Advanced SL/TP Optimisation in Python

@a4logic

Hello, As an alternative to our profile, I, Yasir, will bring my Python skillsonto the table. First off, I commend your decision to focus on the imperfections of your current system, such as slippage and partial fills. My team has specialized in web services and separated ourselves by ensuring the strong integration of multiple facets of a project. Throughout your project, I'll apply this attribute to provide you with a robust module that not only incorporate realistic slippage/fill distributions but also stand up to your expectations. Undoubtedly, my expertise extends to optimizing financial algorithms - much like the task at hand. I'll take full advantage of Pandas, Numpy or any other vectorised libraries appropriate for this job. Working in pure Python is no trouble either since we know how to manage applications using multiple coding languages including yours. Lastly, let me assure you that your satisfaction and ongoing business relationship are my top priorities. By committing to clear inline comments throughout the code and providing you with a concise yet instructional notebook or script for calling the optimiser with your live data feed, I aim to ensure that no query goes unanswered throughout this project and you have all the necessary resources ready for live execution later on. Thanks!

@dfordesigners

Hi there, I have extensive experience in Python programming and algorithm optimization. I understand the importance of realistic slippage and fill distributions in trading strategies. I can develop a robust Python module for your existing codebase that estimates and incorporates these factors, back-tests various stop-loss and take-profit distances, and identifies the optimal risk-reward ratio. My expertise in pandas, numpy, and other vectorized libraries will ensure efficient and flexible logic that can seamlessly integrate with live execution. I will provide clear inline comments and a demonstration script showcasing how to utilize the optimizer with your live data feed. Let's collaborate to enhance your trading strategy effectively.

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Robust Trading Module for Real-Time Market Data ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for a Python trading module. You don't need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for trading systems. I'll create a module that accurately estimates slippage and fill distributions, allowing for effective back-testing and optimization of stop-loss and take-profit distances. ➡️ Why Me? I can easily build your trading module as I have 5 years of experience in Python development, focusing on financial applications, data analysis, and back-testing strategies. My expertise includes using libraries like Pandas and NumPy for data manipulation and analysis. Not only this, but I also have a strong grip on API integration and real-time data processing, ensuring a smooth and effective implementation for your needs. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I'm looking forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Python Programming ✅ Data Analysis ✅ API Integration ✅ Back-Testing Strategies ✅ Slippage Estimation ✅ Risk-Reward Calculation ✅ NumPy Library ✅ Pandas Library ✅ Live Data Processing ✅ Financial Modeling ✅ Error Handling ✅ Code Optimization Waiting for your response! Best Regards, Zohaib

@seefattechnologi

Experienced Python Quant Developer ----------------specializing in trading automation, slippage modeling, and strategy optimization. I can build a plug-and-play module to optimize Stop Loss, Take Profit, and Risk-Reward settings using realistic execution assumptions from your live market data. Please ping me to discuss further and deliver exceptional results. Thanks!!!

@DeveloperStation

Hi, I’ve developed multiple trading solutions that use real-time data from APIs like Tradier and Alpaca, and I’ve worked extensively with libraries like Backtrader and Zipline for backtesting. I understand the importance of optimizing stop-loss and take-profit levels to account for slippage and partial fills, and I can build a dedicated module that integrates seamlessly with your existing code. In addition to optimizing SL/TP levels, I can enhance your strategy with features like dynamic position sizing based on volatility or ATR, which can significantly improve overall performance. Let’s schedule a quick 10-minute call to discuss your project in more detail and ensure I fully understand your requirements. I’m ready to start immediately and can dedicate 15–30 hours per week to your project. Best regards, Adil

@Friends4it

Hi, I've read your spec — you have a Python strategy on real-time data via the TopstepX Tradovate API, with basic stops/targets that ignore slippage, partial fills and intrabar volatility. You need a robust module, callable from your existing codebase, that: estimates and incorporates realistic slippage/fill distributions from live order-book snapshots; back- and forward-tests a range of SL/TP distances under those assumptions; and surfaces the optimal Stop Loss, Take Profit and Risk-Reward Ratio in ticks. This is squarely the kind of quantitative Python tooling we build at Global IT Vision. My approach: - A clean, importable module (no rewrite of your strategy) with a simple API your code calls. - Slippage/fill modelling from L1/L2 order-book snapshots — empirical fill distributions rather than a naive fixed-tick assumption, accounting for partial fills. - A vectorised back-tester + forward-test harness to sweep SL/TP grids under those fill assumptions, reporting expectancy, hit-rate and drawdown per combination. - Optimiser that returns the best SL/TP/RRR in ticks (and currency), with the trade-off surface so you see why, not just a single number. - Tests + clear docs so results are reproducible and you can trust them live. I've posted scope questions on the board. Happy to discuss your data granularity on a quick call and confirm the approach. — Muhammad Idrees / Global IT Vision Pvt. Ltd

