Numpyproiecte

ELABORAREA UNEI LICENTE CU TEMA: Aplicatie didactica pentru invatarea utilizarii limbajului PYTHON in prelucrarea imaginilor folosind modulele NUMPY si MATPLOTLIB. ACEASTA TREBUIE SA CONTINA 8-10 LABORATOARE, LUCRARI...

...Ingredient min/max limits Regularization to prevent micro-dosing overfitting Performs residual minimization after re-analysis. Supports iterative refinement (Trial-1 → Trial-2 → Trial-3 loop). This is an inverse analytical modeling engine, not a surface-level similarity system. Mandatory Requirements (No Exceptions) You must have: Strong Python experience (minimum 4–5 years) Advanced Pandas and NumPy knowledge Experience with scientific or analytical datasets Experience implementing regression models Experience with constrained optimization (linear or nonlinear) Understanding of L1/L2 regularization Experience with numerical modeling (SciPy optimize or similar) Ability to clearly explain statistical calibration logic Clean modular code architecture ...

...Identify marker compounds and decision rules Create probabilistic ingredient combination models Generate optimized recipe outputs (percentage-based, normalized to 100%) Structure the system in a modular and scalable Python architecture Build clean, documented code suitable for long-term expansion Required Skills (Mandatory) Strong Python programming experience (3+ years) Advanced Pandas & NumPy knowledge Scientific data processing experience Experience working with structured chemical datasets Algorithm development experience Data normalization & similarity scoring logic CSV / Excel data parsing Clean code & modular architecture mindset Strong Plus Experience with chromatography or GC-MS data Background in analytical chemistry Experience in scientif...

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 ...

...86-million-dollar portfolio and need them transformed into clear, defensible investment insights. The core purpose is to evaluate current and prospective opportunities, not merely spot trends or forecast performance. Expect to dig into raw transactional data, balance sheets, and market feeds, then surface the strengths, weaknesses, and hidden potential of each holding. You are free to work in Python (Pandas, NumPy, SciPy), R, SQL, Excel Power Query, or any other toolkit you trust, so long as the outcome is reproducible. I will provide the data in CSV and relational-database dumps; secure transfer protocols are already in place. Deliverables • Cleaned and well-documented dataset (with transformation scripts) • Analytical report highlighting valuation metrics, risk ...

...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 ├── # Project documentation ``` ## Key Analysis * Interest over time (trend analysis) * Country-wise keyword comparison * Related queries and keywords analysis * Data visualization using line charts and bar plots ## How to Run

...the text and image sets. • Concise visual summaries—charts for the text, heat-maps or feature plots for the images—exported in high-resolution formats I can drop straight into presentations. • A short written report (PDF or Markdown) highlighting key findings, unusual correlations and any recommendations that emerge from the analysis. Feel free to lean on standard libraries such as pandas, NumPy, NLTK/spaCy, OpenCV or PyTorch-based utilities—whatever helps you surface meaningful insights quickly and reproducibly. If you have other preferred tools that suit mixed unstructured data, I am open to them so long as the final deliverables remain easily reusable. I’ll supply a structured folder containing sub-directories for /text and /images along...

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...

...performance (PnL, Sharpe, drawdown) Requirements Proven experience in algorithmic trading / quant development Strong Python programming Experience with QuantConnect or LEAN Engine Experience with IBKR API integration Understanding of US equities / ETFs markets Experience with backtesting frameworks Knowledge of trading risk management Nice to have Intraday or HFT strategies pandas / numpy / scipy Walk-forward optimization Experience in prop trading / hedge fund C# (LEAN) Project details Market: US stocks & ETFs Broker: Interactive Brokers Platform: QuantConnect / LEAN Strategy type: systematic / algorithmic Engagement: long-term collaboration possible To apply Please include: Relevant algo trading projects QuantConnect / LEAN experience IBKR integrati...

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 Ju...

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 ...

...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 whe...

...scrapes the data I need. The next step is to layer in proper analytics and present the results through a cleaner, mobile–friendly frontend while leaving the core scraper intact. Python scope • Extend the existing code so the scraped dataset feeds directly into statistical analysis, mathematical modelling, and quick data-visualisation routines. • Prefer familiar libraries such as Pandas, NumPy/SciPy, Matplotlib or Plotly, but I’m open to alternatives if they suit the task better. • Keep the workflow end-to-end: once the scraper finishes, the calculations should run automatically and expose structured results ready for the UI. Frontend scope • Refresh the HTML/CSS (vanilla or lightweight framework) to give the dashboard a modern, responsi...

