Mid-Cap Surge Date Analysis

@prashanpatel557

hello, I can conduct a rigorous, reproducible seasonality study on NSE/BSE mid-cap stocks using 10–15 years of daily price and volume data. Approach: • Pull historical data (NSE bhavcopy / yfinance / BSE archives). • Clean and align trading days (handle holidays, leap years). • Compute daily % returns. • Define a configurable “meaningful surge” threshold (e.g., >2% or percentile-based). • Aggregate by calendar date (dd-mm only). • Calculate: – Avg % Move – Hit Rate (% years above threshold) – Sample Size This produces a probability matrix of surge days per stock. Deliverables: • Excel/CSV (clean, filter-ready, no macros) • Python script (pandas + NumPy, well-commented) • Fully reproducible workflow using public data sources All results will reconcile directly with the provided code output. Timeline: 5–7 days depending on basket size. Ready to begin once you confirm the mid-cap list and surge threshold preference.

@nareshk44

With a solid background in data analysis, I'm confident that I'm the ideal candidate to undertake your project. I have a wealth of experience with statistical analysis and data processing utilizing tools such as Excel and Python Pandas, which are prime for unearthing trends from massive datasets like the one you've described. I understand clearly the significance of upholding the highest level of integrity and ensuring reproducibility when drafting scripts like the one you've requested. As such, each step of my analysis will be meticulously documented to produce clean, readable code that aligns perfectly with your deliverable expectations. My commitment to providing fast, accurate and timely services will ensure you don't just get your work completed but also get it well within timeframes. Together with my proficiency in other Microsoft Office tools like Word and PowerPoint, I can furnish visually appealing yet professional reports that complement your data. In conclusion, I guarantee my work meets or exceeds the accepted standards for statistical reporting in regards to accuracy; this is crucial in data analysis since even minor errors can lead to major misinterpretations.

@Sahyadri96

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 I can deliver any project in Trading. Readymade setups for Python available

@haitham1996

Hi! Imagine knowing exactly which dates mid-cap stocks have historically surged — turning intuition into a data-backed edge with clear probabilities for every trading day of the year.I’ll pull 10–15 years of NSE/BSE data (yfinance + official bhavcopy archives), clean it, run a robust date-based seasonality scan (no event leakage), and deliver:A clean, filter-ready Excel/CSV with Symbol | Date (dd-mm) | Avg % Move | Hit Rate | Sample Size + extra metrics Fully commented Python script (Pandas + NumPy) so you can rerun or expand it anytime Timeline & Price Complete analysis + files in 1–2 days (serious, fair rate for high-precision quant work).Let’s turn those calendar patterns into actionable insight — share your preferred mid-cap basket and threshold (e.g., +2%) now and I’ll start today!

@Hemantt841

I understand you require a thorough analysis of mid-cap stocks on NSE/BSE to verify if certain calendar dates consistently show meaningful price surges over the last 10 to 15 years. You want a clean, date-focused seasonality study without event-based filters, culminating in a detailed, filter-friendly spreadsheet with metrics like average percentage move and hit rate. With over 15 years of experience and more than 200 projects completed, I specialize in Python-driven data analysis and statistical processing, especially using Pandas and NumPy for time series and financial data. I am well-versed in pulling and cleaning historical stock data from public sources like yfinance and NSE/BSE archives, ensuring reproducible results with well-documented scripts. For your project, I will assemble a representative basket of Indian mid-cap stocks, extract daily price and volume data spanning at least ten years, and run a date-centric analysis to compute surge probabilities. The deliverable will be a macro-free Excel spreadsheet with all required metrics, accompanied by a commented Python script so you can rerun or extend the study. This will be completed within a realistic timeframe of about two weeks. Feel free to reach out if you want to discuss the approach or any specific stock selection ideas.

@mishrac2

I’ve built similar seasonality and probability studies using Zerodha Kite historical data APIs, where I analyzed NSE stocks across multiple years to detect recurring date-based return patterns. Your intuition about mid-cap counters moving around similar calendar dates is absolutely testable — and I can validate it with a clean, reproducible statistical framework. This will not be a superficial backtest. It will be a structured, date-centric probability analysis built for repeatability and extension. ? How I Will Approach the Study

@Suny9971000

I can do your work in the same way. I have experience in your work for 2 years. I also have a diploma in this field which is also verified.

