Merge branch 'main' into 297-save-strategies-as-part-of-the-backtest-report

Author: MDUYN

.gitignore

@@ -148,6 +148,7 @@ bumpversion.egg-info/
 *.sqlite3
 
 **/backtest_data/*
-*/backtest_reports/
+**/backtest_reports/
 **/backtest_reports/*
+**/databases/
 .vscode/

README.md

@@ -1,3 +1,21 @@
+<br/>
+<div align="center">
+   <h1><a href="https://investing-algorithm-framework.com" target="_blank">Investing Algorithm Framework</a></h4>
+</div>
+<br/>
+
+<div align="center">
+  <b>Rapidly build and deploy quantitative strategies and trading bots</b>
+</div>
+<br/>
+
+<p align="center">
+    <a target="_blank" href="https://investing-algorithm-framework.com">View Docs</a>
+    <a href="https://investing-algorithm-framework.com/Getting%20Started/installation)">Getting Started</a>
+</p>
+
+---
+
 <a href=https://investing-algorithm-framework.com><img src="https://img.shields.io/badge/docs-website-brightgreen"></a>
 [![Build](https://github.com/coding-kitties/investing-algorithm-framework/actions/workflows/publish.yml/badge.svg)](https://github.com/coding-kitties/investing-algorithm-framework/actions/workflows/publish.yml)
 [![Tests](https://github.com/coding-kitties/investing-algorithm-framework/actions/workflows/test.yml/badge.svg?branch=master)](https://github.com/coding-kitties/investing-algorithm-framework/actions/workflows/test.yml)
@@ -6,10 +24,6 @@
 <a href="https://www.reddit.com/r/InvestingBots/"><img src="https://img.shields.io/reddit/subreddit-subscribers/investingbots?style=social"></a> <br/>
 [![GitHub stars](https://img.shields.io/github/stars/coding-kitties/investing-algorithm-framework.svg?style=social&label=Star&maxAge=1)](https://github.com/SeaQL/sea-orm/stargazers/) If you like what we do, consider starring, sharing and contributing!
 
-# [Investing Algorithm Framework](https://github.com/coding-kitties/investing-algorithm-framework)
-
-The Investing Algorithm Framework is a Python framework that enables swift and elegant development of trading bots.
-
 ## Sponsors
 
 <a href="https://www.finterion.com/" target="_blank">
@@ -22,7 +36,7 @@ The Investing Algorithm Framework is a Python framework that enables swift and e
 
 ## Features and planned features:
 
-- [x] **Based on Python 3.9+**: Windows, macOS and Linux.
+- [x] **Based on Python 3.10+**: Windows, macOS and Linux.
 - [x] **Documentation**: [Documentation](https://investing-algorithm-framework.com)
 - [x] **Persistence of portfolios, orders, positions and trades**: Persistence is achieved through sqlite.
 - [x] **Limit orders**: Create limit orders for buying and selling.
@@ -33,7 +47,7 @@ The Investing Algorithm Framework is a Python framework that enables swift and e
 - [x] **Live trading**: Live trading.
 - [x] **Backtesting and performance analysis reports** [example](./examples/backtest_example)
 - [x] **Backtesting multiple algorithms with different backtest date ranges** [example](./examples/backtests_example)
-- [x] **Backtest comparison and experiments**: Compare multiple backtests and run experiments.
+- [x] **Backtesting and results evaluation**: Compare multiple backtests and run experiments. Save and load backtests. Save strategies as part of the backtest. [docs](https://investing-algorithm-framework.com/Getting%20Started/backtesting)
 - [x] **Order execution**: Currently support for a wide range of crypto exchanges through [ccxt](https://github.com/ccxt/ccxt) (Support for traditional asset brokers is planned).
 - [x] **Web API**: Rest API for interacting with your deployed trading bot
 - [x] **PyIndicators**: Works natively with [PyIndicators](https://github.com/coding-kitties/PyIndicators) for technical analysis on your Pandas and Polars dataframes.
@@ -362,4 +376,4 @@ You can pick up a task by assigning yourself to it.
 **Note** before starting any major new feature work, *please open an issue describing what you are planning to do*.
 This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
 
-**Important:** Always create your feature or hotfix against the `develop` branch, not `main`.
+**Important:** Always create your feature or hotfix against the `develop` branch, not `main`.

