Gold Options IV Algorithm

@Muhammadzeesha59

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@toriquldev123

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@besho87

Hello, I’d be excited to work on your implied volatility modeling project. I hold an MBA and have extensive experience in Python, quantitative analysis, Excel automation, financial modeling, and derivatives trading. I also have strong practical experience in futures and options trading, which helps me understand market structure and option pricing beyond the raw data. I can develop a fully automated solution that: * Imports and parses your Rithmic Excel files. * Identifies contract month, strike, option type (Call/Put), and market prices. * Calculates Implied Volatility using a Binomial Option Pricing Model, ignoring the inaccurate IV and Greeks supplied in the source data. * Generates smooth IV smiles, term structures, and a consolidated volatility surface using appropriate interpolation and smoothing techniques. * Produces reproducible, well-documented Python code with a simple one-command workflow and a clear README. I focus on building accurate, maintainable quantitative models using open-source libraries while ensuring the generated curves are suitable for comparison against CME reference data. I’m ready to review your sample files and start immediately. Best regards.

@prashanpatel557

Hello, Your project is a great match for my experience in Python, quantitative finance, options analytics, and trading system development. I’ve built tools for option pricing, backtesting, broker API integration, and financial data processing using Python, Pandas, NumPy, SciPy, and related quantitative libraries. I can develop a robust, fully documented solution that ingests your Rithmic Excel exports, identifies contract metadata, computes Implied Volatility using a Binomial pricing model, and generates smooth IV smiles, term structures, and an IV surface. The implementation will include data validation, interpolation, arbitrage checks, reproducible outputs, and a single-command execution workflow using open-source libraries only. Before we begin, I’d like to confirm one detail: Should the calibration target replicate the CME methodology as closely as possible, or is matching the published ATM IV and producing a smooth, arbitrage-free surface within your stated tolerance the primary objective? This will guide the choice of smoothing and interpolation techniques. I’m ready to start immediately and can deliver clean, maintainable code, comprehensive documentation, and a validation report comparing the generated curves against your reference data to ensure the solution is accurate and easy to extend.

@MZITSERVICES

Hi there, I am A.R.M. MASUD with a strong background in Data Science.I am an experienced Machine Learning developer with expertise in designing, training, and deploying intelligent models that deliver real-world value. My background includes supervised and unsupervised learning, deep learning with TensorFlow and PyTorch, and data preprocessing using Pandas, NumPy, and Scikit-learn. I specialize in developing classification, regression, clustering, and predictive models, as well as computer vision and NLP solutions. I follow best practices in feature engineering, hyperparameter tuning, and model evaluation to ensure high accuracy and scalability. My focus is on building end-to-end ML pipelines that are efficient, reliable, and tailored to your project’s requirements to maximize impact. https://www.freelancer.com/u/MZITSERVICES I appreciate the opportunity to submit this proposal and am excited about the possibility of working with you to bring your project to life. Thanks A.R.M MASUD

@albertc1996

Hi there, hope you’re doing well. Could you specify whether you prefer using a CRR or Leisen Reimer binomial tree implementation for the IV solver, and what risk free rate assumption or yield curve format you'd like aligned with the CME baseline? I can build a clean, end to end Python script using standard quantitative tools to parse your rithmic Excel files, solve for binomial IV across all strikes and maturities, apply cubic spline interpolation for continuous term structures, and output no-arbitrage IV curves matching CME tolerances. I will also provide clear documentation in the README for running the pipeline on new exports. I’ve worked extensively with Python algorithms, data processing, and financial calculations before and would be glad to help you complete this project efficiently. Let’s discuss the details.