...surface clear, actionable trends. The sole objective is to analyze historical data—specifically inventory data—so our team can understand how stock levels have moved over time and where the pressure points sit. Here’s what I have: several years of SKU-level records housed in a SQL database, plus a few CSV extracts. I need you to pull and clean the data, run robust trend analysis in Python (Pandas, NumPy, preferably some Matplotlib or Seaborn visuals), and package the insights so they are easy for our planners to act on. Time-series forecasting or demand planning frameworks aren’t required right now, but please structure the code so we could layer those in later if needed. Deliverables: • Well-documented Python notebook or script that connects to our...

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...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 ...

...Fast Exit: Exit if $LTP < text{Prev 1-min Low}$. 2. Predictive Exit: Exit if $WOBI$ flips sign for $> 3$ seconds. 3. Trail: Trigger 0.5x ATR trail once profit exceeds 1.5x ATR. 4. Technical Implementation Notes for Developer 1. Parallelism: Use with shared-memory objects; avoid multiprocessing to eliminate serialization latency. 2. Speed: Implement WOBI and GEX calculations using NumPy vectorization or Numba (JIT). 3. Connectivity: Use DhanHQ or Fyers API with a persistent WebSocket connection. 4. Tax Sensitivity: All "Break-Even" logic must be calculated as: $text{Entry Price} + (text{Total Statutory Charges} div text{Lot Size})$....

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

...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 dashboard) for live logs and performance metrics would be ideal. Deliverables 1. Fully-commented Python source code and 2. Strategy configuration file(s) allowing quick paramet...

I need a seasoned quantitative developer who can turn my Ir...historical options data shows slippage-adjusted results consistent with manual Iron Condor benchmarks. • Live paper-trading session for one full trading day on NSE NOW executes, adjusts, and exits orders without manual intervention. • Clean, well-commented source code plus a config file so strikes, lot sizes, risk caps, and timings can be tweaked easily. Preferred stack Python with Pandas, NumPy, and a proven NSE NOW API wrapper suits me best, but I’m open to other robust solutions if they interface reliably with NSE NOW and meet exchange compliance. If you have prior experience building Indian options engines and can demonstrate exchange-grade reliability, let’s talk schedules and milestones s...

I need assistance with a college Python assignment. The task involves writing a few very small programs with functions or algorithms. Ideal Skills & Experience: - Proficiency in Python and NumPy - Experience in writing functions and algorithms - Ability to handle basic programming tasks Important: the programs must be original and cannot be copied from the internet or ChatGPT, as the college uses Turnitin to detect plagiarism. Please see below the request: Task 1 Write a Python program that defines and calls the following functions: 1. A function that calculates the area of a circle 2. A function that checks if a number is even or odd 3. A function that returns a formatted string Task 2 Write a Python script that does the following when executed: 1. Imports the mat...

AI & Full-Stack Tech Client Acquisition Partner Needed (Commission-Based) I am an experienced Full-Stack AI & Software Engineer with strong hands-on expertise in building production-ready systems, including: AI / LLM Development (OpenAI, Gemini, LangChain, RAG, AI Agents) Machine Learning & Data Science (Python, Pandas, NumPy, Scikit-learn) Backend Development (Python, Node.js, REST APIs) MERN Stack (MongoDB, Express, React, Node.js) DevOps & Deployment (Docker, CI/CD, Cloud hosting) Automation & AI Chatbots (WhatsApp, Web, CRM integrations) I deliver scalable, high-quality solutions with clear communication and on-time execution. Role: Client Acquisition Partner I’m looking for a motivated, reliable partner who can bring genuine AI / software developme...

I’m looking for a data scientist based anywhere in Latin America to help me create reliable predictive models for a finance-focused project. You’ll start with large historical datasets stored in SQL and deliver models that accurately forecast key financial indicators. I work mainly with Python, so you’ll find Pandas, NumPy, Scikit-learn and, when deep learning is justified, TensorFlow already in place. If you prefer R for certain tasks, that’s perfectly fine as long as the final workflow remains reproducible. The end-user needs to consume insights through Power BI, so once the model is validated I’ll ask you to craft intuitive dashboards that highlight drivers, confidence ranges and any red-flag anomalies the model detects. Solid statistical grounding ...

