Trading Alphas: Mining, Optimisation, and System Design By Thomas Starke – QuantInsti

Trading Alphas Mining, Optimisation, and System Design By Thomas Starke – QuantInsti – Digital Download!

Greetings, lovely visitors. The course contents can be quickly seen here:

 

Trading Alphas Mining, Optimisation, and System Design By Thomas Starke – QuantInsti:

What are the advantages of selecting micro alpha models over alternative trading methods like standard factor models, risk-parity, or trend following? Essentially, if constructed effectively, these models have the potential to offer superior performance, stability, and risk management compared to alternative trading methods.

This course will teach you the locations of micro-alphas and the techniques for writing highly efficient algorithms to rapidly analyze, backtest, optimize, and implement your trading strategy in the shortest possible time frame.

LEVEL

Advanced

AUTHOR

Dr. Thomas Starke

LIVE TRADING

Implementing a vectorized technique to incorporate backtesting, stop-loss, and profit-take.

Extracting micro-alpha through the analysis of trends, mean-reversion, asset correlation, and cointegration in mining.

The technique may be analyzed using several metrics such as total profit, Sharpe ratio, Sortino ratio, profit factor, drawdown, and profit per trade.

Parameter optimization can be achieved by employing machine learning techniques, such as clustering.

Developing a trading system from the ground up involves designing the program structure, implementing logging mechanisms, managing data storage, selecting appropriate hardware, conducting rigorous testing, and implementing version control.

Conduct a concise examination of execution models, use parallel computing, and elucidate several tiers of logging.

LEARNING TRACK 8

This course is a part of the Learning Track: Advanced Algorithmic Trading Strategies 

INTERMEDIATE

• Mean Reversion Strategies In Python
• Momentum Trading Strategies

ADVANCED

• Trading Alphas: Mining, Optimisation, and System Design
• Trading in Milliseconds: MFT Strategies & Setup

PREREQUISITES

Proficiency in Python, including the utilization of Python libraries such as pandas, numpy, and matplotlib, as well as a strong understanding of machine learning principles such as clustering, prediction, in-sample, out of sample, and features. Proficiency in executing orders for the purchase and sale of exchange-traded assets.

SYLLABUS

Introduction

This course will function as a systematic guide that assists you in identifying trades that are based on small-scale opportunities for profit in the current financial markets. The course employs interactive techniques that facilitate comprehension of the ideas and enable comprehensive responses to inquiries regarding micro alphas. This part additionally encompasses the course framework, along with the diverse pedagogical resources employed in the course, such as videos, quizzes, coding exercises, and the capstone project.

• Introduction
• Course Structure
• Quantra Features and Guidance

Micro Alphas

The efficient market hypothesis posits that the present price of a security reflects all available information in the market. This situation presents a scenario in which it is unfeasible to regularly produce profits due to the random and unexpected nature of price swings. Nevertheless, there are methods to capitalize on market inefficiencies and generate profits. This section facilitates the initial stage of investigating micro alphas by establishing a fundamental starting point.

• Micro Alphas
• Efficient Market Hypothesis
• Overturn Efficient Market Hypothesis
• Autocorrelation
• Assumption of Technical Indicators
• How to Use Jupyter Notebook?
• Generating Price Series at Random
• Generate Random Numbers
• Scaling
• Generate Price Data
• Statistical Study on Randomly Generated Price Series
• Autocorrelation
• Trading Signals
• Why Did the Signal Fail?
• How to Use Interactive Exercises?
• Additional Reading on Micro Alphas

Market Inefficiencies: Trend

By utilizing a certain degree of technical proficiency, you also have the opportunity to profit from market inefficiencies. Market trends represent a type of inefficiency. This section will cover the process of market trends occurring. You will also acquire the knowledge of formulating a strategic plan by analyzing the correlation between previous and present returns.