__init__.py

@@ -1 +1 @@
-__version__='v6.1.0'
+__version__='v6.2.0'

examples/backtest_example/run_backtest.py

@@ -2,13 +2,12 @@
 from datetime import datetime
 import logging.config
 from datetime import datetime, timedelta
-
-
 from investing_algorithm_framework import (
     CCXTOHLCVMarketDataSource, CCXTTickerMarketDataSource, PortfolioConfiguration, create_app, pretty_print_backtest, BacktestDateRange, TimeUnit, TradingStrategy, OrderSide, DEFAULT_LOGGING_CONFIG, Context
 )
 
-import tulipy as ti
+from pyindicators import ema, is_crossover, is_above, is_below, is_crossunder
+
 
 logging.config.dictConfig(DEFAULT_LOGGING_CONFIG)
 
@@ -20,22 +19,22 @@
 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
+It uses pyindicators 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/
+https://github.com/coding-kitties/PyIndicators or go directly
+to the pypi page: https://pypi.org/project/PyIndicators/
 """
 
 bitvavo_btc_eur_ohlcv_2h = CCXTOHLCVMarketDataSource(
-    identifier="BTC/EUR-ohlcv",
+    identifier="BTC/EUR-ohlcv-2h",
     market="BINANCE",
     symbol="BTC/EUR",
     time_frame="2h",
     window_size=200
 )
 bitvavo_dot_eur_ohlcv_2h = CCXTOHLCVMarketDataSource(
-    identifier="DOT/EUR-ohlcv",
+    identifier="DOT/EUR-ohlch-2h",
     market="BINANCE",
     symbol="DOT/EUR",
     time_frame="2h",
@@ -54,68 +53,73 @@
     backtest_time_frame="2h",
 )
 
-
-def is_below_trend(fast_series, slow_series):
-    return fast_series[-1] < slow_series[-1]
-
-
-def is_above_trend(fast_series, slow_series):
-    return fast_series[-1] > slow_series[-1]
-
-
-def is_crossover(fast, slow):
-    """
-    Expect df to have columns: Date, ma_<period_one>, ma_<period_two>.
-    With the given date time it will check if the ma_<period_one> is a
-    crossover with the ma_<period_two>
-    """
-    return fast[-2] <= slow[-2] and fast[-1] > slow[-1]
-
-
-def is_crossunder(fast, slow):
+class CrossOverStrategy(TradingStrategy):
     """
-    Expect df to have columns: Date, ma_<period_one>, ma_<period_two>.
-    With the given date time it will check if the ma_<period_one> is a
-    crossover with the ma_<period_two>
+    A simple trading strategy that uses EMA crossovers to generate buy and
+    sell signals. The strategy uses a 50-period EMA and a 100-period EMA
+    to detect golden and death crosses. It also uses a 200-period EMA to
+    determine the overall trend direction. The strategy trades BTC/EUR
+    on a 2-hour timeframe. The strategy is designed to be used with the
+    Investing Algorithm Framework and uses the PyIndicators library
+    to calculate the EMAs and crossover signals.
+
+    The strategy uses a trailing stop loss and take profit to manage
+    risk. The stop loss is set to 5% below the entry price and the
+    take profit is set to 10% above the entry price. The stop loss and
+    take profit are both trailing, meaning that they will move up
+    with the price when the price goes up.
     """
-    return fast[-2] >= slow[-2] and fast[-1] < slow[-1]
-
-
-class CrossOverStrategy(TradingStrategy):
     time_unit = TimeUnit.HOUR
     interval = 2
-    market_data_sources = [
-        bitvavo_dot_eur_ticker,
-        bitvavo_btc_eur_ticker,
-        bitvavo_dot_eur_ohlcv_2h,
-        bitvavo_btc_eur_ohlcv_2h
-    ]
-    symbols = ["BTC/EUR", "DOT/EUR"]
-    fast = 21
-    slow = 75
-    trend = 150
-    stop_loss_percentage = 7
+    symbol_pairs = ["BTC/EUR"]
+    market_data_sources = [bitvavo_btc_eur_ohlcv_2h, bitvavo_btc_eur_ticker]
+    fast = 50
+    slow = 100
+    trend = 200
+    stop_loss_percentage = 2
+    stop_loss_sell_size = 50
+    take_profit_percentage = 8
+    take_profit_sell_size = 50
 
     def apply_strategy(self, context: Context, market_data):
 
-        for symbol in self.symbols:
-            target_symbol = symbol.split('/')[0]
+        for pair in self.symbol_pairs:
+            symbol = pair.split('/')[0]
 
-            if context.has_open_orders(target_symbol):
+            # Don't trade if there are open orders for the symbol
+            # This is important to avoid placing new orders while there are
+            # existing orders that are not yet filled
+            if context.has_open_orders(symbol):
                 continue
 