@vikrant12112

Hi there, Your Rithmic gold options exports need a reliable IV engine that ignores incorrect greeks and produces CME-like smooth curves. I have spent the last 4 years solving exactly this type of problem: cleaning financial workbooks, extracting contract metadata, applying derivatives models, and validating outputs. I will parse the Excel sheets, identify month/strike/call-put/price, implement a binomial implied-vol solver, apply Data Analysis checks for arbitrage consistency, structure the Financial Analysis assumptions clearly, and generate Data Visualization outputs for maturity curves plus a consolidated surface with a reproducible README. Best regards, VIKRANAT

@hayat38402

Hi, how are you doing? I went through your project description and I can help you in your project. your project requirements perfectly match my expertise. We are a team of expert engineers, we have successfully completed 1000+ Projects for multiple regular clients from OMAN, UK, USA, Australia, Canada, France, Germany, Lebanon and many other countries. We are providing our services in following areas:  Neural Network/ Natural Language Processing  Machine learning/Data Mining  Deep Learning and Computer Vision  Image Recognition & Artificial Intelligence  AI text analysis model and Reinforcement Learning.  Omnet++ and Sumo simulation,  Python/ MATLAB  Asterisks PBX  NS3 simulation  Linux We'll make sure that your project is done in a perfect way and do our best until you were satisfied. I am confident I can provide you with top-notch materials that will fit your needs.

@codeandcommand

I can pull the Rithmic sheets straight into pandas, parse strikes and IV per expiry, and build the algorithm on top with numpy and scipy for the stats work. A quick matplotlib or Plotly chart would let you sanity check the IV curve before we go further. I can start today and have a first working pass by Wednesday. The budget and timeline here are starting points based on the post, we will firm them up once I see a sample workbook. Want me to send a quick scope doc?

@Abuzar00

As a seasoned developer with over 5 years in the field, I can assure you that your project is in good hands with me. I not only understand the depths of derivatives math but have also wielded it masterfully across numerous projects, including some in the fintech sector. My prowess has been applied to building accurate and efficient proprietary systems just like the one you need for this project. Having completed several projects across different locations, time zones, and financial market requirements (including Forex trading algorithms), I've securely established myself as a reliable expert in data manipulation and algorithmic modeling. I am more than comfortable using Python for this project, recognizing its ability to function well with open-source libraries. Importantly, readability and reusability are key elements of good software development practice that I focus on when writing scripts. This means that with this project, you get clear code that can be understood -even by coders other than me- at first glance, without repetitions or redundant components. My portfolio attests to this; no ghost deliveries or excuses ever.

@azwa04

Turning raw Rithmic option data into a clean implied volatility term structure is exactly the sort of quantitative workflow that needs to be correct from the pricing model upward, not just visually close to the CME curves. I'd build a reproducible pipeline that parses your Excel exports, identifies contract month, strike, option type and market prices, then backs out implied volatility using a Binomial pricing model before smoothing each maturity into a continuous IV curve and generating the full volatility surface across expiries. I'll validate the output against recent CME ATM term structures, check for common arbitrage issues such as strike monotonicity and convexity, and deliver a single-command solution with clean, documented code so you can run it against future Rithmic exports without modification. I can start right away.

@melopgabriel

Hi, your goal is clear: turn noisy Rithmic gold-options workbooks into a reliable IV term structure that behaves like CME’s curve. I’ve worked with option chains, implied volatility extraction, and curve smoothing in Python using open-source tools. For this, I’d first parse each workbook, identify the contract month, strike, call/put, and last price, then back out IV with a Binomial model and clean the results maturity by maturity. Next I’d smooth and interpolate the data into a continuous expiry-by-expiry surface, while checking for basic arbitrage issues such as monotonicity and convexity. I’d also keep the script reproducible, so you can run it on fresh Rithmic exports with one command. I can deliver the code and a short README that explains the assumptions and inputs clearly. If that sounds aligned, I’d be glad to get started. Best regards, Gabriel

@vzaretsky

Gold futures options on CME are American-style, so the binomial requirement in your brief makes sense — a straight Black-Scholes inversion would misprice the early-exercise premium, especially on puts, which is likely part of why the raw IV/greeks columns in your Rithmic export come out wrong. I'd write a Python script using pandas/openpyxl to parse each sheet into contract month, strike, call/put flag and last traded price, run a Cox-Ross-Rubinstein binomial tree with a bisection or Brent root-find to back out IV per strike, then fit an arbitrage-checked spline (monotone, convex where expected) per expiration and stitch those into the consolidated surface, exporting both CSV and plotted curves against the CME reference. One detail that changes the implementation: what futures price and risk-free rate should feed the tree for each maturity — is that available elsewhere in the Rithmic export, or should the script source it separately? My profile carries a 4.9/5 rating over 12 reviews with 98% on-time and 98% on-budget delivery. Confirm the rate/price source and I can start right away.