@skalogir

Hi there, I understand you need a pure Python risk optimiser for your TopstepX Tradovate strategy that models slippage, partial fills, order-book-based fill distributions, intrabar volatility, and tests stop-loss/take-profit combinations in ticks and currency. I have strong experience with Python trading systems, pandas/numpy backtesting logic, real-time market data handling, order book analysis, execution-risk modelling, and modular code designed for later live integration. I would build a callable optimiser module with clean interfaces, vectorised simulations, configurable tick/currency rules, forward-test hooks, inline comments, and a short notebook/script showing how to run it against your live feed. Q1: Which instrument(s) should the optimiser support first, and what tick size/tick value should be used? Q2: Do you already store order book snapshots historically, or should the module log them for future slippage calibration? Q3: Should optimisation prioritize max expectancy, win rate, drawdown control, Sharpe-like score, or a custom risk metric? Best regards.

@stelianrosca618

Hi, Good problem to work on — the gap between backtested SL/TP performance and what actually happens live is almost always slippage and fill assumptions, so tackling it properly makes a real difference. I'd build this as a standalone module in numpy/pandas that estimates fill distributions from your order book snapshots and runs the optimisation against realistic conditions rather than clean fills. Callable from your existing code, nothing needs restructuring. I'd keep the slippage model and the optimiser separate so they're easy to test and swap out independently. One thing I need to know before I scope it properly — are you getting full order book depth from the Tradovate stream or just best bid/ask? Stelian

@devendrathakur12

Hello, I built many large scale Python quantitative trading and strategy optimization systems similar before and I would love if I get the chance to work on your project. I can build a reusable module using Python, pandas, NumPy, and vectorized backtesting that models slippage, partial fills, and intrabar behavior while optimizing Stop Loss, Take Profit, and Risk Reward ratios in ticks and currency. The code will stay easy to integrate with your existing TopstepX Tradovate workflow. One question, do you already archive Level 2 order book snapshots or should the optimizer estimate slippage from trade and quote history alone? Can we connect over a chat to discuss more about the project? Best regards, Dev Singh

@toriquldev123

Hello dear! I’m Md Toriqul Islam, an experienced Python developer with 10+ years in algorithmic trading, quantitative analysis, backtesting systems, API integrations, and performance optimization for financial applications. I understand you need a Python module that models realistic slippage and fill behavior from Tradovate/TopstepX order book data, backtests and forward-tests Stop Loss and Take Profit combinations, and identifies the optimal risk-reward configuration in ticks and currency for live deployment. My skills include Python, Pandas, NumPy, trading systems, market data analysis, API integration, statistical modeling, and optimization. I’m ready to start immediately and deliver a well-documented, reusable module with demonstration scripts and clear integration guidance. Best regards, Md Toriqul Islam

@sdqtrades

As an experienced developer specializing in Python, I am well-equipped to tackle the advanced SL/TP optimization you require. My fluency in pandas and numpy, libraries often used for this type of optimization, combined with my broad repertoire of trading bots across various platforms like Binance and Coinbase make me an ideal candidate. I've honed my abilities in financial analysis and algorithm development through years of practice within geopolitical and financial consulting firms. I understand the intricate nature of trading, especially how real-time data feeds play a crucial role. That's why I pledge to approach your project with a great sense of adaptability and deliver a flexible solution that can be easily integrated into your existing codebase without any disruption. With my automation skills, fast-paced market conditions such as slippage, partial fills, and intrabar volatility will not be simply considered but incorporated in realistic estimations. Furthermore, I assure you strict confidentiality throughout the process, safeguarding your intellectual property rights with complete transparency from start to finish. My ability to communicate fluently in multiple languages also ensures that we can converse comfortably in whichever language you are most comfortable in. Choosing me means choosing a diligent professional who will provide fast turnaround times while maintaining clean, optimized codes to drive the profitability and success of your trading strategy.

@krupnovdenis

Hello! We can build a Python optimisation module for your current trading strategy tasks. 1. Do you already have historical order book snapshots and execution results for slippage modelling? 2. Which metrics should define the optimal SL/TP setup first: net PnL, drawdown, win rate, or risk-reward? — About us We are dZENcode – a full-cycle IT company for digital product development: from design and programming to integrations and post-release support. We build projects from scratch and also work on existing solutions that need further development, improvements, or technical support. You can find detailed information about our services and rates on our official website: https://dzencode.com. Please review it – after that, we can discuss the details and agree on the next step. ⚠️ After clarifying all details, we will define the scope, the suitable cooperation format – task-based, outsourcing, or outstaffing – and the final cost. Projects are guaranteed to reach release with us: • 10+ years providing IT services; • 90+ in-house specialists; • 250+ public reviews since 2015; • We support products under SLA after launch; • We work under NDA and a company contract!

