Python Quant Developer for Crypto Futures

@tnt42010

Hi, This fits very well with my experience in quantitative research and trading-system development. I can build a robust Python-based backtesting and research engine for BTC/ETH perpetual futures focused on realistic strategy validation before live deployment. Recommended stack: * Python * Pandas / NumPy * VectorBT / Backtrader / custom engine The final deliverable will be fully documented, reproducible, and designed for seamless transition into a future live-trading phase. Ready to discuss the strategy details and research objectives further. — Taher

@quan9824

Hello, I can build a Phase 1 Python backtesting and research engine for your BTC/ETH perpetual futures strategy, focused strictly on validation before any live deployment. My approach would include: • Historical OHLCV/futures data ingestion for BTC and ETH perpetuals • Clean data normalization and resampling • Strategy signal module with configurable parameters • Realistic execution model including fees, slippage, leverage, funding assumptions, and position sizing • Long/short trade simulation • Risk controls such as max drawdown, stop-loss, take-profit, and exposure limits • Performance analytics including CAGR, Sharpe, Sortino, max drawdown, win rate, profit factor, expectancy, trade distribution, and equity curve • Walk-forward testing and parameter sensitivity analysis • Clean research report summarizing whether the strategy is statistically viable I would structure the engine so the strategy logic, data layer, execution assumptions, and reporting are separated and easy to modify. I have experience with Python, pandas, NumPy, crypto market data, quantitative research, backtesting, risk analysis, and trading-system architecture. Before starting, I would need the strategy rules, target exchange/data source, timeframe, fee/slippage assumptions, and preferred reporting format. Best regards

@vishal1145

Your backtesting engine will produce misleading results if you don't account for funding rate volatility in perpetual futures - most retail backtests ignore this and show 300% returns that evaporate in live trading because funding costs eat 15-20% annually during trending markets. Before architecting the solution, I need clarity on two things: What's your target holding period per position (minutes vs hours matters for slippage modeling), and do you have access to tick-level orderbook data or just OHLCV candles? The data granularity changes the entire simulation architecture. Here's the technical approach: - PYTHON + PANDAS: Build a vectorized backtesting engine that processes 3+ years of BTC/ETH perpetual data with realistic fill simulation based on orderbook depth, not just closing prices. - RISK MANAGEMENT: Implement position sizing with Kelly Criterion, max drawdown limits, and dynamic leverage adjustment to prevent margin calls during flash crashes. - STATISTICAL ANALYSIS: Run Monte Carlo simulations with 10K paths to stress-test the strategy against different volatility regimes and calculate confidence intervals on Sharpe ratios. - TIME SERIES ANALYSIS: Model funding rate cycles and basis spreads to identify when perpetual futures deviate from spot pricing - this is where alpha lives in crypto derivatives. - BACKTESTING FRAMEWORK: Include transaction costs (maker/taker fees), realistic slippage curves based on trade size, and funding rate P&L that updates every 8 hours like actual exchanges. I've built 4 similar quant research platforms for crypto hedge funds, including one that identified a funding rate arbitrage strategy generating 40% APY with 0.8 Sharpe. Let's schedule a 15-minute call to discuss your strategy's edge before I design the simulation framework.

@prashanpatel557

Hello, I have strong experience building Python-based quantitative research and backtesting systems for crypto, equities, options, and futures markets. Your project aligns well with my expertise in strategy validation, historical market analysis, and trading research infrastructure. What I will deliver: • Modular Python backtesting and research engine for BTC & ETH perpetual futures • Historical data ingestion and preprocessing pipeline • Realistic execution modeling including: * Trading fees * Slippage * Position sizing * Detailed performance analytics: * CAGR / Total Return * Sharpe Ratio * Maximum Drawdown * Win Rate * Profit Factor * Trade Distribution * Equity curve and drawdown visualisation * Parameter optimisation and sensitivity testing * Clean, documented code for future live-trading integration Tech Stack: Python, Pandas, NumPy, Plotly, Backtesting Frameworks, Jupyter Research Environment Deliverables: • Complete source code • Research notebook and reports • Performance dashboard/charts • Documentation and setup guide Timeline: 2–3 weeks depending on strategy complexity and historical data requirements. Since this is Phase 1 research, I will focus on building a robust validation framework that can later be extended into a production trading system if the strategy proves viable. Please share the strategy logic, exchange/data source, and required backtest period to begin.

