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TradeMaster
at 2Cents Capital
TradeMaster is the all-in-one powerhouse driving 2cents, delivering data-driven insights, live trading, and everything you need to master the markets in one seamless platform.
What makes TradeMaster your key to trading dominance?
TradeMaster is the single self-sufficient platform responsible for powering the entirety ofthe 2cents organisation. From data-driven insights to live trading, TradeMaster servesas the all-in-one solution for mastering the markets.
Key functionalities supported by the platform
Data Layer
The data layer delivers fast, reliable, and comprehensive market data across multiple asset classes for real-time decision-making.
- Access a diverse range of data, from transactional price data to enriched sentiment data for analyzing market behavior.
- Supports all major markets: Indian, US, Crypto, Forex, and Commodities.
- Timeframes range from 1-day intervals to tick-level data, catering to both macro and high-frequency trading needs.
- Tailored to meet the analytical and operational needs of every division within the organization.
- Built on cloud databases, ensuring data accessibility from anywhere for a modern, remote-first workflow.
- Easily scales to handle vast datasets, ensuring smooth performance even as data demands grow.
- Consolidates live data streams into historical datasets, offering a unified resource for strategy testing and decision-making.
- Enables seamless backtesting with near-real-time data for robust strategy validation.
- Designed for lightning-fast data access to support real-time decision-making.
- QuestDB ensures low-latency querying, even with high-frequency data streams, minimizing delays in data retrieval.
- Ideal for real-time trading, strategy backtesting, and rapid analysis of high-volume datasets.
- Built to provide unparalleled speed and accuracy, empowering the team to stay ahead in fast-moving markets.
Strategy Layer
The Strategy Layer is the cornerstone of developing and optimizing trading strategies, integrating Alpha Strategies, Risk Management Models, and Trade Management Models
- Indicator-based: Leverages RSI, MACD, VWAP, and other technical indicators.
- Machine Learning-based: Employs predictive algorithms for pattern recognition.
- Genetic Algorithm-based: Uses evolutionary techniques to optimize strategies.
Flexible approaches enable both traditional and cutting-edge strategy creation.
- Fixed Position Sizing: Static and predictable allocation.
- Dynamic Position Sizing: Adjusts based on volatility, risk tolerance, and performance.
- Fortify Your Capital: Mitigate risks and stay resilient with proactive risk control in uncertain markets.
- Entry/Exit Strategies: Precise conditions for starting and closing trades.
- Stop Loss/Take Profit Rules: Predefined safeguards to limit losses and secure profits.
- Dynamic Trade Adjustments: Stay agile with real-time tweaks to optimize outcomes in ever-changing markets.
- Evaluate combinations of Alpha, Risk, and Trade models.
- Top-Performing Strategies: Identify the best trio for seamless forward testing and live trading.
- Accelerate Development: Slash your testing time while achieving peak performance with unparalleled efficiency.
Processing and
computational layer
The processing and computational layer is concerned with strategy testing, optimisation and validation.
- Supports both event driven and vectorised backtesting.
- Event-Driven Backtesting: Achieve highly accurate results using backtesting.py, with fast, parallelized algorithms ensuring no latency compromise across multiple assets and timeframes.
- Vectorized Backtesting: Experience hyperfast backtesting with near-zero latency on the GPU, powered by Zipline’s vectorized operations.
- Supports single asset, multi-asset, single asset multi-timeframe, and multi-asset multi-timeframe testing.
- Allows for effortless backtesting of strategies on large datasets by simply specifying the data—no adjustments needed, thanks to the modular design.
- Generates detailed strategy performance insights through summary sheets and 3-tiered tearsheets for informed decision-making.
- TradeMaster allows for effortless hyperparameter tuning.
- Focus solely on strategy structure—TradeMaster handles finding optimal parameters for any given target.
- Optimize strategies based on metrics of your choice, such as final equity, Sharpe ratio, or win rate.
- Simply specify the data, parameter space, and target metric—TradeMaster does the rest.
- The modular approach ensures seamless usage regardless of data or parameters.
- Fully parallelized at every stage, ensuring minimal latency and maximum efficiency.
- TradeMaster provides robust strategy cross validation techniques for controlling overfitting on test data.
- Walk Forward Optimization (WFO): Prevent overfitting by splitting data into training-testing chunks, optimizing locally, and analyzing performance on testing data with detailed reports and tearsheets.
- Combinatorial Purged Cross-Validation (CPCV): A cutting-edge method ideal for small datasets, creating multiple train-test splits for comprehensive validation while reducing overfitting risks.
- WFO provides faster cross-validation, testing one out-of-sample window at a time, perfect for larger datasets.
- CPCV ensures statistical robustness by utilizing all data for both training and testing, with purging and embargoing to handle temporal patterns.
Simulation Layer
The simulation layer is aimed at market simulation and forward testing using custom scenarios.
- TradeMaster provides monte carlo simulation for generating virtual market conditions by randomizing historical market candles while maintaining consistent opening and closing levels.
- Allows testing of strategy performance across multiple market variants to ensure extreme robustness.
- Pinpoint market conditions where the strategy may fail, enabling pre-deployment risk mitigation.
- Highlight scenarios where the strategy excels, allowing for increased portfolio weightage in live trading.
- Strengthen strategies before deployment to minimize risks and enhance portfolio performance.
- Enables risk free paper trading wherein strategies are tested in real-time using live market data fetched directly from broker APIs—without risking actual capital.
- Simulate order filling and trades with live broker data to evaluate strategy performance under real market conditions.
- Only strategies that excel in forward testing advance to live trading, ensuring top-tier performance.