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@ayazakhtar75

Hello Client, This is completely possible, as I have extensive experience in Python, pandas, numpy, real-time market data, Tradovate API, order book analysis, slippage modeling, partial fills, intrabar volatility, stop-loss/take-profit optimization, risk-reward ratio calculations, back-testing, forward-testing, and live execution integration. I have created numerous automated processes without any problem. I am confident that I can help you build a robust module that estimates realistic slippage/fill distributions, tests stop/target distances, and surfaces optimal combinations in ticks and currency — callable from your existing Python codebase with clear documentation. Would you mind sharing more details about the integration and the gestionale you are using? Thank you, Ayaz Akhtar

@Cumuc

Hello, Nice to meet you. I have read your post carefully and I think it is a good fit for me. I am ready to help you on an immediate basis. To deliver this, I will develop a Python module that analyzes live order book snapshots to statistically model slippage and fill distributions, integrating these into a flexible backtest and forward-test framework for stop-loss and take-profit strategies. I will ensure the code is modular, well-commented, and easily callable from your existing codebase, with a demonstration script showing how to feed live data and retrieve optimal risk parameters. Your satisfaction with the project is my top priority! So, I will complete your project on time and within your budget. I'm waiting for your kind response. I will do my best for you. Cuzmuc

@OlektraGroup

Hi, You’re dealing with a strategy that currently ignores market micro-structure, leading to suboptimal exits due to slippage, partial fills, and noisy intrabar volatility. You need a modular, data-driven optimization layer to refine your Tradovate-based SL/TP logic. I recently built a custom backtesting engine for a high-frequency pattern recognition system that utilized historical order book snapshots to simulate fill probability distributions, effectively reducing slippage variance by 18%. I’ll implement a vectorized Monte Carlo simulation to stress-test your risk-reward ratios against historical liquidity snapshots, ensuring the logic remains plug-and-play for your existing execution loop. I’ll deliver this as a clean, commented module with a validation notebook. To ensure the simulation aligns with your current latency constraints, what is the average tick-frequency or volume profile of the instruments you’re trading?

@slavkom1

Hi, The SL/TP optimiser is the main piece here, especially modelling slippage and partial fills from live order book data. Intrabar volatility can make simple fixed stops look better than they really are. I’ve built Python trading and backtesting tools using pandas, numpy, live feeds, and execution-style assumptions. I’d keep it as a clean module your current strategy can call: - parse TopstepX/Tradovate snapshots into fill and slippage distributions - test SL/TP ranges in ticks and currency - compare risk-reward results with realistic fill assumptions - return the best combinations in a simple table or dict - add inline comments and a short notebook/script showing the call flow I can start by matching your existing data format, then make the optimiser flexible enough for live execution later. Would it be most helpful to first align on the exact structure of your live order book snapshots, so the slippage and partial-fill model matches what TopstepX/Tradovate is actually giving you? Greetings, Slavko

@emilesd617

Hi, I am a Python trading software developer with 8 years of rich experience. I am familiar with Python, pandas, numpy, Backtesting, and Data Analysis. For this project, the most important part is making the SL/TP logic realistic by including slippage, partial fills, and intrabar volatility. I can build a callable Python optimiser that tests stop-loss and take-profit ranges, calculates risk-reward in ticks and currency, and provides a clear example script for using it with your live data feed. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@codefusion53

Hi I will deliver a modular, pure Python SL/TP optimiser that plugs into your existing real-time strategy codebase and can be called from your live loop. It ingests live data from the TopstepX Tradovate API and order book snapshots to estimate realistic slippage and fill distributions, then backtests and forward-tests a grid of stop loss and take profit distances under those assumptions. The output surfaces the optimal Stop Loss, Take Profit, and overall Risk-Reward Ratio expressed in both ticks and currency, with instrument and tick value mapped so it can be used in live risk controls. The core is vectorized with pandas and numpy, with no heavy backtesting framework required. The API will be small and intuitive: feed price series and distribution data, specify the instrument, and receive an optimized SL/TP and a metrics summary. Inline comments will explain logic and decisions. I will deliver a short notebook or script showing how to call the optimiser with your live feed and wire it into your execution loop. The module is designed to be drop-in and easy to unit test before live use. Best, Justin