@Shrutibimpl

You want a localized Python backtesting and research engine to model execution fees, slippage, and performance metrics for BTC and ETH perpetual futures strategies. This analytical research environment simulates real world market conditions, allowing you to mathematically validate your trading strategies before risking active capital. By integrating precise calculations for maker/taker fees, funding rates, and order book slippage, the model reveals the true net returns of your setups. You get complete peace of mind with detailed statistical summaries that pinpoint your maximum drawdown, win rate, and profit margins, ensuring you make data backed decisions. We will build this research engine using Python with pandas and numpy for vectorized computations, or vectorbt to quickly map indicators across large datasets. The pipeline will ingest historical perpetual futures data from exchange APIs, calculating accurate funding rate adjustments and implementing a slippage simulation model based on historical order book depth. The output layer will compile performance metrics like the Sharpe ratio, Sortino ratio, and equity curves into clean, shareable Jupyter Notebook reports.

@mahads31

Hi, What specific metrics are you aiming to analyze for your crypto trading strategy? I specialize in building robust backtesting engines that incorporate realistic execution assumptions, including fees and slippage. With over 5 years of experience in Python and quantitative finance, I’ve developed various systems for analyzing trading strategies effectively. I’ll ensure that we validate your BTC and ETH perpetual futures strategy thoroughly using historical data. Once we establish the foundation, I can assist you in refining any details and preparing for potential phases beyond this initial research project. Let me know if you want to discuss this further! Best Regards,

@sdqtrades

As an experienced Python Quant Developer, I've spent my career perfecting the very skills that your project calls for. I specialize in backtesting and financial analysis, which are critical components to validating and refining a trading strategy. This is especially crucial when it comes to crypto trading as there are unique factors like fees and slippage to consider. Having already worked on similar projects for platforms like Binance and Coinbase, I have honed my ability to design intelligent algorithms that can assess historical data, factor in realistic execution assumptions, optimize performance, while strictly following risk management principles. What sets me apart goes beyond just being well-versed in the technicalities of coding. My understanding of complex web scraping techniques and smart data extraction plays a great role in ensuring the accuracy and comprehensiveness of the information used by the bot. The fact that we are at phase 1, research stage allows us ample time to finely tune not only our codes but equally strategize well using your gained historical data - a process I thoroughly enjoy and pride myself on. Finally, you should know that I value confidentiality and protect intellectual property rights. Your project will be handled with utmost privacy without compromising on delivering clear, optimized and high-quality codes within schedule. Looking forward to joining you on this exciting quant journey!

@KrisuTechkul

Hi, Krishna here from Delhi. We are a team of 20+ engineers, have completed 300+ projects with 4.7 rating. Recently we have completed a similar project. Would like to chat with you to understand the requirements. As a Python Quant Developer with profound skills in Data Science, I believe I'm the perfect fit for your Phase 1 research project. My extensive knowledge in building and deploying machine learning and deep learning algorithms can guarantee a comprehensive and rigorous approach to backtesting and analyzing your crypto futures trading strategy. I understand the value of realistic execution assumption, fees, slippage, and performance analysis in strategy validation before live deployment, and this is something that I consistently prioritize. Moreover, my experience extends to the domain of finance and economics where I have successfully created various predictive models including demand forecasting for inventory and supply chain optimization. I've also built fraud detection systems for better financial security. Drawing on my capabilities, I can ensure a robust testing process that accurately simulates market conditions while analyzing historical data.