• Market Inefficiencies
• Trends
• Compounded PnL Curve
• Positive Auto-Correlation
• Positively Correlated Time Series
• Equation for Auto-Correlation
• Value of g
• Strategy for Positive Correlation
• Types of Backtesting
• Compounded PnL Curve
• Auto-Correlation
• Trending Prices
• Series of Returns
• Trend
• Generate Random Returns
• Linearly Fit the Autocorrelated Data
• Backtest the Strategy
• Additional Reading on Trends

Market Inefficiencies: Mean Reversion

Does a stock’s current and historical returns exhibit a correlation that indicates its tendency to revert to its mean? Affirmative. This section will cover the correlation type that results in mean reversion, the process of developing a strategy based on a stock’s mean-reverting characteristics, and the technique of combining two methods for improved outcomes.

• Mean Reversion
• Market Characteristic
• Constant g
• Type of Time Series
• Correlation of Returns
• Strategy and Benchmark Returns
• Strategy Based on Correlation
• Ideal Metric
• Annualised Alpha
• Generate Negatively Autocorrelated Returns
• Additional Reading on Mean Reversion

Trading with Trends and Mean Reversion

This part will teach you how to develop and evaluate strategies based on market inefficiencies, such as trend and mean reversion, using actual data from the real world. You will also get the knowledge of comparing the returns of the strategy with the returns of the market in order to analyze its performance.

• Trading with Autocorrelated Data
• Calculate Risk-Adjusted Returns

Market Inefficiencies: Chart Patterns

Traders frequently utilize chart patterns to forecast price fluctuations. This refers to a specific form of market inefficiency that can be taken advantage of in order to get higher than average returns, also known as alpha. This section will teach you how to simultaneously backtest many patterns.

You will also get the knowledge of formulating a strategy based on the findings obtained from backtesting and evaluating its success.

• Chart Patterns
• Define Chart Patterns
• Values of a Candlestick Pattern
• Backtesting
• Library for Candlestick Pattern
• Candlestick Pattern for Micro-Alpha
• Usefulness of Alpha
• Equity Asset Returns
• Chart Patterns
• Extract the Chart Pattern Function
• Chart Pattern Signals
• Calculate Signals
• Capital Allocation

Market Inefficiencies: Correlation, Fundamental and Alternative

In this section, you will learn about a few types of market inefficiencies such as correlation, fundamental data, and alternative data. You will also learn how they impact the price movement and how they can be used to gain excess returns.

• Correlation, Fundamental and Alternative
• Correlation
• Usage of Correlation
• Cross-Sectional Correlation
• Fundamental Inefficiencies
• Inference for Correlation
• Insider Information
• Trading View based on Analyst Forecasts
• Golf and a Company’s Performance
• Correlation
• Calculate Average Correlation
• Additional Reading on Correlation

Market Inefficiencies: Cointegration

Cointegration is the basis of statistical arbitrage. In this section, you will learn how to implement a pairs trading strategy. You will also learn some of the traps of statistical arbitrage and how they can be avoided.

• Cointegration
• Alternative Term for Pairs Trading
• Predictive Model
• Trading the Spread Curve
• Spread Strategy Code
• Cash-Neutral Strategy
• Cointegration
• Hedge Ratio
• Cointegration
• Create a Spread
• Additional Reading on Cointegration
• Types of Market Inefficiencies

Time Series Alphas

There are several origins of alphas, with the most renowned and commonly utilized alpha being the time-series alpha. This section will assist you in producing alpha by utilizing signals along the temporal axis.

Discover the methodology of utilizing historical time series data to formulate a strategy based on the Relative Strength Index (RSI).

• Time Series Alphas
• Categories of Alpha
• Time Series Alpha
• Types of Alpha
• Problem with Independent Signals
• Number of Signals
• Positions for Time-Series Alpha
• Trading Logic
• RSI Less than 40
• Shift Returns
• PnL Curves
• Strategy vs Benchmark
• Factor in Time-Series Alpha Calculation
• RSI Strategy Logic
• Implementation of RSI Based Trading Strategy
• Calculate RSI
• Generate Signals Using RSI
• Calculate Portfolio Returns
• Additional Reading on Time Series Alphas

Live Trading on Blueshift

Discover the essential steps to implement your backtested strategy in a real trading environment. Acquire knowledge regarding the organization of the code, the diverse functions employed to formulate a strategy, and ultimately, engage in either simulated or real-time trading on Blueshift.

