Python & Quantower Option Strategies

@vishal1145

Your strategy will fail if the adjustment logic doesn't account for IV skew shifts during volatile sessions - I've seen traders lose 30% of capital when their delta-neutral positions turned directional because they used naive Black-Scholes Greeks instead of real-time implied vol surfaces. Quick question - are you planning to use NSE's official API or a broker feed like Zerodha Kite? The data latency difference is 200ms vs 2 seconds, which matters when you're adjusting straddles intraday. Also, what's your target position size? If you're trading more than 50 lots per leg, you'll need smart order routing to avoid slippage on illiquid strikes. Here's the architectural approach: - PYTHON CORE ENGINE: Build a modular strategy class with pluggable exchange adapters (NSE, Binance, Bybit) using abstract base classes so you can swap brokers without touching Greeks calculation or adjustment logic. - REAL-TIME GREEKS: Implement vectorized option pricing using NumPy to calculate delta, gamma, vega across the entire chain in under 100ms, with live IV surface interpolation from ATM strikes. - QUANTOWER C# MODULE: Mirror the Python logic using Quantower's API with exposed parameters for strike selection, adjustment triggers, and time filters - I'll ensure both versions produce identical signals on the same data. - ADJUSTMENT ENGINE: Code rule-based logic for rolling strikes, adding hedges, or closing legs based on delta thresholds, P&L targets, and time decay - all configurable via JSON so you don't need to redeploy. - BACKTESTING FRAMEWORK: Build a vectorized backtest engine that replays tick data and simulates order fills with realistic slippage models calibrated to NSE F&O liquidity. I've built 4 production option algos for prop desks trading Indian markets, including a gamma scalping system that processed 200K option chain updates per day. I don't take on projects where the risk model is unclear - let's schedule a 20-minute call to walk through your adjustment rules and position sizing logic before I start coding.

@KarthikTechCon

Hi, I'm Karthik, a Python Developer with 15+ years of experience in algorithmic trading, financial APIs, and automation. I can build a modular Python + Quantower solution for non-directional option strategies (straddles/strangles) with real-time option chain data, Greeks, automated entries/exits, adjustment logic, broker/exchange integrations, trade logging, and a configurable Quantower strategy module. Why me? ✔ Python & trading automation expertise ✔ REST/WebSocket API integration ✔ Modular, production-ready architecture ✔ Clean code, documentation & demo support I can deliver a scalable solution with an initial prototype within your timeline. Best Regards, Karthik

@purealmaz

Regarding your project, I have a quick question: for the standalone Python back-tester, do you have preferred historical data providers, or should the initial design prioritize a generic adapter to accommodate various sources? I plan to approach this by using Python with `asyncio` and `pandas` to build a modular core engine. This will handle the real-time data, Greek calculations, and strategy logic, which can then be exposed via a C# wrapper for the Quantower module, ensuring logic remains consistent. I previously tackled a similar challenge in the "Online .NET Backtesting Prototype" project. There, I built an engine to ingest historical price data, execute trading logic from code snippets, and return key performance metrics like equity curves and win/loss ratios—concepts directly applicable here. Let's connect to discuss the architecture. Regards, Philip O.

@mobasherreza10

As an experienced software developer deeply skilled in Python, REST API, and Software Architecture, I believe I am the professional of choice for your option trading project. I have a vast knowledge of various financial markets including derivatives segments and crypto trading. My expertise in designing and deploying clean, readable code that adheres strictly to PEP-8 standards will ensure you receive a well-structured package that meets all your expectations. Modularity is key in any successful tech endeavor, and my extensive experience with APIs, microservices, and SaaS perfectly aligns with your need for code that can easily accommodate new exchanges or brokers without touching the core strategy engine. My specialization in automation and data analysis will certainly provide you with the flexibility to test individual legs separately and effectively fine-tune features like parameter tweaking strikes or thime filters.

@zyeloncomputech

Hi sir, Thank you for giving opportunity for biding... we have gone through your requirements and we can build your Python & Quantower Option Strategies according to your exact requirements. Why You Need To Go With Us? And What Special you get with us. • Your vision = Our mission • Your idea + Our expertise = Winner on Web • Cutting edge web technology & design • Innovative, Cost effective & Customized service • Pragmatic Approach • Constant communication with clients • Consistent performance • On-time delivery • Maintenance of global quality standards • Your online business + Our experience = Your success Python, Data Analytics, Data Science Portfolio IoT Data Analysis for Dairy Refrigerator Temperature Monitoring Real-time Object Detection using OpenCV and YOLO Supply Chain Management System Enterprise Data Warehouse Implementation Cloud Data Lake Migration Web Application Development for Wind Turbine Performance Prediction Data Analytics Platform for Supply Chain Optimization AI Bill System AI Try Dress

@RajV12

Hi, I've reviewed your project requirements carefully. The architecture and scope are clear, and I'm confident I can develop a modular solution with a Python trading engine, REST/WebSocket integrations, backtesting support, and a Quantower strategy module. Before starting, I'd like to clarify a few technical details, including the broker/exchange integrations, complete strategy logic (entry, adjustment, and exit rules), and the expected scope of the first prototype. This will help define realistic milestones and ensure the implementation matches your expectations. I've submitted my bid based on the current project description and would be happy to discuss the implementation plan in more detail. Looking forward to hearing from you.

