09.01.2024 / 08:45
Intraday Power Algo Trading
The Essential Elements of Algo-Based Trading
Pimp Your Algos! Ready to learn more about intraday power algo trading?
We will unpack the essential elements of algorithm-based trading across the dynamic energy landscape.
In today’s fast-changing energy markets, automated trading has become fully established. Auto trading capitalizes on profit potential, which requires even the most experienced algorithmic traders to continually redefine their approaches.
When operating an auto trader, your main question is: "Are my algos running robust and profitably, not only today, but also tomorrow?"
We'll explore the following and even more key topics to help you improve your algorithmic trading strategies:
Data Management Mastery: Ensure high data quality, defined by data completeness and data consistency, for successful algo trading.
Architectural Brilliance: Build a robust infrastructure that’s resilient against the challenges of energy markets, ensuring seamless and efficient operations.
Risk Management & Governance Essentials: Safeguard your assets with sufficient risk management governance which lays a solid foundation for your algorithms.
New Trading Strategy Exploration: Establish a dynamic innovation cycle to continuously explore new trading strategies, and to ensure that your existing algorithms remain current and future-proofed.
In our ongoing journey to enhance your algorithmic trading prowess, we’re diving into a critical aspect - Data Management.
Clean Up Your Data! Efficient data management is the bedrock foundation for robust and profitable algo trading. It gives your algorithms a springboard to greater reliability and profitability. The key steps to cleaning up your data mess are:
Collecting Data and Ensuring Data Availability: Lay the groundwork through meticulous data collection, and by normalizing various data formats from different vendors for seamless availability- This applies to any relevant data types – for example, price data, weather data, trade data and down to orderbook data.
Data Storage and Data Consistency: Say “no” to data jungles! Go with centralized data management to avoid decentralized and “unmanaged” storage solutions. Use normalized data formats to ensure organized, consistent data storage.
Data Completeness and Validation: Implement automated routines to ensure data completeness. Validate your data, making sure that all entries, such as those in a forward curve, make sense. Completeness and validation contribute to achieving optimal data quality.
Data Ownership and Data Compliance: Establish a robust data ownership concept; clarify responsibilities, avoid silos, and introduce a governance framework. Uphold data compliance to prevent penalties from license breeches, ensuring only authorized access and usage of data.
Data Accessibility and Distribution: Centralized data management ensures seamless data accessibility, and provides a solid framework for your algorithms. API-driven interfaces ensure efficient and robust data distribution.
Efficient data management isn't just a process – it’s the foundation of future success in algo trading. The steps above will help you build a resilient and robust foundation for executing your algos.
Testing and Benchmarking
In the dynamic realm of energy markets, you often notice a fascinating phenomenon – market prices are not in perfect harmony. Seizing this opportunity gives you a gateway to risk-free arbitrage profits.
From Manual Insights to Automated Trading:
Diving into the trading world, we decode signals with the naked eye and craft strategies by hand. This attention to detail builds a solid understanding of market dynamics and lays the foundation for scalable, robust, and profit-driving algorithmic execution!
When putting your algorithms into action, it's essential to ensure that the implemented version functions as intended, and is consistent with the evidence you gained in the prototyping phase.
Evaluating your latest algorithmic trading strategy is a must to guarantee error-free implementation and deployment.
Running tests on historical data paints a vivid picture of its prowess – a crucial step in the continuous journey of refinement.
Benchmarking the New Algo:
The benchmark for a new algo begins with our expectations, often molded by the status quo – in this context, manually implemented strategies. Benchmarking sets the baseline for performance, and acts as a compass, guiding your algorithms toward achieving and exceeding predefined goals.
When manually executing new trading strategies, always compare with expectations, which can be derived from your profit and loss.
Remember to evaluate against established industry benchmarks.
When developing your algorithms from scratch to production, it's essential to conduct a thorough evaluation of profit potential and risk. Historical Simulations test your algo’s performance based on historical data.
Historical Simulation Concept:
Your algorithmic trading strategies undergo testing in an ideal laboratory setting, assuming all specified data, including actual historical prices and order book data, is readily accessible and available in a timely manner. Historical orderbook data is a prerequisite meaningfully assessing your algorithmic trading performance in a meaningful way.
Trading signals are generated instantly by your algo, leading directly to a trading execution.
Evaluate the simulated historical profits and losses your algo would have accrued.