-            df = market_data[f"{symbol}-ohlcv"]
-            ticker_data = market_data[f"{symbol}-ticker"]
-            fast = ti.sma(df['Close'].to_numpy(), self.fast)
-            slow = ti.sma(df['Close'].to_numpy(), self.slow)
-            trend = ti.sma(df['Close'].to_numpy(), self.trend)
-            price = ticker_data["bid"]
-
-            if not context.has_position(target_symbol) \
-                    and is_crossover(fast, slow) \
-                    and is_above_trend(fast, trend):
+            ohlvc_data = market_data[f"{pair}-ohlcv-2h"]
+            # ticker_data = market_data[f"{symbol}-ticker"]
+            # Add fast, slow, and trend EMAs to the data
+            ohlvc_data = ema(
+                ohlvc_data,
+                source_column="Close",
+                period=self.fast,
+                result_column=f"ema_{self.fast}"
+            )
+            ohlvc_data = ema(
+                ohlvc_data,
+                source_column="Close",
+                period=self.slow,
+                result_column=f"ema_{self.slow}"
+            )
+            ohlvc_data = ema(
+                ohlvc_data,
+                source_column="Close",
+                period=self.trend,
+                result_column=f"ema_{self.trend}"
+            )
+
+            price = ohlvc_data["Close"][-1]
+
+            if not context.has_position(symbol) \
+                    and self._is_buy_signal(ohlvc_data):
                 order = context.create_limit_order(
-                    target_symbol=target_symbol,
+                    target_symbol=symbol,
                     order_side=OrderSide.BUY,
                     price=price,
                     percentage_of_portfolio=25,
@@ -125,31 +129,49 @@ def apply_strategy(self, context: Context, market_data):
                 context.add_stop_loss(
                     trade=trade,
                     trade_risk_type="trailing",
-                    percentage=5,
-                    sell_percentage=50
+                    percentage=self.stop_loss_percentage,
+                    sell_percentage=self.stop_loss_sell_size
                 )
                 context.add_take_profit(
                     trade=trade,
-                    percentage=5,
+                    percentage=self.take_profit_percentage,
                     trade_risk_type="trailing",
-                    sell_percentage=50
-                )
-                context.add_take_profit(
-                    trade=trade,
-                    percentage=10,
-                    trade_risk_type="trailing",
-                    sell_percentage=20
+                    sell_percentage=self.take_profit_sell_size
                 )
 
-            if context.has_position(target_symbol) \
-                    and is_below_trend(fast, slow):
+            if context.has_position(symbol) \
+                    and self._is_sell_signal(ohlvc_data):
                 open_trades = context.get_open_trades(
-                    target_symbol=target_symbol
+                    target_symbol=symbol
                 )
 
                 for trade in open_trades:
                     context.close_trade(trade)
 
+    def _is_sell_signal(self, data):
+        return is_crossunder(
+            data,
+            first_column=f"ema_{self.fast}",
+            second_column=f"ema_{self.slow}",
+            number_of_data_points=2
+        ) and is_below(
+            data,
+            first_column=f"ema_{self.fast}",
+            second_column=f"ema_{self.trend}",
+        )
+
+    def _is_buy_signal(self, data):
+        return is_crossover(
+            data=data,
+            first_column=f"ema_{self.fast}",
+            second_column=f"ema_{self.slow}",
+            number_of_data_points=2
+        ) and is_above(
+            data=data,
+            first_column=f"ema_{self.fast}",
+            second_column=f"ema_{self.trend}",
+        )
+
 
 app = create_app(name="GoldenCrossStrategy")
 app.add_strategy(CrossOverStrategy)
@@ -161,9 +183,7 @@ def apply_strategy(self, context: Context, market_data):
 # Add a portfolio configuration of 400 euro initial balance
 app.add_portfolio_configuration(
     PortfolioConfiguration(
-        market="BINANCE",
-        trading_symbol="EUR",
-        initial_balance=400,
+        market="BINANCE", trading_symbol="EUR", initial_balance=400,
     )
 )
 
@@ -175,7 +195,9 @@ def apply_strategy(self, context: Context, market_data):
         end_date=end_date
     )
     start_time = time.time()
-    backtest_report = app.run_backtest(backtest_date_range=date_range)
+    backtest_report = app.run_backtest(
+        backtest_date_range=date_range, save_in_memory_strategies=True
+    )
     pretty_print_backtest(backtest_report)
     end_time = time.time()
     print(f"Execution Time: {end_time - start_time:.6f} seconds")

examples/bitvavo_trading_bot.py

@@ -23,6 +23,8 @@
 # Define your market credential for bitvavo, keys are read from .env file
 bitvavo_market_credential = MarketCredential(
     market="bitvavo",
+    api_key="your_api_key",
+    secret_key="your_secret_key"
 )
 # Define your market data sources for coinbase
 bitvavo_btc_eur_ohlcv_2h = CCXTOHLCVMarketDataSource(