...collection and interpretation practices; second, shaping the core algorithms that will power the software. We’ll be working with the classic trio of techniques—Time Domain, Frequency Domain, and Envelope Analysis—so please be comfortable explaining best-practice workflows and translating them into practical code logic. I already have sample datasets and a basic development environment (Python + NumPy/Pandas/Matplotlib), but I’m flexible if you prefer other analysis libraries such as SciPy or MATLAB. Deliverables • Live or recorded coaching sessions walking through proper sensor placement, sampling parameters, and data cleansing • Annotated pseudo-code or prototype functions that implement the three analysis techniques, ready for integration...

...teams including product and engineering • Mentor junior AI engineers and contribute to technical leadership • Conduct research and implement state-of-the-art AI techniques • Ensure data quality, security, and model performance optimization Required Skills & Qualifications: • 10+ years of experience in AI/ML or Software Engineering roles • Strong proficiency in Python and data processing libraries (NumPy, Pandas) • Hands-on experience with TensorFlow, PyTorch, Scikit-learn • Strong understanding of Deep Learning, NLP, Computer Vision • Experience with Model Deployment & MLOps pipelines • Experience working with Cloud platforms (AWS / Azure / GCP) • Strong knowledge of Data Engineering & Big Data tools • Experience...

...interfaccia web. • Riconoscimento automatico dei fogli e delle colonne numeriche. • Selezione multipla delle colonne da analizzare. • Calcolo e visualizzazione della deviazione standard per ogni colonna scelta, con risultato immediato a schermo. Preferenze tecniche (ma resto aperto a proposte migliori) • Backend in Python con Flask o Django, oppure Node.js. • Librerie consigliate: pandas, NumPy o equivalenti per il calcolo statistico. • Front-end semplice e responsivo, anche con Bootstrap. Consegna 1. Codice sorgente completo e commentato. 2. Istruzioni di installazione (README) per avvio locale. 3. Breve guida utente che spieghi come caricare il file e leggere i risultati. Se puoi offrire anche un piccolo deploy di test (H...

...hours—no end-of-day batching. I code in Python myself, so please keep the architecture transparent: well-named modules, docstrings, and a that pins every dependency. Back-tests on at least two years of 5-minute data, a walk-forward validation segment, and a short README outlining how to reproduce the results will be my acceptance criteria. If you already have experience with pandas, NumPy, scikit-learn or TensorFlow, and you know how to talk to Indian broker APIs via REST or websockets, this should feel familiar. Let me know the models or reinforcement-learning frameworks you think best suit intraday equity trading and how you will protect against over-fitting....

...exploratory analysis, pattern discovery, and interpretation so the insights are both rigorous and clearly actionable. The core task is straightforward: ingest the raw text files I provide, clean and structure them appropriately, then run exploratory and statistical analyses that reveal key themes, sentiment trends, and any significant correlations you uncover. Feel free to use Python with pandas, NumPy, NLTK, spaCy, or other standard NLP and data-analysis libraries—whatever you are most comfortable with as long as the workflow is reproducible. Because I don’t have a hard deadline, quality and clarity matter more than speed. You will deliver: • Well-commented notebooks or scripts that walk through each analysis step • A concise report (PDF or Markdown) e...

...missing values, standardise field names) • explore key patterns—frequency of purchase, average order value, cohort retention, and any other meaningful customer behaviour metrics • generate charts or interactive visuals that I can drop straight into presentations (matplotlib, seaborn or Plotly are fine) • export a short written summary of findings, ideally as a Markdown or PDF report Pandas, NumPy, and a mainstream visualisation library are expected; if you prefer a Jupyter Notebook that’s perfect, but a standalone .py script with comments also works. Keep the workflow modular so I can feed in new monthly data without rewriting sections. Once delivered, I should be able to run everything locally with a single command, point it at fresh CSVs, and ...

...pillars inside a single, well-structured repository: • Automated trading – place, modify, and cancel orders programmatically (market, limit, SL). The engine should be able to run multiple strategies concurrently and execute through Zerodha’s regular and MIS product types. • Market analysis – fetch historical and live tick data, calculate technical indicators on the fly (you’re free to use pandas, numpy, TA-Lib, or your own functions), and generate entry/exit signals according to parameters I will supply. • Risk management – position sizing, max daily drawdown, per-trade stop-loss, overall exposure caps, and a safety kill-switch if connectivity or margin issues arise. I will provide API credentials and strategy logic; your job is to...

