Fix issue with data sources parameters passing

README.md

@@ -19,7 +19,7 @@ The Investing Algorithm Framework is a Python framework that enables swift and e
 
 Features:
 
-* Indicators module: A collection of indicators and utility functions that can be used in your trading strategies. 
+* Indicators module: A collection of indicators and utility functions that can be used in your trading strategies.
 * Order execution and tracking
 * Broker and exchange connections through [ccxt](https://github.com/ccxt/ccxt)
 * Backtesting and performance analysis reports [example](./examples/backtest_example)
@@ -41,8 +41,8 @@ from investing_algorithm_framework import create_app, PortfolioConfiguration, \
     RESOURCE_DIRECTORY, TimeUnit, CCXTOHLCVMarketDataSource, Algorithm, \
     CCXTTickerMarketDataSource, MarketCredential, SYMBOLS
 
-# Define the symbols you want to trade for optimization, otherwise the 
-# algorithm will check if you have orders and balances on all available 
+# Define the symbols you want to trade for optimization, otherwise the
+# algorithm will check if you have orders and balances on all available
 # symbols on the market
 symbols = ["BTC/EUR"]
 
@@ -52,6 +52,13 @@ config = {
     SYMBOLS: symbols
 }
 
+state_manager = AzureBlobStorageStateManager(
+    account_name="<your account name>",
+    account_key="<your account key>",
+    container_name="<your container name>",
+    blob_name="<your blob name>",
+)
+
 # Define market data sources
 # OHLCV data for candles
 bitvavo_btc_eur_ohlcv_2h = CCXTOHLCVMarketDataSource(
@@ -67,7 +74,11 @@ bitvavo_btc_eur_ticker = CCXTTickerMarketDataSource(
     market="BITVAVO",
     symbol="BTC/EUR",
 )
-app = create_app(config=config)
+app = create_app(
+    config=config,
+    sync_portfolio=True,
+    state_manager=state_manager
+)
 algorithm = Algorithm()
 app.add_market_credential(MarketCredential(
     market="bitvavo",
@@ -85,16 +96,16 @@ app.add_algorithm(algorithm)
 
 @algorithm.strategy(
     # Run every two hours
-    time_unit=TimeUnit.HOUR, 
-    interval=2, 
+    time_unit=TimeUnit.HOUR,
+    interval=2,
     # Specify market data sources that need to be passed to the strategy
     market_data_sources=[bitvavo_btc_eur_ticker, bitvavo_btc_eur_ohlcv_2h]
 )
 def perform_strategy(algorithm: Algorithm, market_data: dict):
     # By default, ohlcv data is passed as polars df in the form of
-    # {"<identifier>": <dataframe>}  https://pola.rs/, 
+    # {"<identifier>": <dataframe>}  https://pola.rs/,
     # call to_pandas() to convert to pandas
-    polars_df = market_data["BTC-ohlcv"]  
+    polars_df = market_data["BTC-ohlcv"]
     print(f"I have access to {len(polars_df)} candles of ohlcv data")
 
     # Ticker data is passed as {"<identifier>": <ticker dict>}
@@ -104,21 +115,21 @@ def perform_strategy(algorithm: Algorithm, market_data: dict):
     trades = algorithm.get_trades()
     open_trades = algorithm.get_open_trades()
     closed_trades = algorithm.get_closed_trades()
-    
-    # Create a buy oder 
+
+    # Create a buy oder
     algorithm.create_limit_order(
         target_symbol="BTC/EUR",
         order_side="buy",
         amount=0.01,
         price=ticker_data["ask"],
     )
-    
+
     # Close a trade
     algorithm.close_trade(trades[0].id)
-    
+
     # Close a position
     algorithm.close_position(positions[0].get_symbol())
-    
+
 if __name__ == "__main__":
     app.run()
 ```
@@ -136,14 +147,14 @@ To run a single backtest you can use the example code that can be found [here](.
 You can use the ```pretty_print_backtest``` function to print a backtest report.
 For example if you run the [moving average example trading bot](./examples/crossover_moving_average_trading_bot)
 you will get the following backtesting report:
- 
+
 ```bash
 
