Quantitative Trading Analyst

Qompyl•,

6h•Remote

About The Position

Qompyl is an early-stage fintech startup building a no-code platform that enables users to create, backtest, analyze, and monitor trading strategies. Our mission is to make sophisticated quantitative trading tools more intuitive, visual, and accessible. We are developing a platform where accuracy, data integrity, and sound financial logic are essential to delivering a reliable and trustworthy user experience. We are looking for a Quantitative Trading Analyst to help research, build, test, and improve trading strategies, indicators, and quantitative trading functionality within Qompyl. This is a hands-on and cross-functional role combining quantitative research, systematic trading, indicator and signal development, strategy analysis, product validation, and product collaboration. We are specifically looking for someone with a mathematically and statistically driven approach to trading. The ideal candidate can take a market hypothesis or trading idea, translate it into measurable rules or signals, test it rigorously using historical data, and critically evaluate whether the resulting performance is statistically and financially meaningful. You will work directly with Qompyl's indicators and strategy-building tools: researching and developing indicators and signals, validating their mathematical and financial logic, building and backtesting systematic strategies, analyzing performance and risk, and identifying opportunities to improve the platform. You will also work closely with our product and trading teams to translate quantitative trading concepts into intuitive tools that traders can use without needing to code. This is not a traditional software QA role or a purely discretionary trading role. We are looking for someone who combines financial-market knowledge with quantitative thinking, enjoys experimenting with data and models, and can contribute to an evolving trading product.

Requirements

Strong quantitative foundation in mathematics, statistics, probability, econometrics, engineering, computer science, physics, quantitative finance, or a related discipline.
Hands-on experience with quantitative research, systematic trading, algorithmic trading, or quantitative strategy development.
Strong understanding of statistical analysis and its application to financial markets.
Experience researching and testing trading signals or systematic strategies.
Strong understanding of backtesting methodology and common sources of bias.
Ability to analyze strategy performance using risk and return metrics.
Proficiency with Python for quantitative analysis, research, or backtesting.
Strong practical understanding of financial markets and trading mechanics.
Understanding of entries, exits, order logic, position sizing, risk, P&L, returns, volatility, and drawdowns.
Ability to critically evaluate whether quantitative results are statistically and financially reasonable.
Strong analytical mindset, attention to detail, and healthy skepticism.
Ability to communicate quantitative concepts clearly to both technical and non-technical collaborators.
Comfortable working independently in a remote, asynchronous, early-stage startup environment.

Nice To Haves

Degree or advanced coursework in mathematics, statistics, quantitative finance, econometrics, engineering, physics, computer science, or another highly quantitative discipline.
Experience with NumPy, pandas, SciPy, statsmodels, scikit-learn, or similar quantitative/data-science libraries.
Experience with time-series analysis, statistical modeling, optimization, factor research, or machine learning applied to financial markets.
Experience creating or modifying trading indicators.
Familiarity with TradingView, Pine Script, or other strategy-building/backtesting platforms.
Experience working with stocks, ETFs, futures, forex, cryptocurrencies, or derivatives.
Experience with SQL and financial datasets.
Experience contributing to a fintech, trading, analytics, or investment product.
Product-oriented or entrepreneurial mindset.
Experience communicating quantitative research or trading concepts to broader audiences.

Responsibilities

Research quantitative trading ideas, signals, indicators, and market relationships.
Translate market hypotheses into measurable, testable quantitative rules.
Apply statistical and mathematical methods to evaluate signal quality and robustness.
Analyze relationships across prices, returns, volatility, volume, momentum, market regimes, and other relevant market variables.
Identify noise, overfitting, unstable relationships, and potential biases.
Evaluate whether observed patterns are economically and statistically meaningful.
Document research methodology, assumptions, findings, and limitations.
Research and create new trading indicators and quantitative signals for the Qompyl platform.
Define indicator logic, formulas, parameters, signals, and expected behavior.
Validate calculations and outputs for mathematical and financial correctness.
Test indicators across different assets, time periods, market regimes, and parameter configurations.
Identify edge cases, unstable behavior, misleading signals, or unintended relationships.
Collaborate with trading and product teams to improve Qompyl's indicator library.
Translate quantitative concepts into intuitive functionality within the Strategy Builder.
Build and test systematic trading strategies using Qompyl and analytical tools such as Python.
Combine signals, indicators, market conditions, and risk rules to research different strategy hypotheses.
Analyze entries, exits, position sizing, portfolio allocation, returns, P&L, drawdowns, volatility, Sharpe ratio, and other relevant performance and risk metrics.
Design rigorous backtests and critically evaluate their results.
Account for potential issues such as overfitting, look-ahead bias, survivorship bias, transaction costs, slippage, and parameter sensitivity.
Perform out-of-sample, robustness, and scenario testing when appropriate.
Challenge results that appear statistically or financially unrealistic.
Compare strategy behavior across different assets and market regimes.
Work closely with Qompyl's product, engineering, data, and trading contributors.
Bring a quantitative trading perspective to new indicators, strategy-building functionality, analytics, and product features.
Help determine whether proposed quantitative features are mathematically sound and useful to traders.
Translate quantitative research into clear product requirements and user-friendly functionality.
Participate in brainstorming and exploration of new trading and research tools.
Help bridge the gap between quantitative research and an intuitive no-code trading experience.
Engage with traders and beta users to understand how they build and evaluate strategies.
Gather feedback on indicators, strategy analytics, and quantitative functionality.
Identify recurring needs that could become new signals, indicators, analytics, educational resources, or product improvements.
Explain quantitative trading concepts clearly to users with different levels of technical expertise.