As you navigate through historical data, there is no feedback that occurs between your algo and other market participants. You may detect competing algos in historical orderbook data; however, they can’t detect your algo.
Historical order book data is invaluable as it enables assessing the profitability of your strategies more realistically. When executing a trading strategy by placing an order with a specified volume, you can precisely determine the transaction price from the order book data.
Lessons from History:
Explore historical extreme scenarios to understand how your algo operates in challenging conditions.
Gain insights into associated risks, enabling you to set triggers for algo suspension, limiting overall risk exposure.
High data quality! To execute historical simulations, high quality data management is crucial (see above "Data Management). Ensure your required data is available, accessible, complete, consistent, and validated.
Storing orderbook data! Prices are shallow information whereas orderbook data provides depth. Capturing and storing orderbook data is challenging and different from storing any other kind of energy data formats.
Reproducing the full orderbook history! Orderbook data evolve dynamically over time. Reproducing an exact state of an orderbook along the past is paramount for evaluating your algo and its historical profitability.
Exploring New Trading Strategies
The next step in our journey will be all about refining profitable algorithmic trading strategies.
Correlation Trading: A Closer Look
Correlation trading revolves around identifying pairs of contracts that are in equilibrium with highly correlated market price changes.
When a pair of contracts momentarily deviates from equilibrium, the correlation breaks down, signaling a profitable trading opportunity for the pair.
Key Steps to Monetizing Insights on a Broader Scale:
Understand Market Dynamics: Explore key drivers in the business and the wider economy. For the correlation trading example, validate the economic rationale behind the equilibrium of two contracts.
Detect Patterns in Market Data: Look for those patterns that can be translated into profitable algorithmic trading strategies. For the correlation trading example, high correlation of price changes is the pattern that indicates an equilibrium.
Formulate Quantifiable Metrics: Use these to define the essential components of the strategy. For the correlation trading example, the correlation between price changes is the metric for identifying those pairs that have a profitable equilibrium relationship. Trading signals are specified by a correlation breakdown.
Build a Market Screener: Create this by collecting a library of patterns consisting of quantifiable metrics for various trading strategies.
Automatically Detect Patterns: Run the market screener on your market data to identify potentially profitable contracts for your algo trading strategies. For the correlation trading example, a multitude of potential contract pairs can be identified.
Build and Explore New Trading Strategies: Use historical market data to generate trading signals on your identified contracts, and track the profit and loss from execution (see above "Historical Simulation")
Algo Fine-Tuning and Recalibration
Let's explore the inner workings of algorithmic trading strategies and demonstrate how to sharpen their performance.
Algorithmic Strategies: Sensitivity to Defining Parameters
Pay close attention to the parameters that define your algorithmic strategies. For example, in correlation trading, a trading opportunity is signaled when a pair is out of equilibrium. This is indicated by a correlation breakdown, which occurs when the correlation drops below a pre-defined threshold. The result? A stricter trading signal is generated, which reduces the number of trading opportunities.
Aim: Optimize Parameters for Maximum Profits
The primary goal of your automated trading strategies is to regularly optimize their parameters to maximize expected profits —and not just for a single point in time, but continuously.
For the correlation trading example, find the threshold that balances the number of trading opportunities with their profit potential. A stricter threshold leads to higher profit potential for each trading opportunity, but simultaneously reduces the overall number of trading opportunities.
Harness New Data for Continuous Improvement
New data brings a torrent of valuable information into your system. Harness its value by constantly fine-tuning your algo through ongoing recalibration. For the correlation trading example, regularly update your strategy parameters, using new historical data (see above "Historical Simulation").
Monitor Markets for Structural Breaks
Your algorithmic trading strategy is based on historical data, and assumes that the past is a good predictor for the future. Continuously validate your underlying assumptions by looking for structural breaks. For the correlation trading example, the underlying assumption is that a pair of prices is in equilibrium, indicated by a sufficiently high correlation.
It’s crucial to check that this equilibrium assumption is still valid, using regularly updated historical data. A structural break can undermine the equilibrium assumption and can endanger your profits through a trading strategy that’s no longer valid.
⇒ What will follow next? Trading Architecture and Infrastructure 🏗️
🚧 Our journey doesn't stop here, as next stop in our itinerary we'll delve into how to give your algorithms a robust and scalable foundation.