Add aws deployment mode (#322)

* Add aws deployment mode

* Fix string formatting

Author: MDUYN

README.md

@@ -73,18 +73,30 @@ This will create:
 ---
 
 ## 📈 Example: A Simple Trading Bot
-The following example connects to Binance and buys BTC every 2 hours.
+The following example trading bot implements a simple moving average strategy.
+The strategy will use data from bitvavo exchange and will calculate 
+the 20, 50 and 100 period exponential moving averages (EMA) and the 
+14 period relative strength index (RSI).
+
+> This example uses [PyIndicators](https://github.com/coding-kitties/pyindicators) for technical analysis.
+> This dependency is not part of the framework, but is used to perform technical analysis on the dataframes.
+> You can install it using pip: pip install pyindicators.
+
+```bash
 
 ```python
 import logging.config
 from dotenv import load_dotenv
 
+from pyindicators import ema, rsi
+
 from investing_algorithm_framework import create_app, TimeUnit, Context, BacktestDateRange, \
     CCXTOHLCVMarketDataSource, CCXTTickerMarketDataSource, DEFAULT_LOGGING_CONFIG, \
-    TradingStrategy, SnapshotInterval
+    TradingStrategy, SnapshotInterval, convert_polars_to_pandas
 
 load_dotenv()
 logging.config.dictConfig(DEFAULT_LOGGING_CONFIG)
+logger = logging.getLogger(__name__)
 
 # OHLCV data for candles
 bitvavo_btc_eur_ohlcv_2h = CCXTOHLCVMarketDataSource(
@@ -111,21 +123,58 @@ class MyStrategy(TradingStrategy):
     data_sources = [bitvavo_btc_eur_ohlcv_2h, bitvavo_btc_eur_ticker]
 
     def run_strategy(self, context: Context, market_data):
-        # Access the data sources with the indentifier
-        polars_df = market_data["BTC-ohlcv"]
-        ticker_data = market_data["BTC-ticker"]
-        unallocated_balance = context.get_unallocated()
-        positions = context.get_positions()
-        trades = context.get_trades()
-        open_trades = context.get_open_trades()
-        closed_trades = context.get_closed_trades()
+
+        if context.has_open_orders(target_symbol="BTC"):
+            logger.info("There are open orders, skipping strategy iteration.")
+            return
+
+        print(market_data)
+
+        data = convert_polars_to_pandas(market_data["BTC-ohlcv"])
+        data = ema(data, source_column="Close", period=20, result_column="ema_20")
+        data = ema(data, source_column="Close", period=50, result_column="ema_50")
+        data = ema(data, source_column="Close", period=100, result_column="ema_100")
+        data = rsi(data, source_column="Close", period=14, result_column="rsi_14")
+
+        if context.has_position("BTC") and self.sell_signal(data):
+            context.create_limit_sell_order(
+                "BTC", percentage_of_position=100, price=data["Close"].iloc[-1]
+            )
+            return
+
+        if not context.has_position("BTC") and self.buy_signal(data):
+            context.create_limit_buy_order(
+                "BTC", percentage_of_portfolio=20, price=data["Close"].iloc[-1]
+            )
+            return
+
+    def buy_signal(self, data):
+        if len(data) < 100:
+            return False
+        last_row = data.iloc[-1]
+        if last_row["ema_20"] > last_row["ema_50"] and last_row["ema_50"] > last_row["ema_100"]:
+            return True
+        return False
+
+    def sell_signal(self, data):
+
+        if data["ema_20"].iloc[-1] < data["ema_50"].iloc[-1] and \
+           data["ema_20"].iloc[-2] >= data["ema_50"].iloc[-2]:
+            return True
+
+        return False
 
 date_range = BacktestDateRange(
-    start_date="2023-08-24 00:00:00",
-    end_date="2023-12-02 00:00:00"
+    start_date="2023-08-24 00:00:00", end_date="2023-12-02 00:00:00"
 )
-backtest_report = app.run_backtest(backtest_date_range=date_range, initial_amount=100, snapshot_interval=SnapshotInterval.STRATEGY_ITERATION)
-backtest_report.show()
+app.add_strategy(MyStrategy)
+
+if __name__ == "__main__":
+    # Run the backtest with a daily snapshot interval for end-of-day granular reporting
+    backtest_report = app.run_backtest(
+        backtest_date_range=date_range, initial_amount=100, snapshot_interval=SnapshotInterval.STRATEGY_ITERATION
+    )
+    backtest_report.show()
 ```
 
 > You can find more examples [here](./examples) folder.