opes-core · nitintonypaul · Jan 22, 2026 · Jan 22, 2026 · Jan 22, 2026
diff --git a/README.md b/README.md
@@ -33,6 +33,7 @@ The information provided by OPES is for educational, research and informational
 |                                          | Maximum Sharpe Ratio                               |
 | **Principled Heuristics**                | Uniform (1/N)                                      |
 |                                          | Risk Parity                                        |
+|                                          | Hierarchical Risk Parity                           |
 |                                          | Inverse Volatility                                 |
 |                                          | Softmax Mean                                       |
 |                                          | Maximum Diversification                            |
@@ -106,7 +107,7 @@ Alternatively, you are also welcome to install directly from the GitHub reposito
 
 ```bash
 git clone https://github.com/opes-core/opes.git
-cd opes
+cd opes-main
 pip install .
 ```
 
@@ -162,7 +163,7 @@ asset_data = yf.download(
 kelly_portfolio = Kelly(fraction=0.8, reg="l2", strength=0.001)
 
 # Compute portfolio weights with custom weight bounds
-kelly_portfolio.optimize(data, weight_bounds=(0.05, 0.8))
+kelly_portfolio.optimize(asset_data, weight_bounds=(0.05, 0.8))
 
 # Clean negligible allocations
 cleaned_weights = kelly_portfolio.clean_weights(threshold=1e-6)
@@ -171,7 +172,7 @@ cleaned_weights = kelly_portfolio.clean_weights(threshold=1e-6)
 print(cleaned_weights)
 ```
 
-This showcases the simplicty of the module. However there are far more diverse features you can still explore. If you're looking for more examples, preferably some of them with *"context"*, I recommend you check out the [examples](https://opes.pages.dev/examples/good_strategy/) page within the documentation.
+This showcases the simplicity of the module. However there are far more diverse features you can still explore. If you're looking for more examples, preferably some of them with *"context"*, I recommend you check out the [examples](https://opes.pages.dev/examples/good_strategy/) page within the documentation.
 
 ---
 
@@ -201,7 +202,6 @@ Also it eats up RAM like pac-man.
 These features are still in the works and may or may not appear in later updates:
 
 | **Objective Name (Category)**                    |
-| ------------------------------------------------ |
-| Hierarchical Risk Parity (Principled Heuristics) |              
+| ------------------------------------------------ |             
 | Online Newton Step (Online Learning)             | 
 | ADA-BARRONS (Online Learning)                    |
diff --git a/docs/docs/backtesting.md b/docs/docs/backtesting.md
@@ -252,8 +252,8 @@ def plot_wealth(
 OPES ships with a basic plotting utility for visualizing portfolio wealth over time.
 
 This method exists for quick inspection and debugging, not for deep performance analysis.
-It visualizes cumulative wealth for one or multiple strategies using their periodic 
-returns. It also provides a breakeven reference line and optional saving of the plot to 
+It visualizes cumulative wealth for one or multiple strategies using their periodic
+returns. It also provides a breakeven reference line and optional saving of the plot to
 a file.
 
 !!! tip "Recommendation:"

diff --git a/docs/docs/index.md b/docs/docs/index.md
@@ -23,15 +23,15 @@ OPES is a research-oriented and experimentation-focused Python module for portfo
 !!! example "Demo"
     ```python
     # Demonstration of portfolio optimization using the Kelly Criterion
-    # 'data' represents OHLCV market data grouped by ticker symbols
+    # `return_data` represents OHLCV market data grouped by ticker symbols
 
     from opes.objectives import Kelly
 
     # Initialize a Kelly portfolio with fractional exposure and L2 regularization
     kelly_portfolio = Kelly(fraction=0.8, reg="l2", strength=0.01)
 
     # Compute portfolio weights with custom bounds and clean negligible allocations
-    kelly_portfolio.optimize(data, weight_bounds=(0.05, 0.8))
+    kelly_portfolio.optimize(return_data, weight_bounds=(0.05, 0.8))
     cleaned_weights = kelly_portfolio.clean_weights(threshold=1e-6)
 
     # Output the final portfolio weights

diff --git a/docs/docs/installation.md b/docs/docs/installation.md
@@ -7,59 +7,94 @@ This page guides you through installing OPES for experimentation & research.
 !!! warning "Warning:"
 	OPES is currently under development. While it is relatively stable for experimentation, some features may change or break. Use at your own discretion and always verify results when testing.
 
-## Prerequisites
-
-- Python 3.10+ (tested up to 3.12)
-- `pip` package manager
-
 ---
 
 ## Procedure
 
-### 1. Install OPES
+Python 3.10+ is required for `opes` to run (although it *may* work on some lower versions). `opes` is tested upto Python 3.14. To install a stable release of `opes`, `pip` is recommended for convenience.
 
-You can install OPES easily via PyPI:
+### Installation
+
+You can install OPES easily via PyPI using `pip`.
 