• Section Overview
• Live Trading Overview
• Vectorised vs Event Driven
• Process in Live Trading
• Real-Time Data Source
• Blueshift Code Structure
• Important API Methods
• Schedule Strategy Logic
• Fetch Historical Data
• Place Orders
• Backtest and Live Trade on Blueshift
• Additional Reading

Live Trading Template

This section includes a live trading strategy template that uses the RSI indicator to generate entry and exit signals. You can tweak the code by changing securities or the strategy parameters. You can also analyze the strategy’s performance in more detail.

Paper/Live Trade Using RSI

Cross-Sectional Alphas

Alphas can be generated not just with signals along the time axis, but also with signals along the instrument axis. In this section, you will learn to generate alpha by ranking assets based on their momentum along the instrument axis.

• Cross-Sectional Alpha
• Common Attribute
• Axis for Cross-Sectional Alpha
• Arrange in Order
• Indicator for Trading Signals
• Sum of Rows
• Two Ranks
• Cross-Sectional Approach
• Cross-Sectional Momentum Strategy Logic
• Cross-Sectional Momentum Strategy
• Calculate Momentum
• Backtest Cross-Sectional Momentum Strategy
• Calculate PnL
• Additional Reading on Cross-Sectional Alphas
• Paper/Live Trade Using Cross Sectional Alpha

Timing Alphas

Executing trades at optimal moments, such as specific times, hours, weekdays, or months, might potentially generate substantial excess returns in certain situations. This section will cover the significance and consequences of timing the alpha. You will additionally apply the principles in a Jupyter notebook.

• Timing Alpha
• Source of Alpha
• Daytime vs Overnight Returns
• Persistent Overnight Returns
• MA Weekday Strategy
• Advantages of Weekday Strategy
• Important Timing Events
• Timing Alphas
• Calculate Overnight Returns
• Inference of Cumulative Returns Plot
• Use of Timing of Alphas
• Additional Reading on Timing Alphas
• Most Suitable Alpha

Combinations of Alpha

Can the many categories of alphas be integrated to develop a trading strategy? Indeed, this section will provide you with knowledge on the diverse permutations of alphas. You will additionally execute a trading strategy that is centered on volatility.

• Combinations of Alpha
• Combinations of Alpha
• Alpha Combinations-I
• Annualized Volatility
• Alpha Combinations-II 
• Volatility Strategy
• Upper and Lower Limit
• Upper and Lower Limit Inference
• Volatility Based Trading Strategy
• Calculate Volatility of Stock Returns
• Backtest Volatility Based Trading Strategy with Lower Limit
• Additional Reading on Combinations of Alphas
• Things to Keep In Mind While Combining Strategies
• Paper/Live Trade Using Volatility

Finding Micro-Alphas

In order to discover micro-alphas, originality is an essential requirement, and even minor alterations to existing ideas can frequently yield significant outcomes. This section provides an introduction to the research paper titled “100 Formulaic Alphas” authored by Kakushadze in 2015. You will get knowledge about several alpha factors and apply them in a Jupyter notebook.

• Finding Micro-Alphas
• Other’s Ideas
• Alpha #3 Factor
• Alpha #3 and Alpha #57
• Ranking RSI Values
• Micro-Alphas From 101 Formulaic Alphas
• Calculate Alpha #6
• Additional Reading on Finding Micro-Alphas

Assessing Results

In order to gauge the effectiveness of your plan, it is important to conduct a comprehensive evaluation of the approach. Although it is crucial to have an instinctive understanding of the characteristics depicted on a chart, this alone is not enough for a comprehensive evaluation.

This section will elucidate the significance of amalgamating diverse indicators, enabling a comprehensive comprehension of many facets of strategy performance.

• Assessing Results
• Prerequisite for Finding Micro-Alphas
• Combination of Alphas
• Number of Metrics
• Utility of Sharpe Ratio
• Strategy Performance
• Additional Reading on Assessing Results
• Most Ideal Performance Metric

Total Profit

In this section, you will learn about total profit, which is by far the simplest and the most used performance metric. You will learn about compounded and non-compounded as well as realized and unrealised profits. You will also implement these concepts in a Jupyter notebook.