@pradhumayg

Technical Clarifications & Core Architecture 1. Language Stack Reality: Quantower strategies are natively developed in C# (.NET), not Java. The architecture will feature a unified Python core engine for backtesting and API execution, paired with a clean C# (.NET) wrapper module for the Quantower live UI deployment. 2. Market Nuances: For the Indian segment (NSE), the engine will incorporate custom liquidity filters to handle wide bid-ask spreads on stock options, ensuring Greek calculations (Delta/Vega) do not skew entry/adjustment thresholds. 3. Modular Design: The engine will strictly decouple the Strategy Logic from the Exchange/Broker Adapters (REST/WebSocket). ### Implementation Roadmap * Milestone 1: Core Python Package & Data Pipeline (Days 1–4) - Real-time option chain ingestion (Crypto + NSE format). - Black-Scholes Greek calculation module. - Local logging architecture (CSV/SQLite). * Milestone 2: Strategy Engine & Execution Logic (Days 5–9) - Straddle/Strangle entry and multi-leg tracking. - Dynamic adjustment logic (Delta-neutral and premium-ratio triggers). - Independent leg management (toggle specific legs on/off). * Milestone 3: Quantower Integration & Handover (Days 10–14) - C# Quantower strategy module mirroring the Python engine. - UI parameter exposure (Strikes, Time filters, Slopes, Thresholds). - 10-minute video walkthrough and deployment documentation.

@ahmadraza001912

Hi, Straddle and strangle setups with full adjustment logic across both Indian F&O and crypto — that's a non-trivial scope, especially since you need both a plain-vanilla Python package and a Quantower custom strategy module running in parallel. The part worth thinking through early: keeping the core strategy engine truly broker-agnostic means the exchange adapters need clean interface contracts from day one, not bolted on later. I'd architect this as a pluggable adapter layer — CCXT for crypto pairs, and a separate NSE/BSE adapter for the Indian derivatives segment. - Real-time option chain data via WebSocket (Zerodha Kite or equivalent) - Greek-based risk metrics calculated in-process (py_vollib or mibian) - Entry, partial exit, and adjustment orders routed through the adapter layer - Trade logging to PostgreSQL with CSV export - Quantower module exposing strike, time filter, and adjustment threshold params in UI I work in Python daily, and the modular package structure you're describing maps cleanly onto what I build. One question: for the Indian derivatives segment, are you already using a broker with a WebSocket API (Zerodha, Upstox), or is broker selection still open?

@amana95

Hi, Clear brief, and the architecture you're describing is exactly how I'd build it. A single Python package, cleanly layered so the strategy engine stays independent of any exchange: core/ — straddle/strangle setup and adjustment logic, individual legs toggleable for testing. Broker-agnostic, the only place strategy rules live. adapters/ — one module per venue behind a shared interface, so adding an exchange or broker never touches the core engine. data/ — real-time option chains and Greek-based risk metrics (delta/gamma/theta/vega). execution/ — entry, partial exit, and adjustment orders from your parameters. logging/ — trades to CSV or SQLite for analysis. The plain Python build handles backtesting and live automation over REST/WebSocket. The Quantower module mirrors the same logic with strikes, time filters, and thresholds as editable UI fields. Same rules, two runtimes. Deliverables: modular package, Quantower module, PEP-8 code with sensible error handling, brief docs, and a short video of both running on demo data. I'd get an initial prototype to you within the week, starting with one Indian venue and one crypto pair to prove the flow before scaling out.

@amandite16

This proposal outlines the development of a production-ready, PEP-8 compliant algorithmic trading engine designed to execute non-directional option straddles and strangles with dynamic adjustment logic across both Indian F&O and crypto markets. The project will be developed via a dual-track approach: a modular Python package for backtesting and REST/WebSocket API automation, and a mirroring C# custom strategy module for live deployment in Quantower featuring UI-exposed parameters. By decoupling the core Greeks-calculation state machine from the execution layer, the architecture ensures you can toggle individual legs independently and seamlessly slot in new brokers or exchanges without altering the core strategy. A fully functional prototype featuring simulated data feeds, real-time risk metrics, and automated order triggers will be delivered within six days, followed by complete documentation and a video walkthrough demonstrating both environments operating on demo data.

@kunalk229

Hi, I can build both the Python framework and Quantower strategy module for your non-directional options trading system. My approach will be to create a modular architecture with a shared strategy engine, allowing the same straddle/strangle entry, adjustment, and exit logic to run across Indian F&O markets and crypto exchanges. The Python package will support real-time option chain ingestion, Greeks calculation, broker/exchange adapters, order execution, risk controls, trade logging (CSV/DB), and backtesting. The Quantower implementation will mirror the same logic with configurable UI parameters for strikes, time windows, adjustments, and risk thresholds. The code will follow PEP-8 standards, include robust error handling, and be designed for easy integration of additional brokers/exchanges without modifying core strategy logic. I can provide documentation, setup instructions, and a demo walkthrough. An initial working prototype can be delivered within one week for review.