@Vsion2

Hi, I'm Alema, Python developer with 10 years of experience. My main passion and specialization is working with data extraction. I have worked with many sites and wrote a lot of bots which can extract data in any required format, like Excel, CSV, Json or save data to a DB. You can see completed projects: https://www.freelancer.com/projects/excel/Looker-Studio-Performance-Dashboard/proposals https://www.freelancer.com/projects/french-translator/ation-Agent-Polyvalent/reviews https://www.freelancer.com/projects/ubuntu/Ubuntu-Price-Tracking-Suite-Deployment/proposals https://www.freelancer.com/projects/documentation/Python-Security-Audit-Report/proposals https://www.freelancer.com/projects/beautifulsoup/Data-from-https-www-gsmarena/proposals If you're looking for a reliable Python backend developer to help with your project, feel free to reach out. Your faithfully. Eng. Alema Akter

@jk31

Hello, I worked via Fiverr with a German Trading Startup (BTC trading) and wrote together with a friend a bot trading BTC (it was however a market maker bot). I wrote privately trading bots for myself. I did backtesting often enough to be able to help you. Is this bot for you personally? Or a business idea? I am trading privately Crypto and am in contact with some great traders. Other than bot development, I could also help to trade manually. Or also I could connect you to some manual traders, too. Many people will tell you something else, but for trading, you need an edge. I have a good friend - with whom I wrote several automated trading bots - and who himself wrote many automated trading bots. The more short-term you trade, the more noise is in the market - the more difficult it is to get an edge. While, the more long term you trade, the less noise is there, the better predictable. We are not talking about high frequency trading or abritrage trading. They are lucrative. But especially for high frequency trading, you need special setups which normal retail traders like us don't have. The more longterm you go (daily chart), however, the less need there is for automating the trade. And often just longterm holding BTC beats automated trading with smaller amounts. We could discuss also different strategies.

@collenliebenberg

I see you need a Python Quant Developer for crypto futures backtesting and research engine for BTC and ETH perpetual futures. I’d build this using Python to validate the strategy with historical data, execution assumptions, fees, slippage, and performance analysis. This will allow you to assess the strategy thoroughly before any live deployment considerations. I’ve worked with similar projects, ensuring accurate testing and analysis for optimal strategy performance. Quick question: Are you open to discussing further details to tailor the solution to your specific needs? Regards, Collen Jr Liebenberg

@Visharaa2024

Hello, I am interested in developing your Phase 1 quantitative research and backtesting engine for crypto futures strategies focused on BTC and ETH perpetual markets. With strong experience in Python development, quantitative analysis, and trading system design, I can build a robust, research-grade backtesting framework that accurately simulates real market conditions including fees, slippage, funding rates, and realistic execution logic. The system will be designed specifically for strategy validation rather than live trading, ensuring clean separation between research and execution layers. I will structure the engine to support modular strategy testing, historical data ingestion, performance analytics, and clear reporting of key metrics such as Sharpe ratio, drawdown, win rate, and risk-adjusted returns. The final deliverable will include well-documented Python code, reproducible research workflows, and a flexible architecture that can be extended later for live trading if required. I would be happy to discuss your strategy assumptions and data sources to ensure accurate modeling from the start.

@ziedb4

Hi, I build Python backtesting engines for algorithmic trading strategies — this project is exactly my area. I recently completed a full backtest research engine for a forex fade strategy (EURUSD/GBPUSD, M15), implementing: • Realistic slippage, spread, and commission modeling • Equity-based position sizing with compounding • Max drawdown, daily DD, Profit Factor, Win Rate reporting • Monthly P&L breakdown with HTML visual reports For your BTC/ETH perpetual futures project, I would: 1. Fetch historical OHLCV data (Binance/Bybit API) 2. Implement your strategy logic with realistic futures fees (maker/taker) and funding rate costs 3. Run parameter sweeps to find optimal settings 4. Deliver a clean research report: equity curve, drawdown chart, monthly breakdown, and full metrics This is Phase 1 research — I will not over-engineer it. Clean, well-commented Python code you can build on later. Could you share the strategy logic or indicator details so I can confirm scope and timeline?

@hugog17

Hi there — a backtesting engine that produces realistic results lives or dies on one implementation detail: whether slippage and fee modeling reflect actual futures market conditions, because optimistic assumptions at the research stage lead to strategies that look great in backtest and bleed in live markets. I've built Python backtesting frameworks for crypto perpetual futures using CCXT for historical data, vectorised execution simulation with realistic maker/taker fee tiers, funding rate drag, and configurable slippage models based on order book depth. A BTC/ETH momentum strategy research engine I delivered produced a full performance tearsheet — Sharpe, max drawdown, win rate, and fee-adjusted returns — across 2 years of minute-level data, which revealed a 23% overestimation of returns when realistic slippage was applied versus zero-slippage assumptions. The research engine will cover data ingestion, signal generation, execution simulation, and a clean performance report you can use to make an informed live deployment decision. What timeframe and signal type is the strategy based on — trend-following, mean reversion, or something else — so I can structure the backtesting framework around the right execution assumptions from the start?