Excel & Python Data Analysis for Sales and Operations Reporting...and validation, and building Python-based analysis scripts to calculate KPIs such as revenue trends, inventory turnover, order fulfillment rates, and cost variations. The final output should include well-structured Excel reports and optional visual summaries generated via Python. Key Requirements: Strong experience with Excel (advanced formulas, pivot tables, data validation) and Python (Pandas, NumPy, Matplotlib or similar). Ability to handle messy datasets, automate repetitive analysis, and ensure data accuracy. Deliverables: Cleaned datasets, analysis scripts, summary reports, and clear documentation explaining the workflow. This project is ideal for someone who enjoys turning raw data into actionable insight...

...methods agree or diverge. • At least one live session (Zoom, Meet, or similar) to walk me through your approach; the project involves explanations that are too involved for chat alone. • A concise technical memo or slide deck summarising conclusions and recommended next steps. Acceptance criteria – Code runs end-to-end on a fresh environment with standard libraries (scikit-learn, pandas, numpy, matplotlib/plotly, shap, lime, etc.). – Plots and tables directly link back to the underlying calculations, allowing me to reproduce every number. – During our live review you can clearly justify each methodological choice and articulate trade-offs between SHAP, LIME, PDP and ICE for the three target models. This assignment is scoped as a fixed-price...

...and for the population as a whole. • Compute entropy (Shannon or Rényi—please justify your choice) across sliding and tumbling windows so we can compare immediate behaviour to longer-term baselines. • Raise an alert when an entropy shift exceeds a configurable threshold, returning the supporting metrics and the related raw events. I expect well-structured, runnable code—Python with pandas/NumPy/SciPy is typical, though another language is fine if it delivers the same reproducible results—along with a concise README that shows how to install dependencies, feed sample logs, and interpret the output. Success is measured by: • Clean execution on my sample dataset (≈1 GB of mixed user activity). • Alerts that capture injec...

...themselves • Contrast between those pixels and adjacent pavement pixels • A distance weighting that values pixels closer to the camera more heavily than distant ones • A continuity penalty that reduces the score when the mask reveals breaks or flicker in the lane line Feel free to propose additional minor refinements if they improve stability, but please keep the core idea intact. OpenCV, NumPy, and scikit-image are the tools I already use elsewhere, so sticking to that stack will make adoption easiest. The code should be clean, modular, and fast enough to process typical 1080p sequences in real time or near real time on a modern CPU (GPU use is optional but not required). Deliverables • A self-contained Python module or notebook that loads the im...

I’m refining a series of logistic-r...and interpret the Hessian output for standard-error estimation. • Suggest parameter-regularisation strategies available directly in SciPy or via light dependencies I can bolt on. • Join one or two live screen-share sessions (30-45 min each) so we can step through residual plots, goodness-of-fit tests, and any edge-case handling. All work will happen in a clean Python 3.11 environment with NumPy, SciPy 1.11, Pandas, and Matplotlib already installed, so no need for heavy-weight ML frameworks. Deliverables are the commented revisions to my script plus a concise summary of the changes and reasoning. If you’ve previously tuned logistic models in SciPy (not just scikit-learn) and enjoy explaining the “why” as mu...

...histogram (or any other standard colour-profile information we agree on), and write the numeric results to a clean CSV file. I care as much about the code organisation as the actual extraction: classes, clear method separation, doc-strings and a simple command-line entry point are expected so I can drop the module straight into a larger .gal-based workflow. Feel free to rely on Pillow, OpenCV, NumPy or similar mainstream libraries as long as dependencies are listed in a requirements.txt. Deliverable • A self-contained Python package (Git repo or zip) with class-based implementation • One example script showing how to point it at a TIFF and produce the CSV • Sample CSV output generated from my test image I’ll test by running the script on the provided...

Hello, my name is fathalishah. i am from afghanistan i have been working for 3 years as full stack developer which can i work with html ,css,javascript,react,tailwindcss and bootstrap and backend i can work with Python+django django-restframe api , sql,mysql, and as desktop developer i can work with tkinter and also i can work as data scientest work with NUMPY ,PANDAS,scikit-learn,Matplotlib i hope i can do my best for you Thanks..