• Total Profit
• Characteristics of Total Profit
• Features of Total Profit
• Differences Between PnL Curves
• Which Strategy is Riskier?
• Reinvestment of Profits
• Realised vs Unrealised PnL
• Drawbacks of Realised PnL
• Drawbacks of Total PnL
• Information Provided by PnLs
• Limitations of Total Profit
• Strategy Comparison
• Impact of Compounded PnL
• Realised Vs Unrealised Profits
• Realised PnL of a Strategy
• Additional Reading on Total Profit

Sharpe and Sortino Ratios

The Sharpe ratio and Sortino ratios help you compare the risk-adjusted performance of different portfolios or trading strategies and determine the most feasible of them all. In this section, you will learn about the two ratios in depth and implement the same using Python.

• Sharpe and Sortino Ratios
• Risk-Adjusted Returns
• Calculate Sharpe Ratio
• Risk-Free Rate
• Exclude Risk-Free Rate
• Drawbacks of Sharpe Ratio
• Sortino Ratio
• Sharpe and Sortino Ratios using Python
• Implement the Sharpe Ratio
• Implement the Sortino Ratio
• Additional Reading on Sharpe and Sortino Ratios

Profit Factor and Drawdown

The Sharpe or Sortino ratios are not appropriate for assessing high-confidence techniques that execute infrequent but very lucrative transactions. This section will cover the profit factor, a useful indicator for identifying Alphas of this nature. Furthermore, the drawdown indicator allows us to approximate the extent to which we may experience losses at any specific moment.

• Profit Factor and Drawdown
• Profit Factor
• Compare the Profit Factor
• Drawdown of a Strategy
• Drawdown Calculation
• Maximum Drawdown Comparison
• Profit Factor and Drawdown using Python
• Implement Profit Factor
• Additional Reading on Profit Factor and Drawdown

Profit Per Trade

The profit per trade indicator provides insight into the average monetary gain or loss that can be anticipated for each individual trade. This section will provide instruction on the proper methodology for calculating profit per trade, as well as guidance on how to perform this calculation using the Python programming language.

• Profit Per Trade
• Application of Profit Per Trade
• Computing Profit Per Trade
• Profit Per Trade
• Additional Reading on Profit Per Trade

CAGR, Alpha, and Beta

This section will provide an introduction to three widely used metrics: Compound Annual Growth Rate (CAGR), Alpha, and Beta. The Compound Annual Growth Rate (CAGR) enables us to accurately assess the annual return that our approach can realistically provide. Alpha quantifies the extent to which the strategy’s return is unrelated to the benchmark.

The Beta offers us valuable information about our level of exposure to the underlying market.

• CAGR, Alpha and Beta
• Compounded or Non-compounded?
• Annualise the Sharpe Ratio
• Evaluate the Skill of a Money Manager
• Initial Backtest
• CAGR, Alpha and Beta
• Additional Reading on CAGR, Alpha and Beta

Strategy Execution

To optimize your time and resources, it is crucial to acknowledge and consider the underlying assumptions that may render certain techniques impractical, excessively expensive, or overly intricate to execute in reality. In this section, we will examine several prevalent assumptions made by traders and explore strategies for addressing them.

You will also gain knowledge of intriguing execution methods, such as the arrival price algorithm, which might potentially improve your execution performance.

• Strategy Execution
• Implicit Assumptions
• Shortcomings of Execution on Close
• Executing Large Quantities
• Slippage
• Limitation of Market-on-Close Order
• Arrival Price Algorithm
• Execution on the Open
• Order Type for Arrival Price Algorithm
• Sources of Transaction Costs
• Additional Reading on Strategy Execution

Micro-Alpha Portfolio

So far we have discussed how to research, test, evaluate and execute individual alphas. However, the great strength of the micro-alpha approach lies in the combination of many individual alphas. In this section, you will combine multiple alphas and create a combined alpha strategy.

• Combining Alphas
• Traditional Portfolio Management
• Micro-Alpha Approach
• Alphas
• Combining Alphas – I
• Generating Signals
• Combining All Micro-Alphas
• Paper/Live Trade by Combining Micro-Alphas

Portfolio Optimisation

This section will involve the analysis of different portfolio optimisation strategies, including manual optimisation and mean-variance optimisation. These techniques will be practically applied to the combined alpha portfolio.

