Fix report formatting

Author: MDUYN

docusaurus/docs/Contributing Guide/contributing.md

@@ -40,4 +40,54 @@ to the maintainers in the [Discussions](https://github.com/coding-kitties/invest
 
 8. Create a Pull Request inline with the [Pull Request Template](https://github.com/coding-kitties/investing-algorithm-framework/blob/main/docs/PULL_REQUEST_TEMPLATE.md).
 9. Wait for the maintainers to review your PR. Make changes if requested.
-10. Once your PR is approved, it will be merged into the main branch.
+10. Once your PR is approved, it will be merged into the main branch.
+
+## Architecture Overview
+
+### Onion layered architecture
+
+The Investing Algorithm Framework follows an Onion Architecture pattern, which promotes separation of concerns and testability. The core of the architecture consists of:
+- **Domain Layer**: Contains the core business logic and domain entities. This layer is independent of any external frameworks or libraries.
+- **Application Layer**: Contains application services that orchestrate the domain logic. It interacts with the domain layer and provides a clean API for the outer layers.
+- **Infrastructure Layer**: Contains implementations of external services, such as data sources, APIs, and other integrations. This layer depends on the application layer but not the other way around.
+- **Service layer**: Contains the service classes that provide a higher-level API for the application layer. It is responsible for coordinating the interactions between the application layer and the infrastructure layer.
+
+The architecture looks as follows:
+
+```bash
+- investing_algorithm_framework
+  ├── domain
+  │   ├── models
+  │   ├── metrics
+  │   ├── exceptions
+  │   ├── constants  
+  │   └── interfaces
+  ├── services
+  │   ├── services
+  │   └── use_cases
+  ├── infrastructure
+  │   ├── data_sources
+  │   └── external_services
+  ├── APP
+  │   ├── app.py
+  │   ├── config.py
+  |   └── logger.py
+  └── service_layer
+```
+
+### Dataframes
+The framework relies heavily on dataframe operations and supports both Pandas and Polars. However, internally we always use Polars dataframes for performance reasons.
+Therefore, if a dataframe is passed as a parameter to a function, you can safely assume that it's a polars dataframe.
+
+Polars is generally faster than Pandas for handling datetime operations — especially on large datasets — due to:
+* Its Rust-based backend (highly optimized)
+* Better multi-threaded execution
+* More efficient memory handling
+
+#### Datetime handling
+Each timeseries dataframe will have its Datetime column timezone set to UTC. This is done to ensure consistency across different data sources 
+and to avoid issues with timezone conversions. When working with datetime columns, always ensure that you are aware of the timezone and convert it to UTC if necessary.
+
+Also, it's often the case the exchanges and brokers use UTC as the timezone for their data. For example, The CCXT documentation clearly states:
+
+> Timestamps returned by fetchOHLCV() and other market data methods are in milliseconds since Epoch in UTC.

examples/backtest_example/resources/backtest_data/OHLCV_BTC-EUR_BINANCE_2h_2023-08-07-06-00_2023-12-02-00-00.csv

@@ -1,5 +0,0 @@
-from .strategy_v3 import CrossOverStrategyV3
-
-__all__ = [
-    "CrossOverStrategyV3",
-]

examples/backtest_example/resources/backtest_data/TICKER_BTC-EUR_BINANCE_2023-08-23-22-00_2023-12-02-00-00.csv

@@ -1,10 +0,0 @@
-# Trading bot strategies series
-This directory forms a list of example quantitative algorithms for 
-implemented in Python with the Investing Algorithm Framework. 
-The goal is to create a set of jupyter notebooks that can used as a reference 
-for developing your own strategies. 
-
-
-## Strategies 
-1. [SMA_CROSSOVER: A strategy that uses Simple Moving Average Crossover](simple_moving_average_crossover.ipynb)
-2. ADX_RSI.ipynb: A strategy that uses Average Directional Index and RSI (adx_rsi.ipynb)

examples/backtest_example/strategies/strategy_v3/__init__.py

@@ -2,7 +2,7 @@
     TradingStrategy, StatelessAction, Task, AppHook, Context, \
     add_html_report, add_metrics, BacktestReport, \
     BacktestReportsEvaluation, pretty_print_trades, pretty_print_positions, \
-    pretty_print_orders, pretty_print_backtest
+    pretty_print_orders, pretty_print_backtest, select_backtest_date_ranges
 from investing_algorithm_framework.domain import ApiException, \
     TradingDataType, TradingTimeFrame, OrderType, OperationalException, \
     OrderStatus, OrderSide, TimeUnit, TimeInterval, Order, Portfolio, \
@@ -93,5 +93,6 @@
     "add_html_report",
     "AWSS3StorageStateHandler",
     "AWS_S3_STATE_BUCKET_NAME",
-    "AWS_LAMBDA_LOGGING_CONFIG"
+    "AWS_LAMBDA_LOGGING_CONFIG",
+    'select_backtest_date_ranges'
 ]

examples/backtest_report.html

@@ -8,6 +8,7 @@
 from .reporting import add_html_report, add_metrics, \
     BacktestReport, pretty_print_backtest, BacktestReportsEvaluation, \
     pretty_print_trades, pretty_print_positions, pretty_print_orders
+from .analysis import select_backtest_date_ranges
 
 
 __all__ = [
@@ -26,5 +27,6 @@
     "BacktestReportsEvaluation",
     "pretty_print_trades",
     "pretty_print_positions",
-    "pretty_print_orders"
+    "pretty_print_orders",
+    "select_backtest_date_ranges"
 ]