289 include unrealized gains in backtesting result

.github/workflows/test.yml

@@ -41,7 +41,10 @@ jobs:
       fail-fast: true
       matrix:
         os: [ "ubuntu-latest", "macos-latest" ]
-        python-version: [ "3.8", "3.9", "3.10", "3.11" ]
+        python-version: [ "3.9", "3.10", "3.11" ]
+    defaults:
+      run:
+        shell: bash
     runs-on: ${{ matrix.os }}
     steps:
       #----------------------------------------------
@@ -99,7 +102,7 @@ jobs:
       #----------------------------------------------
       - name: Run tests
         run: |
-          source .venv/bin/activate
+          source $VENV
           coverage run -m unittest discover -s tests
 #      #----------------------------------------------
 #      #             upload coverage stats

README.md

@@ -37,7 +37,6 @@ Features:
 The following algorithm connects to binance and buys BTC every 5 seconds. It also exposes an REST API that allows you to interact with the algorithm.
 
 ```python
-import logging
 import logging.config
 
 from investing_algorithm_framework import create_app, PortfolioConfiguration, \
@@ -61,7 +60,6 @@ bitvavo_btc_eur_ticker = CCXTTickerMarketDataSource(
     symbol="BTC/EUR",
 )
 app = create_app()
-algorithm = Algorithm()
 # Bitvavo market credentials are read from .env file
 app.add_market_credential(MarketCredential(market="bitvavo"))
 app.add_portfolio_configuration(
@@ -71,18 +69,16 @@ app.add_portfolio_configuration(
         initial_balance=400
     )
 )
-app.add_algorithm(algorithm)
 
 # Run every two hours and register the data sources
-@algorithm.strategy(
+@app.strategy(
     time_unit=TimeUnit.HOUR,
     interval=2,
     market_data_sources=[bitvavo_btc_eur_ticker, bitvavo_btc_eur_ohlcv_2h]
 )
 def perform_strategy(algorithm: Algorithm, market_data: dict):
     # Access the data sources with the indentifier
     polars_df = market_data["BTC-ohlcv"]
-
     # Convert the polars dataframe to a pandas dataframe
     pandas_df = polars_df.to_pandas()
     ticker_data = market_data["BTC-ticker"]
@@ -93,18 +89,21 @@ def perform_strategy(algorithm: Algorithm, market_data: dict):
     closed_trades = algorithm.get_closed_trades()
 
     # Create a buy oder
-    algorithm.create_limit_order(
+    order = algorithm.create_limit_order(
         target_symbol="BTC/EUR",
         order_side="buy",
         amount=0.01,
         price=ticker_data["ask"],
     )
+    trade = algorithm.get_trade(order_id=order.id)
+    algorithm.add_trailing_stop_loss(trade=trade, percentage=5)
 
     # Close a trade
-    algorithm.close_trade(trades[0].id)
+    algorithm.close_trade(trade=trade)
 
     # Close a position
-    algorithm.close_position(positions[0].get_symbol())
+    position = algorithm.get_position(symbol="BTC/EUR")
+    algorithm.close_position(position)
 
 if __name__ == "__main__":
     app.run()
@@ -116,7 +115,14 @@ if __name__ == "__main__":
 
 The framework also supports backtesting and performing backtest experiments. After a backtest, you can print a report that shows the performance of your trading bot.
 
-To run a single backtest you can use the example code that can be found [here](./examples/backtest).
+To run a single backtest you can use the example code that can be found [here](./examples/backtest_example). Simply run:
+
+> Its assumed here that you have cloned the repository, installed the framework and
+> are in the root of the project.
+
+```bash
+python examples/backtest_example/run_backtest.py
+```
 
 ### Backtesting report