@miguelcen64

Hi, This is a strong fit. I can build an end-to-end script that parses your Rithmic Excel workbook, extracts contract month/expiry, strike, option type, and last price, then backs out IV per strike using a binomial model and produces smoothed maturity-level curves plus a consolidated surface. I’m comfortable with derivatives math, data cleaning, and no-arbitrage validation. I’d structure this to run in one command with open-source libraries only, and include a concise README covering assumptions, inputs, and reruns on fresh downloads. I can also validate the output against CME ATM term structure across multiple recent sessions and flag any data-quality edge cases from the source workbook. The attached GC diagnostics file is helpful for designing a robust parser from the start. Best, Miguel

@sayarsinghseo

The task you've outlined calls for someone with not just expertise in data manipulation, but also solid knowledge of derivatives maths - which I bring to the table. With my degree in mathematics and professional background as a data scientist, I've acquired a firm grasp of the quantitative concepts necessary to effectively deal with option pricing and implied volatility models. My proficiency in Python programming will prove invaluable in designing and implementing the algorithm you need, while my extensive experience in data processing and visualization will ensure that the final output is clean, accurate, and efficient. In addition to these technical skills, I'm also a strategic thinker with a proven track record of delivering measurable outcomes. This means that I'm not merely capable of coding up the IV curve extraction algorithm, but also inclined towards creating valuable context around the process. I intend to provide you with a comprehensive README that outlines all the input assumptions at play and holds your hand through replicating our workflow on future Rithmic downloads for greater autonomy. Ultimately, choosing me means having a professional who won't just complete the project to your satisfaction but provide you with an optimized approach leveraging open-source libraries. So let's find value in your raw data by turning them into actionable insights and optimized strategies that drive results - together!

@mikes126

Hi there ! This is the kind of quantitative problem I enjoy working on. I'll build a reliable Python script that reads your Rithmic files and calculates implied volatility using a Binomial model. The output will include smooth IV curves, a complete volatility surface, and documented assumptions. Everything will run with one command and stay easy to reuse with new data. Budget : $130 Timeline : 2 days

@Abror86

Hi — Abror-Yakubov here from Uzbekistan, "GOLD OPTIONS IV CURVE ALGORITHM" — you need reliable volatility curves from raw option quotes. I’ll build a Python workflow that reads the Rithmic Excel files, identifies contracts, strikes, expirations and prices, then calculates implied volatility with a binomial model and creates smooth IV curves/surfaces. I’ll focus on the difficult parts: cleaning incorrect IV/Greeks, handling sparse strikes, and validating the output against market behavior with reproducible code and a clear README. Do you already have the CME IV curve files for the comparison step, or should I prepare the validation workflow from another reference source? Looking forward to working with you.

@arturg6

Hello, "Binomial IV Extraction Script" - turn raw option data into smooth IV curves. I will use Python with pandas, NumPy and SciPy to read the Excel sheets, parse contract month, strike and call/put flags, then apply a binomial model to back‑out the implied volatility. I built a similar Excel data‑processing pipeline for lead extraction here: https://www.freelancer.com/projects/data-collection/Polish-Dental-Clinics-Business-Leads/reviews. I will also add arbitrage checks to keep the curve monotonic and convex, and use spline interpolation for a continuous surface. Do the files always follow the same column order and should the final output be CSV or Excel? Looking forward to working with you. Artur Giżycki

@hetali5

Hello, We understood your requirement properly and if you have no issues will perform sample work for you. Your success is our business, Let's discuss. "All The Best"