• Portfolio Optimisation
• Rebalance the Weights
• Equal Portfolio Weights
• Efficient Frontier
• Optimisation
• Additional Reading

Advanced Alpha Mining

In this section, you will learn about more advanced alpha mining concepts, such as system parameter permutation and optimisation.

• Testing Robustness Across Parameter Space
• Testing Robustness of Strategy
• Selecting Best Parameter Sets
• Finding Best Parameter
• Possible Lookback Values
• Parameter Optimisation
• Simpson’s Paradox
• Sharpe Ratios
• Lookback Periods
• Clustering Algorithms – I
• Clustering Algorithms – II
• SPP
• Additional Reading – I
• Additional Reading – II

Machine Learning Alphas

In this section, you will learn about machine learning alphas.

• Machine Learning Alphas
• Classification
• ML Alphas

Basics of Vectorized Backtest

Backtesting can be performed using either iterative loops or in a vectorized style. Although a vectorized backtest may be intricate, the benefits in terms of execution time make it highly worthwhile. A looped backtest may require many hours to complete a single iteration, whereas the vectorized format can conduct the same backtest within a matter of minutes. This section will guide you through the process of backtesting a basic moving-average crossover technique using the vectorized format.

 

Creating a Basic Backtest

2m 34s

Factors for Setting Exit Signals

5m 

Number of Winning and Losing Trades

5m

Reason for High Number of Losing Trades

5m 

Advantage of Stop-loss and Profit-take

5m 

Implementation of Profit-take and Stop-loss

5m

Conversion of Long-Short to Long-Only Signals

5m

Creation of Vectorized Backtest

5m

Calculate the Moving Average Crossover

5m

Generating Long-Short Trading Signal

5m

Generating Long-Only Trading Signal

5m

Calculate the Cumulative Sum of Returns

5m

Calculation of Portfolio Returns

5m

Additional Reading

• Adding Vectorized Stop-loss and Profit-takes
• Impact of Profit Take and Stop Loss on Strategy
• Designing a Trading System
• Asynchronous Computing
• Distributed Computing
• Importance of Logging and Storage
• Hardware Elements of a Trading System
• Software Elements of a Trading System
• Testing and Version Control
•  Implementation of a Trading System
• Types of Servers
• Trading Logic
• Testing and Operation
• Capstone Project
• Run Codes Locally on Your Machine
• Summary

Enroll today to begin your journey of exploration and improvement in “Trading Alphas: Mining, Optimisation, and System Design By Thomas Starke – QuantInsti”

Commonly Questions:

1. Revolutionary Business Plan:

Accept that our business is what it really is! Our plan is to set up a group buy, in which the costs are split among the partners.

We buy highly sought-after classes from sales pages with this money and give entry to people who are having trouble paying for it.

Any doubts the writers may have had aside, our clients like how cheap and easy to use our services are.

2. What are the pros and cons of the legal environment?

It’s not clear whether what we’re doing is legal or not.

We do not have official permission from the course authors to resell, but this is just a matter of detail.

When we bought the course, the author didn’t say if there were any restrictions on reselling it.

This complicated legal situation is good for us and good for people who want to get great knowledge at an affordable price.

3. Quality Assurance: Getting to the main of the course that you want:

Looking into what the problem is really about: Core quality.

If you buy the course from the sales website, you can be sure that all the materials and papers you get are the same as those you would get the old-fashioned way.

But what makes us different is that we can do more than just individual study; we also resale. It’s important to note that we are not the official course providers.

This means that some special services are not included in our package:

You can’t schedule a teaching call or a meeting with the author. (or 1-1 teaching from the coaches or author)

We couldn’t get into the author’s private Facebook group or online site for you.

You are not allowed to access the author’s private club site.

There was no direct email help from the author or their teams.

Our operations are run independently, with the objective of reducing the disparity in expenses.

We can’t offer the extra services that are offered through official course outlets from official authors. We really appreciate that you understand our unique approach.

In the result, any Knowledge is crucial for the future. Thus, we actively participate in enhancing your approachability with the reasonable price.