free to lean on pandas, NumPy, TA-Lib, backtrader or similar libraries) and must be able to import and work with historical market data only—no live feed is required for this milestone. Here is what I expect: • A clean, well-commented Python script (or notebook) that ingests historical data, generates trade signals, executes the logic, and outputs detailed performance metrics and an equity curve. • Clear instructions on how to map the code to CSVs or API endpoints I already use for historical NSE data. • A short README explaining any configurable parameters so I can tweak settings for further experiments. Back-testing accuracy, ...

I need a reliable algorithmic bot that can execute an intraday strategy on the stock market. The core idea is simple: pull real-time equity data, apply my day-trading rules, and place orders automatically through a brokerage API (Interactive Brokers, Alpaca, or any comparable platform you are comfortable with). Low-latency data handling, solid risk management (position sizing, stop-loss, and max-drawdown limits), and accurate order execution are critical. Speed matters—I’d like a working version delivered as soon as realistically possible, so please factor rapid development and clear communication into your timeline. Python is my preferred language because of its mature ecosystem (Pandas, NumPy, TA-Lib, backtrader, etc.), but I’m open to alternatives if you can prove com...

Job Title: Python Developer for Geophysical Data Analysis (K-Means Clustering & 3D Visualization) Project Overview: I am a Geophysics student working on a structural mapping project of the Red Sea Rift. I have a dataset of 50 years of earthquake records (USGS CSV format) and need a Python expert to build a machine learning workflow that clusters these events based on their seismic attributes to identify hidden fault structures. Scope of Work: Data Pre-processing: Clean a USGS earthquake catalog and perform feature scaling on four specific attributes: Latitude, Longitude, Depth, and Magnitude. Unsupervised Machine Learning: * Implement K-Means Clustering to group seismic events. Provide an Elbow Method plot to justify the optimal number of clusters (K). 3D Visualization: * Create ...

Project Overview: We are seeking an experienced Machine Learning Specialist to assist in the development and implementation of machine learning models for the controlled synthesis of carbon dots (CDs). This project involves data-driven prediction of optical properties of CDs based on key reaction parameters. The ideal candidate will have expertise in machine learning algorithms, data preprocessing, feature engineering, and model optimization. Knowledge of Python and relevant libraries (e.g., Pandas, Scikit-learn) is essential. Project Description: The project aims to apply machine learning techniques to predict and optimize the synthesis of carbon dots, focusing on properties like fluorescence intensity, emission wavelength, and stokes shift. The dataset comprises experimental data on 80 ...

We need Python Programmer for coding share market strategies. Please come back with your quotes.

I have a spreadsheet (~5,000 product rows) where each row contains a full HTML eBay listing template. Each row includes: ID SKU Description Short description The Description field contains a large block of HTML (decorative listing template), but the actual product description is embedded inside it. Your job is to extract the correct text. 1. Extract the Correct Description In every row, the real product description is located inside this HTML block: <div class="desc-rd desc-text"> Requirements: Extract only the content inside this div Ignore all other HTML content in the row (menus, images, headers, shipping info, footer, etc.) Do not use the rest of the HTML outside this block 2. Clean the Extracted Text The content inside the div typically contains HTML such as...

I have a numerical dataset that needs a careful scrub before any sense can be made of it. The main headache right now is outliers—they are skewing the story the numbers are trying to tell. I need you to detect, diagnose, and treat those extreme values using an approach you can justify statistically (Python / Pandas, R, or even advanced Excel are all fine as long as the method is transparent and reproducible). Once the data are healthy, I want straightforward descriptive statistics—think clear measures of central tendency, dispersion, and a concise written interpretation that highlights anything interesting the cleaned data reveals. No forecasting or trend-spotting models this time; just an honest summary of what the numbers say after the noise is removed. Deliverables: •...

I have a flow of numerical data arriving on a regular basis and I want the entire predictive-analysis cycle handled automatically in Python. The goal is a script or small pipeline that can pull the fresh data, clean and transform it, train or update a predictive model, and then return the forecasts (plus standard performance metrics) without manual intervention. I expect you to choose the appropriate Python libraries—think pandas for wrangling, scikit-learn or a comparable framework for modelling, and perhaps joblib or pickle for model persistence—and stitch them together in a way that lets me trigger everything with a single command or scheduled job. Clear, well-commented code and a short README that shows how to run the automation are part of the deliverable. If you can set ...