@CodeFixerPro

Hi, this is a well-defined and interesting seasonality analysis project. I can pull 10–15 years of daily OHLCV data (NSE/BSE or via yfinance where applicable), clean and normalize the dataset, and run a date-centric seasonality scan as described. The output will include: – A clean Excel/CSV file with Stock Symbol, Calendar Date (dd-mm), Avg % Move, Hit Rate, Sample Size, and additional supporting metrics – A fully commented Python script (Pandas/NumPy) so results are reproducible and extendable I suggest working with a clearly defined representative basket of mid-cap stocks to keep the study statistically robust and computationally clean. Delivery time: 5 days. Let me know if you already have a preferred stock list or threshold definition for “meaningful move.” Best regards, Tristan

@PyMLExpert1

Having spent many years working on similar projects, including an analysis of NSE and BSE stocks using historical data, my skills perfectly align with your project needs. I've extensive experience in leveraging Python to clean, analyze, and extract insights from financial data sets. With your explicit request for a well-commented script, I can ensure your study remains reproducible and easily extendable. Additionally, my proficiency in Pandas, NumPy, and matplotlib/seaborn aligns perfectly with your preference for these tools. These strongholds will be instrumental in conducting the required data cleaning process as well as presenting the insights in a visually-pleasing yet easy-to-filter spreadsheet. As a module-centric data analyst, timely delivery is my watchword. You can expect great attention to detail in data extraction as well as an analysis that goes beyond your specifications to identify any patterns or irregularities that might enhance the trustworthiness and scope of your study. Given the significance of this task, I'm ready to allocate all available resources to make sure you receive nothing but the most valuable insights for your investment decisions.

@ssldevmaster

Hi, I can easily DO your work IN 24 HOURS, DM me now to get started, PRICE NEGOTIABLE 100% Work satisfaction is provided

@EmperorHabeeb

I am a Data Analyst experienced in data processing, Python, and R for statistical analysis. I can assist you in confirming or debunking the seasonality of mid-cap stocks. How I can assist: Data Extraction: Gather 10–15 years of daily price and volume data for the specified NSE/BSE mid-cap stocks. Data Cleaning: Clean and structure the data to focus on dates while excluding earnings and dividend events. Seasonality Analysis: Using Python (Pandas, NumPy), I will perform a seasonality scan to identify trends and create a probability table for “surge days” showing how often stocks exceeded a specific margin on certain dates. Deliverables: A clean Excel/CSV file containing Stock Symbol, Calendar Date, Avg % Move, Hit Rate, and additional metrics. A well-commented Python/R script for replicating or extending the analysis. Skills: Data Processing & Cleaning: Ensuring accurate, structured data. Statistical Analysis: Using Python (Pandas, NumPy, Matplotlib) to perform time series and seasonality analysis. Excel/CSV Output: Clean, organized results with no macros. I’ll ensure reproducibility using publicly available data like NSE Bhavcopy, BSE archives, and Quandl. Looking forward to working with you!

@connectsurendram

Hi I have read your requirement and I am confident I can help you quantify the seasonality of Indian mid-cap stocks. Please message me so that we can have a detailed discussion. I have 8+ years of experience in Data Analysis and Software Development, with expertise in Python, Pandas, NumPy, R (tidyverse), and visualization tools like matplotlib and seaborn. I can pull 10–15 years of historical daily NSE/BSE data, clean it, and generate a date-centric seasonality analysis. The deliverables will include: Excel/CSV showing Stock Symbol, Calendar Date, Avg % Move, Hit Rate, Sample Size, and additional insightful metrics. Well-commented Python/R script for reproducibility and extension. Looking forward to connecting on chat for further discussion. Thanks and Regards, Surendra Bheema Infotech Pvt Ltd, Indore (M.P)

@mpumelelon28

Hello, I would love to help you with this task. I am detail-oriented and comfortable with typing and data entry. I can start immediately and deliver accurate work on time. Looking forward to working with you.

