Add examples

Author: MDUYN

.vscode/settings.json

@@ -0,0 +1,11 @@
+{
+    "python.testing.unittestArgs": [
+        "-v",
+        "-s",
+        "./tests",
+        "-p",
+        "test*.py"
+    ],
+    "python.testing.pytestEnabled": false,
+    "python.testing.unittestEnabled": true
+}

README.md

@@ -32,6 +32,9 @@ Features:
 * Stateless running for cloud function deployments
 * Polars dataframes support out of the box for fast data processing [pola.rs](https://pola.rs/)
 
+Additional features:
+* Indicators (python >= 3.10 required): Set of indicators that can be used in your trading bot. You can donwload the package with `pip install investing-algorithm-framework[indicators]` or `poetry add investing-algorithm-framework[indicators]]`
+ 
 ## Example implementation
 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.

examples/backtest_example/algorithm/strategy.py

@@ -9,16 +9,17 @@
 This strategy is based on the golden cross strategy. It will buy when the
 fast moving average crosses the slow moving average from below. It will sell
 when the fast moving average crosses the slow moving average from above.
-
-The strategy will also check if the fast moving average is above the trend
-moving average. If it is not above the trend moving average it will not buy.
+The strategy will also check if the fast moving average is above the trend 
+moving average. If it is not above the trend moving average it will not buy. 
 
 It uses tulipy indicators to calculate the metrics. You need to 
 install this library in your environment to run this strategy.
 You can find instructions on how to install tulipy here:
 https://tulipindicators.org/ or go directly to the pypi page:
 https://pypi.org/project/tulipy/
 """
+
+
 def is_below_trend(fast_series, slow_series):
     return fast_series[-1] < slow_series[-1]
 
@@ -87,7 +88,7 @@ def apply_strategy(self, algorithm: Algorithm, market_data):
                 )
 
             if algorithm.has_position(target_symbol) \
-                and is_below_trend(fast, slow):
+                    and is_below_trend(fast, slow):
                 open_trades = algorithm.get_open_trades(
                     target_symbol=target_symbol
                 )