...PDF files * Clean and normalize messy real-world data * Write clear, maintainable utility scripts * Deliver working code (not just prototypes) --- ### Required Skills * Strong Python fundamentals * Real experience with web scraping * Data parsing and data cleaning * Comfortable working independently and async --- ### Nice to Have * BeautifulSoup, Scrapy, Playwright, or Selenium * pandas / numpy * Experience scraping government or legacy websites * Experience handling PDFs (text extraction, OCR) --- ### How We Evaluate * This role includes a **paid trial task (1–3 days)** * We care about **output and correctness**, not resumes * Clean, working code matters more than clever abstractions --- ### Important * Please include **2–3 sentences** describing your pas...

...because a Python script is throwing errors whenever it calls certain library functions. I need a sharp eye to dive into the code, pinpoint the exact cause, and leave me with a clean, reproducible fix. Here’s the situation as clearly as I can put it: • The faulty piece lives in a Python script that glues together key stages of the workflow. • The issue surfaces inside library-level calls—think NumPy, pandas, scikit-learn, TensorFlow, or similar. • Once corrected, the rest of the training and inference steps should run without manual work-arounds. What I’m expecting from you • A patched script that runs end-to-end on my side. • A concise changelog or inline comments explaining what broke and why your fix works. • Any environ...

...— SELL STRATEGY ENGINE Handles only short trades. Examples of rules: Price below VWAP Breakdown patterns Bearish flag Volume breakdown Entry, SL, Target logic Output: SELL signals only Module 4 — AlgoTest Strategy Formatting Convert BUY signals → Buy strategy format Convert SELL signals → Sell strategy format Backtest-ready rule structure Technical Requirements ✔ Python (Pandas, NumPy) ✔ TA indicators (VWAP, EMA, Volume) ✔ Modular coding structure ✔ Knowledge of AlgoTest rule system ✔ Experience in intraday trading logic Deliverables : Separate Python files: Chartink stock fetch script Strategy logic documentation Editable conditions Setup guide Note : Payment will be released only after successful setup and testing on my live A...

...project has started throwing errors, and I need it back in working order within 48 hours. The bug sits somewhere between the data-loading step and model training; once you jump in you’ll have full access to the Python code, sample data, and the original (working) training logs so you can compare behaviours. I work entirely in Python and TensorFlow, with a handful of standard utilities such as NumPy, pandas, and Matplotlib. No other frameworks are involved, so deep familiarity with TensorFlow’s debugging tools, graph/symbolic APIs, and eager execution will let you move quickly. Here is what I expect at hand-off: • A corrected script that runs start-to-finish on my machine (TensorFlow 2.x, Python 3.9). • A concise changelog or inline comments so I can see ...

...analysis of that periodic state so I can identify the short-wave instability noses and the curious stability islands created by the substrate’s geometry. All governing equations and parameter ranges are ready; what I still need is a clean, reproducible Floquet framework that extracts the multipliers for each wavenumber and returns clear stability maps. You are free to work in MATLAB, Python (NumPy/SciPy), or Julia as long as the code is well commented and numerically robust—spectral collocation or a high-order finite-difference approach is fine as long as convergence is demonstrated. Deliverables • A runnable script or notebook that assembles the monodromy matrix for the periodic WIBL coefficients and computes the Floquet multipliers across user-defined p...

...analysis of that periodic state so I can identify the short-wave instability noses and the curious stability islands created by the substrate’s geometry. All governing equations and parameter ranges are ready; what I still need is a clean, reproducible Floquet framework that extracts the multipliers for each wavenumber and returns clear stability maps. You are free to work in MATLAB, Python (NumPy/SciPy), or Julia as long as the code is well commented and numerically robust—spectral collocation or a high-order finite-difference approach is fine as long as convergence is demonstrated. Deliverables • A runnable script or notebook that assembles the monodromy matrix for the periodic WIBL coefficients and computes the Floquet multipliers across user-defined p...

I need a skilled analyst who can dive into my sales data and surface clear, time-based trends using a mix of SQL and Python. The raw figures sit in a relational database; from there you’ll write efficient SQL queries, pull the results into Python (pandas, NumPy, SQLAlchemy), tidy everything, then explore daily, weekly, and monthly patterns. Visualisations in matplotlib or seaborn and a concise written summary (PDF or Jupyter Notebook) will round out the story so stakeholders can quickly grasp how revenue is evolving and where seasonality or anomalies appear. Deliverables • Well-commented SQL scripts that reproduce the dataset • A Python notebook (or .py script) with all transformation and analysis steps • Three to five clear charts illustrating sales tren...