 ```bash
 pip install opes
 ```
 
-This will fetch the latest stable release and all required dependencies.
-
-You are also welcome to install the module directly from GitHub:
+This will fetch the latest stable release and all required dependencies. Alternatively, you are also welcome to install the module directly from GitHub.
 
 ```bash
 git clone https://github.com/opes-core/opes.git
 cd opes-main
-pip install -e .
+pip install .
 ```
 
-!!! note "Note:"
-	The `-e` flag installs OPES in editable mode, so any changes you make to the source code are reflected immediately without reinstalling. This is great for developers or those tinkering with advanced features.
+You can also install in editable mode if you plan on making any changes to the source code.
+
+```bash 
+# After cloning and in the root of the project
+pip install -e .
+```
 
 ---
 
-### 2. Verify the Installation
+### Verification
 
-After installation, make sure everything works by opening Python and importing OPES:
+After installation, make sure everything works by opening Python and importing `opes`.
 
 ```python
 >>> import opes
 >>> opes.__version__
-'1.0.0'
+>>> '0.10.0' # May not be the current version but you get the idea
 ```
 
-If no errors appear, OPES is ready to use.
+You can also verify your installation by using `pip`.
+
+```bash
+pip show opes
+```
+
+If no errors appear, `opes` is ready to use.
 
 ---
 
-## Dependencies
+## Getting Started
 
-OPES requires the following Python modules:
+`opes` is designed to be minimalistic and easy to use and learn for any user. Here is an example script which implements my favorite portfolio, the Kelly Criterion.
+
+```python
+# I recommend you use yfinance for testing.
+# However for serious research, using an external, faster API would be more fruitful.
+import yfinance as yf
+
+# Importing our Kelly class
+from opes.objectives import Kelly
+
+# ---------- FETCHING DATA ----------
+# Obtaining ticker data
+# Basic yfinance stuff
+TICKERS = ["AAPL", "NVDA", "PFE", "TSLA", "BRK-B", "SHV", "TLT"]
+asset_data = yf.download(
+    tickers=TICKERS, 
+    start="2010-01-01", 
+    end="2020-01-01", 
+    group_by="ticker", 
+    auto_adjust=True
+)
+
+# ---------- OPES USAGE ----------
+# Initialize a Kelly portfolio with fractional exposure and L2 regularization
+# Fractional exposure produces less risky weights and L2 regularization contributes in penalizing concentration
+kelly_portfolio = Kelly(fraction=0.8, reg="l2", strength=0.001)
+
+# Compute portfolio weights with custom weight bounds
+kelly_portfolio.optimize(asset_data, weight_bounds=(0.05, 0.8))
+
+# Clean negligible allocations
+cleaned_weights = kelly_portfolio.clean_weights(threshold=1e-6)
+
+# Output the final portfolio weights
+print(cleaned_weights)
+```
 
-| Module name     | Minimum version | Maximum version |
-| --------------- | --------------- | --------------- |
-| **NumPy**       | 2.2.6           | < 3.0           |
-| **pandas**      | 2.3.3           | < 3.0           |
-| **SciPy**       | 1.15.2          | < 2.0           |
-| **matplotlib**  | 3.10.1          | < 4.0           |
+This showcases the simplicity of the module. However there are far more diverse features you can still explore. If you're looking for more examples, preferably some of them with *"context"*, I recommend you check out the [examples](./examples/good_strategy.md) page within the documentation.
diff --git a/docs/docs/objectives/dro.md b/docs/docs/objectives/dro.md
@@ -132,7 +132,7 @@ $$
 !!! example "Example:"
     ```python
     # Importing the dro Kelly module
-    from opes.objectives.distributionally_robust import KLRobustKelly
+    from opes.objectives import KLRobustKelly
 
     # Let this be your ticker data
     training_data = some_data()
@@ -289,7 +289,7 @@ Uses the log-sum-exp technique to solve for numerical stability.
 !!! example "Example:"
     ```python
     # Importing the dro maximum mean module
-    from opes.objectives.distributionally_robust import KLRobustMaxMean
+    from opes.objectives import KLRobustMaxMean
 
     # Let this be your ticker data
     training_data = some_data()
@@ -449,7 +449,7 @@ $$
 !!! example "Example:"
     ```python
     # Importing the dro maximum mean module
-    from opes.objectives.distributionally_robust import WassRobustMaxMean
+    from opes.objectives import WassRobustMaxMean
 
     # Let this be your ticker data
     training_data = some_data()
@@ -623,7 +623,7 @@ $$
 !!! example "Example:"
     ```python
     # Importing the dro mean-variance module
-    from opes.objectives.distributionally_robust import WassRobustMeanVariance
+    from opes.objectives import WassRobustMeanVariance
 
     # Let this be your ticker data
     training_data = some_data()
@@ -792,7 +792,7 @@ $$
 !!! example "Example:"
     ```python
     # Importing the dro minimum variance module
-    from opes.objectives.distributionally_robust import WassRobustMinVariance
+    from opes.objectives import WassRobustMinVariance
 
     # Let this be your ticker data
     training_data = some_data()