@mussaibb7

I'll tackle this crypto futures backtesting and research engine with a tailored approach. By leveraging my experience in computer vision and ML delivery, I'll bring a strong feature engineering focus to this project. This will involve crafting a thoughtful strategy for extracting relevant features from your crypto futures data, alongside a well-structured validation framework to ensure robustness and reliability. My approach will prioritize reproducibility, with a clear training and validation flow and a saved model or submission file that can be easily replicated. To guarantee success, we'll establish a clear scope, milestone, and technical constraint before delivery begins. One key insight I'll apply is the importance of feature strategy, evaluation discipline, and reproducible training. This will drive the build quality, rather than relying on a generic model pass. For instance, I worked on retraining automation across 30+ model classes in computer vision and ML systems, which demonstrates my expertise in managing complex feature sets. Another relevant example is deploying multi-client ML inference APIs with sub-200ms latency and production-grade cloud delivery.

@purealmaz

Regarding your project, I have a quick question: will you be providing the historical tick-level data, or should my plan include integrating with a specific data provider/exchange API to fetch it? I plan to approach this by using Python with libraries like Pandas for data manipulation and a vectorized backtesting framework (like vectorbt or a custom-built one) to efficiently process historical data, model slippage/fees, and generate performance metrics. I previously tackled a similar challenge in a personal project where I developed a backtesting framework for a moving-average crossover strategy on spot markets. This involved ingesting historical OHLCV data, simulating trades with commission models, and calculating key metrics like Sharpe ratio and max drawdown. Let's connect to discuss the architecture. Regards, Philip O.

@rakhshanz3

As a data scientist with nearly two decades of experience in the field, I'm well-equipped to tackle the research and backtesting tasks you have in mind. My academic and professional background has bolstered my skills in Python and propelled me to leverage AI and ML-driven technologies for complex problem solving - making me an ideal choice for your project. Moreover, my previous role as CTO of an AI startup underscores my ability to employ cutting-edge approaches, npetonationally up-to-date technology, and an understanding of the crucial importance of accurate data processing in creating robust models. In light of this, I’m confident I can create a high-performing strategy validation system that's flexible enough to adapt to various execution assumptions, fees and slippage rates you may need to consider. Intricately acquainted with both cloud-deployment and scalable architecture design, I'll ensure that your system is designed with maximum efficiency and cost effectiveness at every stage. My aim is not just to fulfill your current needs, but to proactively develop a research platform that can seamlessly align with any future expansion or incorporation of live trading bots. Let's collaborate on charting a successful journey for your cryptocurrency futures trading!

@rishus753

Looking for a reliable software developer to build a high-quality, scalable, and secure solution for your business? You're in the right place. I will develop custom software tailored to your specific requirements, whether you need a web application, desktop application, business management system, automation tool, or API integration. Services Included Custom Software Development Web Application Development Desktop Application Development API Development & Integration Database Design & Management Bug Fixing & Performance Optimization Software Maintenance & Support Third-Party Service Integration Why Choose Me? ✔ Clean and maintainable code ✔ Scalable architecture ✔ Secure development practices ✔ Timely delivery ✔ Responsive communication ✔ Post-delivery support Technologies Frontend: React, Angular, Vue.js, HTML, CSS, JavaScript Backend: Node.js, Python, PHP, .NET Database: MySQL, PostgreSQL, MongoDB Cloud: AWS, Azure, Google Cloud

@warrick1718

This aligns perfectly with my skill set. I understand the need for a clean, professional, and seamless backtesting engine for your crypto futures trading strategy. While I am new to freelancer, I have tons of experience and have done other projects off site. I specialize in Python development, quantitative analysis, and building robust research engines for financial strategies. I would love to chat more about your project! Regards, Warrick Van Eeden