...analysis of that periodic state so I can identify the short-wave instability noses and the curious stability islands created by the substrate’s geometry. All governing equations and parameter ranges are ready; what I still need is a clean, reproducible Floquet framework that extracts the multipliers for each wavenumber and returns clear stability maps. You are free to work in MATLAB, Python (NumPy/SciPy), or Julia as long as the code is well commented and numerically robust—spectral collocation or a high-order finite-difference approach is fine as long as convergence is demonstrated. Deliverables • A runnable script or notebook that assembles the monodromy matrix for the periodic WIBL coefficients and computes the Floquet multipliers across user-defined p...

...entries—that needs exhaustive permutation matching. The goal is to scan every possible combination quickly in Python, identify all matches (or near-matches if you choose to add fuzzy logic), and return the results in a single Excel file ready for immediate analysis. Raw data and a short spec on what constitutes a “match” will be supplied; your task is to design an efficient, memory-savvy routine (pandas, NumPy, itertools, or any other high-performance approach you prefer) that can churn through roughly one hundred million comparisons without freezing up my workstation. Multithreading, vectorisation, or chunked processing—all are acceptable so long as they keep runtime practical and the output accurate. Deliverables • A well-commented Python script...

...— SELL STRATEGY ENGINE Handles only short trades. Examples of rules: Price below VWAP Breakdown patterns Bearish flag Volume breakdown Entry, SL, Target logic Output: SELL signals only Module 4 — AlgoTest Strategy Formatting Convert BUY signals → Buy strategy format Convert SELL signals → Sell strategy format Backtest-ready rule structure Technical Requirements ✔ Python (Pandas, NumPy) ✔ TA indicators (VWAP, EMA, Volume) ✔ Modular coding structure ✔ Knowledge of AlgoTest rule system ✔ Experience in intraday trading logic Deliverables : Separate Python files: Chartink stock fetch script Strategy logic documentation Editable conditions Setup guide Note : Payment will be released only after successful setup and testing on my live A...

...errors; restart should pick up open positions correctly. • Clear, timestamped logging to CSV or SQLite for fills, PnL and key signals. • Simple config file for API keys, instrument whitelist, risk caps per trade and per session. Deliverables 1. Python 3.x source with inline comments and docstrings. 2. Quick-start README explaining environment setup, required packages (requests, pandas, numpy, ccxt or native DeltaExchange SDK, whichever you prefer) and example configuration. 3. A short back-test notebook or script that demonstrates the momentum logic on historical exchange data so I can validate its edge before switching to live mode. 4. One short hand-off call or text walkthrough to ensure I can run, tweak and extend the bot on my VPS. If you have already ...

...need a careful sweep so every value left in the file genuinely reflects reality. My only objective is to boost overall data accuracy, and I want to follow a clear-cut, statistical approach: any record sitting outside a set number of standard deviations from the mean should be flagged and deleted outright—no replacement, no imputation. You’re free to work in Excel, Google Sheets, Python (pandas / NumPy), R, or any tool you trust, so long as the final files return in the same structure and encoding they arrived in. A concise log of how many rows were dropped per file will help me double-check the results. Deliverables • Cleaned CSV files, identical column order and headers • A brief summary (CSV or TXT) listing for each source file: total rows before, ro...

...demographics, maps behavioural segments, and highlights high-value cohorts. • Benchmarks our performance against key competitors and quantifies where we are winning or losing ground. • Detects wider market trends that could influence product positioning and future growth. I will share the CSV files plus any supporting market-research PDFs. Use the tools you are most comfortable with—Python (Pandas, NumPy, scikit-learn), R, SQL, Tableau or Power BI—so long as the code, queries, and dashboards are reproducible. Deliverables: 1. Cleaned, well-documented dataset. 2. Jupyter notebooks or R scripts with comments explaining each step. 3. Interactive dashboard for leadership to drill into findings. 4. Concise PDF/Slides report summarising key metrics, vi...

...Test-Time Compute approach outlined in Jeremy Berman’s posts ( and follow-up). My main goal is stronger generalization, not just memorized accuracy, so the solution must show consistent gains across the public and hidden splits. Everything will run in Python. You can pick whatever supporting libraries you like (PyTorch, JAX, NumPy, Hugging Face, etc.) as long as setup stays lightweight and the code is clearly documented. Core scope • Implement evolutionary prompt search: generate, mutate, rank, and select prompts on-the-fly against ARC tasks. • Automate evaluation: scoring script should mirror ARC’s official rubric so results are directly comparable to the leaderboard. • Track sample efficiency: log compute time, number