@sdivrbt99

I specialize in quantitative analysis, financial data engineering, and statistical validation. I don’t just compute averages — I stress-test signals for stability, sample bias, survivorship bias, and overfitting risk. I am strong in: Pandas / NumPy for market data manipulation(had done a project in telecom churn prediction) Statistical robustness checks Reproducible research workflows Clean,simple, institutional-grade Excel outputs Most importantly, I will focus on separating pattern from noise. Many seasonal signals will disappear under proper statistical testing — I will quantify whether your “same calendar date jumps” intuition is real alpha, clustering illusion, or liquidity artifact. If the seasonality exists, we’ll prove it numerically. If it doesn’t, we’ll debunk it scientifically.

@maarisaay

Hello! This project caught my eye because I specialize in data analysis and I am very interested in market seasonality. I am currently looking for my first project on Freelancer to build my profile, so I am offering to complete this study for a low price in exchange for a positive review. I will use Python (Pandas and NumPy) to pull 15 years of historical data via yfinance/NSE archives and calculate the "hit rates" and average moves for your mid-cap basket. I stay updated on the latest financial data trends on Instagram to ensure my spreadsheets are not just accurate, but also clean and easy to filter.

@DavidGsSilva

Drawing on my strong Python and database skills, I am an ideal fit for your Mid-Cap Surge Data Analysis project. I have experience working with large datasets for over a decade, and can comfortably manage the vast amounts of data required for your task. My expertise in Python, particularly in Pandas and NumPy libraries, makes me the perfect candidate to clean and process the 10-15 year daily price and volume data you need. Moreover, I have an extensive history of working with SQL databases like MySQL, PostgreSQL which aligns effectively with your requirements to deliver neat and accurate results. I understand the significance of data integrity and cleanliness, and will ensure that all your files are delivered error-free. I don’t stop there; in addition to providing an insightful spreadsheet with all the requested metrics, my entrepreneurial spirit compels me to include a well-commented Python script. In conclusion, choosing me means gaining access to a steadfast professional who is not only technically skilled but highly committed to ensuring that every project meets and exceeds expectations. Let's collaborate to generate a calendar of "surge days" for mid-caps that will not only confirm or debunk your intuition but provide a clear probability table for future investments.

@aarushv13

Hello, This is a very interesting hypothesis, and it’s absolutely testable with a clean, reproducible framework. I can pull 10–15 years of daily OHLCV data for a representative basket of NSE/BSE mid-cap stocks using publicly available sources (NSE Bhavcopy, BSE archives, yfinance, etc.), clean and align the data, and run a date-centric seasonality analysis to quantify “surge day” probabilities. My approach: Compile and validate ≥10 full years of daily price and volume data per stock Compute daily returns and aggregate by calendar date (dd-mm) Calculate Avg % Move, Hit Rate (based on defined surge threshold), Sample Size, and supporting metrics Generate a clean, filter-friendly Excel output with no macros Deliver a fully commented Python script (Pandas/NumPy) so results are reproducible and extendable I will also perform basic sanity checks (data continuity, holiday alignment, split adjustments where applicable) to ensure the probability table reflects real trading behavior and not artifacts. To define “meaningful surge” correctly, would you prefer a fixed threshold (e.g., >2% daily return), or should I determine a stock-specific percentile-based threshold for consistency across volatility profiles? I can deliver the complete spreadsheet and well-documented code within 4–5 working days. Looking forward to working on this analysis. Regards, Aarush

@Choudharysubh07

Hi there, How I will approach this: Data Sourcing: I will use yfinance or NSE Bhavcopy to pull the required daily price and volume data for a representative mid-cap basket. Processing: Using Pandas and NumPy, I will clean the data to focus strictly on calendar dates (dd-mm) and calculate the daily percentage moves. Seasonality Scan: I will build a probability table that calculates the Hit Rate (how often a move exceeded the threshold) and the Average % Move for every single trading date across your 15-year sample. Quality Control: I use Pylint to ensure my scripts are clean, well-commented, and easy for you to rerun or extend in the future. Deliverables: Clean CSV/Excel: A filter-friendly spreadsheet with Stock Symbol, Date, Avg % Move, Hit Rate, and Sample Size. Python Script: A well-documented .py or .ipynb file using Pandas/Matplotlib for the analysis and sanity plots. I have experience building secure, data-centric tools in Python—including a recent cryptography project that required high attention to data accuracy and confidentiality. I can ensure your financial data is handled with the same level of technical rigor. I can get started on this immediately and have the first draft of the seasonality table ready for you to review within 2-3 days.