- Trades and orders are recorded in real time, stored in a database for strategy evaluation and portfolio optimization.
- TradeMaster enables you to create tailored market scenarios, allowing strategies to be tested under specific, rare, or extreme conditions for maximum preparedness.
- Sudden Volatility Spikes: Replicate extreme price fluctuations caused by unexpected events to evaluate risk management and trade execution in chaotic markets.
- Flash Crashes: Simulate rapid price drops and recoveries to assess strategy resilience against illiquidity, slippage, and panic-driven activity.
- Low-Liquidity Conditions: Test strategies in thinly traded markets with wide bid-ask spreads and delayed order execution.
- Trend Reversals and Breakouts: Model abrupt trend shifts or breakout patterns to refine momentum and mean-reversion strategies.
- Economic Events and Announcements: Mimic macroeconomic shocks like interest rate decisions or GDP reports to gauge strategy performance during high-impact news releases.
- Enhance Strategy Robustness:
Identify weaknesses and fine-tune strategies to ensure optimal performance in real-world conditions, no matter how extreme.
Tearsheet Generation Layer
The Tearsheet Layer focuses on analyzing and presenting trading strategy performance through key metrics, risk analysis, and detailed insights across various levels for comprehensive evaluation and refinement.
Risk Metrics and Strategy Performance
- Level 1 tearsheets provide key risk metrics to assess strategy effectiveness
- Exposure Time
- Return (Annual and Cumulative)
- Volatility and Sharpe Ratio
- Win Rate, Profit Factor, Expectancy
- Max Drawdown, Avg. Drawdown, etc.
- SQN, Kelly Criterion
- These metrics help identify the overall performance and risk profile of a strategy.
- Provides a quick snapshot of how the strategy would have performed historically.
Trade Book and Time-of-Day Analysis
The Level 2 Tearsheets focus on providing a comprehensive and granular analysis of trading strategies by breaking down their performance into various components for detailed evaluation.
It includes the following aspects:
- Equity Curve Analysis: Tracks strategy performance over time, highlighting trends, drawdowns, and recoveries to identify periods of strong or weak performance.
- TradeBook Insights: Documents every trade for in-depth analysis, enabling identification of profitable trades, losses, execution efficiency, and patterns to refine the strategy.
- Market Factor Comparison: Assesses strategy performance against factors like volatility, momentum, market types, and specific markets or sectors to identify optimal conditions.
- Time-of-Day Analysis: Analyzes trade performance by time of day, revealing favorable trading windows for optimizing execution.
Strategy Persona Profiling and Seasonality Analysis
- Strategy Persona Profiling: Categorizes strategies by performance in various market conditions (e.g., aggressive, conservative), highlighting strengths and weaknesses in specific environments.
- Seasonality Patterns: Analyzes performance across seasons or months to uncover recurring trends, enabling seasonal strategy adjustments.
- Drawdown Behavior Analysis: Examines equity declines to identify underperformance periods, aiding in risk mitigation and strategy refinement.
- Equity Teardown by Macro Factors: Decomposes equity performance in relation to macroeconomic variables like interest rates and inflation, offering insights into broader economic influences.
Distributed computing
In today’s volatile financial markets, speed and accuracy are critical. Traditional systems struggle with the demands of large-scale data analysis and high-frequency trading. Our scalable, automated solutions empower financial institutions to process real-time data, adapt to market shifts, and make smarter, faster decisions—reducing risks and maximizing performance in an ever-changing industry.
- High-Performance Infrastructure: Efficient execution of complex algorithms and backtesting.
- Seamless Team Collaboration: Enhanced workflows through integrated development environments.
- Consistent Backtesting Environments: Ensured accuracy and scalability for reliable testing.
- Detailed Performance Reporting: Comprehensive metrics to support informed decision-making.
- Data-Driven Decision-Making: Advanced reporting and infrastructure for efficient operations.
- CI/CD Pipelines: Automate integration and deployment, ensuring fast, reliable updates from development to production, reducing time-to-market.
- AI for Workload Optimization: Leverage AI to dynamically optimize resource allocation, improving efficiency and performance in backtesting processes.
- Hybrid Deployment Strategy: Combine on-premises and cloud resources for seamless scalability, adapting to varying workloads while maintaining cost-effectiveness.
- Global Scalability: Position the system to scale beyond the limitations of a single VPS, supporting global operations efficiently.
Frequently Asked Questions
TradeMaster is designed as an all-in-one platform for trading, offering features such as historical data analysis, strategy development, backtesting, optimization, and live trading to empower traders and strategists.
TradeMaster supports tools for developing customizable strategies, AI-driven insights, and robust testing methods, allowing users to adapt to evolving market dynamics seamlessly.
The data layer supports various markets, including Indian, US, Crypto, Forex, and Commodities, with timeframes ranging from 1-day intervals to tick-level precision.
By integrating low-latency time-series databases like QuestDB, TradeMaster minimizes fetching latency, ensuring real-time access to data for informed decision-making.
Yes, the Strategy Layer supports a modular approach, allowing you to create indicator-based, machine learning-based, or genetic algorithm-based strategies.
The layer offers dynamic and fixed position sizing, precise entry/exit conditions, and real-time adjustments to optimize risk management and trade execution.
TradeMaster supports both event-driven and vectorized backtesting for single or multiple assets and timeframes, ensuring accuracy and speed.
The platform automates hyperparameter tuning, optimizing strategies based on user-defined metrics like Sharpe ratio or final equity.
Monte Carlo simulations create virtual market conditions to test strategies under various scenarios, ensuring robustness and identifying potential weaknesses.
Forward testing evaluates strategies in real-time using live market data, providing insights into performance under current conditions without risking actual capital.
