Introduction
Quantitative trading is a data-driven approach that leverages mathematical models, statistical techniques, and algorithms to identify and execute trading opportunities. Unlike manual or discretionary trading, which relies heavily on human judgment and emotions, quant trading is systematic, scalable, and objective.
By analyzing vast datasets and executing trades in small timeframes, it minimizes emotional bias, enhances consistency, and captures market inefficiencies with precision. This makes quant trading especially powerful in today's fast-paced, information-rich financial markets where speed and accuracy are key.
At P3, the Quantitative Trading Division of Valura, we focus on delivering consistent, scalable alpha through systematic and data-driven trading strategies. We combine quantitative research, strategy engineering, and robust risk analytics within a cohesive, innovation-led framework.
Core Components
1. Quantitative Research
In our research team, we explore financial markets through the lens of statistics, econometrics, and machine learning. We work on signal generation, factor modelling, anomaly detection, and regime switching analysis.
We rigorously backtest hypotheses using clean data in custom-built simulation environments. These research insights form the backbone of our alpha strategies.
2. Systematic Strategy Development
We transform research insights into live trading strategies using a modular, production-grade framework. Our strategies span multiple time horizons and asset classes (primarily Forex and commodities) and incorporate factors like momentum, mean reversion, value, and volatility.
Our development process emphasizes backtesting, forward testing, and real-time monitoring to ensure robustness and performance.
3. Portfolio and Risk Analytics
We manage capital allocation through a dedicated focus on portfolio and risk analytics. This includes position sizing, drawdown controls, risk attribution, correlation analysis, and stress testing.
Using real-time dashboards, we maintain full transparency on performance, risk exposures, and alpha drivers—ensuring portfolio stability even in volatile markets.
Infrastructure and Tools
We've built and operated on Synapse, our proprietary research and strategy development framework based on backtesting.py. Synapse standardizes the research lifecycle—integrating plug-and-play modules for strategy development, transaction cost modeling, alpha combination, and portfolio analytics.
It helps us reduce operational overhead and maintain reproducibility across projects. Additionally, we have a Strategy Lab as well, where we seamlessly customize and enhance our strategies.
At Valura, we operate at the intersection of quantitative rigor, strategic automation, and robust execution. Our vertically integrated structure, proprietary tooling, and commitment to continuous learning allow us to consistently deliver scalable alpha. We take pride in building strategies that not only perform but prepare us—and our firm—for long-term success in the fast-evolving world of quantitative finance.
USD/JPY Strategy Case Study
To illustrate our pipeline in action, let us walk you through how the P3 team analyses data and develops a trading strategy from start to finish. For this demonstration, we will focus on the USD/JPY currency pair and guide you step-by-step through the entire process of strategy creation.
This will provide a clear understanding of how raw market data is transformed into a systematic trading approach designed to perform effectively in live markets.
Data Analysis
In research, data analysis plays a critical role in developing effective predictive models and trading strategies. Statistical and Visualization techniques like Histograms, Boxplots, ADF Test, VIF Scores and Mutual information provide comprehensive insights into data distribution, stationarity, multicollinearity, and nonlinear relationships.
This enables more robust feature selection, transformation, and model design tailored to complex market dynamics. Data Lab is where we understand and prepare our data before any modelling or strategy development. We considered USDJPY Forex Data in our analysis.
Data Correction
The initial few rows often lack sufficient historical context for rolling window calculations. Carefully dropping these rows preserves integrity for subsequent computations without propagating NaNs or distortions.
Now we have cleaned and correctly indexed data ready for EDA.
The EDA Lab Pipeline
- Distribution Analysis: Analyse the Distribution type of the features, to decide if they can be used in regime shifting or trending or mean reverting strategies. Understanding whether features exhibit normality, skewness, multimodality, or heavy tails helps determine their suitability for different modelling approaches.
- Stationarity Testing: Apply statistical tests to assess stationarity, which is crucial for time series modeling and strategy development.
- Correlation Analysis: Examine correlations and multicollinearity to identify redundant features and optimize model performance.
- Nonlinear Relationships: Use mutual information to capture nonlinear relationships that traditional correlation might miss.
Strategy Development Process
Our strategy development follows a systematic approach that combines technical analysis with quantitative methods. For the USD/JPY example, we developed a mean-reversion strategy using Bollinger Bands and Volume Weighted Moving Average (VWMA) as key indicators.
Entry Conditions
- Aggressive Long Entry: Triggered when the closing price falls below the midpoint of the lower Bollinger Band and is above the VWMA. This suggests the price has fallen significantly below the mean and is expected to revert upward.
- Aggressive Short Entry: Triggered when the closing price rises above the midpoint of the upper Bollinger Band and is below the VWMA. This indicates the price has exceeded typical resistance and is expected to revert downward toward the mean.
Trade Execution
When entry conditions are met, the strategy closes any opposite open positions to avoid conflicting trades and opens a new position (buy or sell).
Risk Management
- Stop Loss (SL): Set relative to current price, capped at a maximum of 20 price units to prevent excessive risk. Calculated using the pip size inferred from the price's decimal format.
- Take Profit (TP): Set at a target distance (generally twice the risk, e.g., 0.02 for 2%), scaled by the current price.
- Position Sizing: Based on a fixed risk percentage (e.g., 1%) and the distance between entry and SL, ensuring consistent risk across trades.
Strategy Lab
Our automation pipeline includes a powerful tool called 'Strategy Lab,' developed by our team. The tearsheet generated is passed to this tool, and it will analyse the performance by examining various factors including:
- Macroeconomic factors like inflation, GDP, national income
- Stock, bond, commodity, and other capital markets
- Interest rates
- Top 25 technical indicators chosen by the data analysis tests
- Sentiment analysis and more relevant to forex assets
Analysis from Strategy Lab for Current Strategy
- For worst 5 maximum drawdown periods the value of RSI is < 25 for long positions and > 75 for short positions.
- Bollinger Bands multiplier (2.5) is giving false signals; reducing to 2.0 will better capture breakouts without false signals.
- Stop-loss limit (max 20 units) is too rigid; replace with ATR-based dynamic stop-loss for adaptation to current volatility.
- The returns are too volatile which reduces sharpe ratio, adding volatility condition would decrease drawdown and thus sharpe ratio.
Forward Testing
Once a strategy has successfully passed our backtesting criteria, the next step is to test it in a real-time market environment—but without risking actual money. We do this by running the strategy in a simulated live setting, to see how it performs with current market conditions.
During this forward testing phase, we closely monitor the strategy over several months. We look at how well it performs, whether it remains consistent, and if it handles different market regimes—such as trending or volatile periods—appropriately.
If the strategy continues to show strong and stable results throughout this period, we then consider it proven enough to move it into our pool of approved strategies.
From this pool, strategies are selected and allocated capital based on our overall portfolio management framework. This framework is designed to balance risk and reward by combining multiple strategies, ensuring a diversified and resilient portfolio.