Update personal info and fix typos

_config.yml

@@ -8,9 +8,9 @@
 
 # Basic Site Settings
 locale                   : "en-US"
-title                    : "Robert Bach"
+title                    : "QuantFin"
 title_separator          : ""
-name                     : &name "Robert Bach"
+name                     : &name "QuantFin"
 description              : &description "Robert's personal website"
 url                      : https://quantfin.net # the base hostname & protocol for your site e.g. "https://mmistakes.github.io"
 baseurl                  : "" # the subpath of your site, e.g. "/blog"
@@ -24,7 +24,7 @@ author:
   avatar           : "profile_picture.jpg"
   name             : "Robert Bach"
   pronouns         : # example: "she/her"  
-  bio              : "I want to apply my mathematical skills in machine learning algorithm development, portfolio return optimization, and trading derivatives."
+  bio              : "I am a Quantitative Researcher, specialized in Algorithmic Trading and Portfolio Management. If you are interested in mathematics and programing, please connect with me."
   location         : "Bangkok City"
   employer         : 
   uri              : # URL

_posts/2024-08-28-ohlc-resampling.md

@@ -10,13 +10,11 @@ tags:
 
 In algorithmic trading, the accuracy of backtesting can make or break a strategy. The ability to simulate how your trading algorithm would have performed historically is crucial for refining and optimizing your approach. A key aspect of this process is how time series data, particularly OHLC (Open, High, Low, Close) data, is handled. While pandas provides a built-in method for resampling, it often falls short when dealing with the unique demands of financial data. In this post, we’ll explore a custom OHLC resampling function that offers greater precision, allowing for more reliable backtesting results.
 
-The Limitations of Pandas’ OHLC Resampling
-======
+## The Limitations of Pandas’ OHLC Resampling
 
 Pandas is a powerful tool for data manipulation, widely used in the data science community. However, its built-in `resample()` method, while convenient, can be problematic when dealing with financial data. Traditional resampling methods may not correctly capture the nuances of OHLC data, particularly when dealing with irregular time intervals, missing data, or the specific needs of different timeframes (e.g., hourly vs. daily). These limitations can lead to inaccurate calculations of open, high, low, and close values, which in turn can skew backtesting results.
 
-Introducing the Custom OHLC Resampling Function
-======
+## Introducing the Custom OHLC Resampling Function
 
 To overcome these limitations, I’ve developed a custom resampling function tailored specifically for OHLC data. This function is designed to respect the intricacies of financial time series, ensuring that each resampled interval accurately reflects the market’s behavior during that period. Here’s the function:
 
@@ -68,8 +66,7 @@ def resample_ohlc(dataframe, time_delta):
     return resampled_data
 ```
 
-Why This Approach Is Superior
-======
+## Why This Approach Is Superior
 
 Unlike the default pandas resampling method, this custom function directly addresses the issues commonly encountered with financial data. Here’s how:
 
@@ -83,8 +80,6 @@ These enhancements ensure that your backtesting process is based on data that tr
 
 To see the impact of this function, imagine backtesting a strategy on daily OHLC data. Using pandas’ built-in method might miss subtle but significant market movements, leading to a distorted view of your strategy’s performance. With the custom resampling function, however, every tick is accounted for, ensuring that the backtest accurately represents what would have happened in a real trading environment. This level of precision is invaluable for refining your strategies and gaining confidence before deploying them in live markets.
 
-Conclusion
-======
+## Conclusion
 
 In the high-stakes world of algorithmic trading, precision is key. The custom OHLC resampling function provided here offers a more accurate and reliable way to handle time series data, enabling more trustworthy backtesting results. By replacing the standard pandas method with this tailored approach, you can ensure that your strategies are tested against data that truly reflects market realities. As you refine your trading algorithms, this tool will be an invaluable asset in your pursuit of success.
-------

_posts/2024-08-29-devdocs.md

@@ -6,23 +6,19 @@ tags:
   - software development
   - python
 ---
-
 As a data scientist and quant developer, I constantly juggle between various libraries and frameworks. Scikit-learn, Pandas, and NumPy are the pillars of my work, whether I’m building predictive models, analyzing financial data, or optimizing trading strategies. However, keeping track of the vast amount of documentation for each library used to slow me down. That’s where devdocs.io comes in, and it has completely transformed my workflow.
 
-The Challenges of Managing Multiple Libraries
-======
+## The Challenges of Managing Multiple Libraries
 
 Working with multiple libraries is an everyday necessity in the fields of data science and quantitative finance. Whether it's manipulating data with Pandas, performing complex mathematical operations with NumPy, or implementing machine learning models with scikit-learn, each library comes with its own set of documentation. Traditionally, navigating this vast landscape meant keeping numerous tabs open, constantly switching between them, and struggling to remember where specific functions or methods were documented. This multitasking often led to lost focus and inefficiencies, affecting the speed and quality of work.
 
-What is DevDocs.io?
-======
+## What is DevDocs.io?
 
 [DevDocs.io](https://devdocs.io) is an open-source project that aggregates documentation for a multitude of programming languages, libraries, and frameworks into one simple, easy-to-navigate interface. It combines simplicity with power, allowing you to search through all the necessary docs in real time. The tool is particularly useful for developers working in data science and quantitative finance, where speed and accuracy are crucial.
 
 ![Accessing multiple documents](https://quantfin.net/images/blogs/multi-docs.gif)
 
-How DevDocs.io Has Transformed My Workflow
-======
+## How DevDocs.io Has Transformed My Workflow
 
 Since incorporating DevDocs.io into my workflow, the difference has been night and day. Here’s how it has revolutionized my work:
 
@@ -32,8 +28,7 @@ Since incorporating DevDocs.io into my workflow, the difference has been night a
 
 - **Offline Access**: DevDocs even offers an offline mode, which means I can access the documentation even when I’m not connected to the internet. This has been a lifesaver during long flights or when working in areas with spotty connectivity.
 
-Why DevDocs.io Is a Game-Changer for Data Scientists and Quant Developers
-======
+## Why DevDocs.io Is a Game-Changer for Data Scientists and Quant Developers
 
 The impact of DevDocs.io on my workflow extends beyond just convenience. Here’s why it’s a must-have tool:
 
@@ -45,8 +40,6 @@ The impact of DevDocs.io on my workflow extends beyond just convenience. Here’
 
 ![Offline mode](https://quantfin.net/images/blogs/offline-mode.gif)
 
-Conclusion
-======
+## Conclusion
 
 In the fast-paced world of data science and quantitative finance, every second counts. DevDocs.io has become an indispensable tool in my toolkit, streamlining my coding process and enabling me to work more efficiently. By centralizing all the documentation I need in one accessible location, DevDocs has not only saved me time but also enhanced the quality of my work. If you’re not using it yet, I highly recommend giving it a try—you’ll be amazed at how much time you can save!
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