In addition to event-driven strategies and algorithmic trading, the 520 Arbitrage Fund leverages the power of quantitative trading to identify statistical patterns and make informed investment decisions. Our investment models take into account data on events relevant to financial and economic anomalies, which are analyzed to identify statistical patterns and make predictions about the direction of equity prices.
By mining data for random event movements and using advanced pattern recognition techniques, we are able to make statistical predictions with a high degree of accuracy. Our data-driven approach helps to take emotion out of trading and ensures that we are making informed investment decisions based on objective criteria. This approach, combined with our focus on short-term trading opportunities, may help to generate exceptional returns for our investors.
Strategy Identification
Strategy Backed Testing
Execution Systems
Risk Management
Each algorithmic being designed (Momentum A, Momentum B, Mean Reversion A, etc.) will go through the following stages. This sequence begins with an idea and progresses through coding, analyze, back-test, optimize, walk forward and then potentially back to idea in the event a change is being considered.
Based on the specific characteristics of the corporate event in question, these strategies may adopt either relative value or directional positions to capitalize on the evolving situation.
Utilize cutting-edge data analytics and real-time market insights to uncover untapped trading opportunities stemming from market inefficiencies and corporate events. Our system operates around the clock, meticulously scanning and selecting the top equities daily, ensuring optimal performance and growth potential.
Performance Metrics: Evaluate KPIs and compare against benchmarks.
- Risk Analysis: Assess risk exposure and conduct stress tests.
- Assumption Validation: Test sensitivity to changes in assumptions.
- Overfitting Detection: Monitor performance and minimize overfitting.
Monitor performance and minimize overfitting.
Expansive Timeframe: Execute back-tests across the broadest possible historical data range, ensuring a comprehensive assessment of the algorithm’s performance.
Insightful Metrics: Delve into crucial indicators, including average gain per trade, drawdowns, and profit factor, to uncover the algorithm's true strengths and limitations.
Fine-tune parameters for optimal returns and risk.
Continuously refine strategy and risk management.
Evaluate robustness across market scenarios.
Ensuring real world Performance: Out-of-Sample
Evaluation: Test Algorithm effectiveness on unseen data.
Dynamic Timeframes: Simulate real-world trading using rolling windows.
Robustness Analysis: Examine resilience to diverse market conditions.
Ongoing Validation: Regularly update analysis for continued live trading relevance.
