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Chapter 5: . Modeling an autoregressive process
In the previous chapter, we explored Moving Average (MA) processes, where the current value of a time series is modeled as a linear combination of past…
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Chapter 4: Modeling a moving average process
In the previous chapter, we delved into Random Walk processes, characterizing them by their non-stationarity and the absence of autocorrelation in their…
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Chapter 3: Going on a random walk
A fundamental concept in time series analysis, particularly relevant in financial markets, is the random walk process.
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Chapter 2: A naive prediction of the future
In the realm of time series forecasting, before we embark on developing complex algorithms, it’s crucial to establish a baseline model.
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Understanding time series forecasting
Time series forecasting is a specialized area of data analysis and machine learning focused on predicting future values of a variable based on its past…
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Optimizing Trading Strategies with Bayesian Optimization
Optimizing the parameters of a quantitative trading strategy is a critical step in enhancing its performance and robustness.
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Backtesting a Trading Strategy
Backtesting is a fundamental process in quantitative finance, serving as a cornerstone for validating and refining trading strategies before their…
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Quantitative Momentum Trading: From Theory to Implementation
A Step-by-Step Guide to Data Acquisition, Signal Generation, and Performance Evaluation in Python
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The Digital Arena: A Trader's Guide to Modern Electronic Markets
From Algorithms to Execution, an In-depth Look at the Mechanics of Today's High-Speed Financial Systems
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Decoding the Markets: An Introduction to Quantitative Trading
From Core Concepts to Python Code: A Practical Journey into Data-Driven Trading
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Onepagecode

June 2025

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