                   :%%%#+-          .=*#%%%        Backtest report
                   *%%%%%%%+------=*%%%%%%%-       ---------------------------
                   *%%%%%%%%%%%%%%%%%%%%%%%-       Start date: 2023-08-24 00:00:00
                   .%%%%%%%%%%%%%%%%%%%%%%#        End date: 2023-12-02 00:00:00
-                   #%%%####%%%%%%%%**#%%%+        Number of days: 100 
+                   #%%%####%%%%%%%%**#%%%+        Number of days: 100
              .:-+*%%%%- -+..#%%%+.+-  +%%%#*=-:   Number of runs: 1201
               .:-=*%%%%. += .%%#  -+.-%%%%=-:..   Number of orders: 40
               .:=+#%%%%%*###%%%%#*+#%%%%%%*+-:    Initial balance: 400.0
@@ -156,10 +167,10 @@ you will get the following backtesting report:
            .++-        -%%%%%%%%%%%+=             Percentage negative trades: 70.0%
           .++-        .%%%%%%%%%%%%%+=            Average trade size: 100.9692 EUR
          .++-         *%%%%%%%%%%%%%*+:           Average trade duration: 83.6 hours
-        .++-          %%%%%%%%%%%%%%#+=         
-        =++........:::%%%%%%%%%%%%%%*+-         
-        .=++++++++++**#%%%%%%%%%%%%%++.         
-        
+        .++-          %%%%%%%%%%%%%%#+=
+        =++........:::%%%%%%%%%%%%%%*+-
+        .=++++++++++**#%%%%%%%%%%%%%++.
+
 Price noise
 
 Positions overview
@@ -220,8 +231,8 @@ Trades overview
 
 ### Backtest experiments
 
-The framework also supports backtest experiments. Backtest experiments allows you to 
-compare multiple algorithms and evaluate their performance. Ideally, 
+The framework also supports backtest experiments. Backtest experiments allows you to
+compare multiple algorithms and evaluate their performance. Ideally,
 you would do this by parameterizing your strategy and creating a factory function that
 creates the algorithm with the different parameters. You can find an example of this
 in the [backtest experiments example](./examples/backtest_experiment).
@@ -237,14 +248,14 @@ from investing_algorithm_framework import PortfolioConfiguration, \
 app = create_app()
 app.add_market_credential(
     MarketCredential(
-        market="<your market>", 
+        market="<your market>",
         api_key="<your api key>",
         secret_key="<your secret key>",
     )
 )
 app.add_portfolio_configuration(
     PortfolioConfiguration(
-        market="<your market>", 
+        market="<your market>",
         initial_balance=400,
         track_from="01/01/2022",
         trading_symbol="EUR"
@@ -267,27 +278,27 @@ pip install investing-algorithm-framework
 
 ## Disclaimer
 
-If you use this framework for your investments, do not risk money 
-which you are afraid to lose, until you have clear understanding how 
+If you use this framework for your investments, do not risk money
+which you are afraid to lose, until you have clear understanding how
 the framework works. We can't stress this enough:
 
-BEFORE YOU START USING MONEY WITH THE FRAMEWORK, MAKE SURE THAT YOU TESTED 
-YOUR COMPONENTS THOROUGHLY. USE THE SOFTWARE AT YOUR OWN RISK. 
+BEFORE YOU START USING MONEY WITH THE FRAMEWORK, MAKE SURE THAT YOU TESTED
+YOUR COMPONENTS THOROUGHLY. USE THE SOFTWARE AT YOUR OWN RISK.
 THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR INVESTMENT RESULTS.
 
-Also, make sure that you read the source code of any plugin you use or 
+Also, make sure that you read the source code of any plugin you use or
 implementation of an algorithm made with this framework.
 
 ## Documentation
 
-All the documentation can be found online 
+All the documentation can be found online
 at the [documentation webstie](https://investing-algorithm-framework.com)
 
-In most cases, you'll probably never have to change code on this repo directly 
-if you are building your algorithm/bot. But if you do, check out the 
+In most cases, you'll probably never have to change code on this repo directly
+if you are building your algorithm/bot. But if you do, check out the
 contributing page at the website.
 
-If you'd like to chat with investing-algorithm-framework users 
+If you'd like to chat with investing-algorithm-framework users
 and developers, [join us on Slack](https://inv-algo-framework.slack.com) or [join us on reddit](https://www.reddit.com/r/InvestingBots/)
 
 ## Acknowledgements
@@ -302,12 +313,12 @@ first. If it hasn't been reported, please [create a new issue](https://github.co
 
 ### Contributing
 
-The investing algorithm framework is a community driven project. 
+The investing algorithm framework is a community driven project.
 We welcome you to participate, contribute and together help build the future trading bots developed in python.
 
 Feel like the framework is missing a feature? We welcome your pull requests!
 If you want to contribute to the project roadmap, please take a look at the [project board](https://github.com/coding-kitties/investing-algorithm-framework/projects?query=is%3Aopen).
-You can pick up a task by assigning yourself to it. 
+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.

investing_algorithm_framework/__init__.py

@@ -11,12 +11,17 @@
     pretty_print_backtest_reports_evaluation, load_backtest_reports, \
     RESERVED_BALANCES, APP_MODE, AppMode, DATETIME_FORMAT, \
     load_backtest_report, BacktestDateRange, convert_polars_to_pandas, \
-    DateRange
+    DateRange, get_backtest_report
 from investing_algorithm_framework.infrastructure import \
     CCXTOrderBookMarketDataSource, CCXTOHLCVMarketDataSource, \
     CCXTTickerMarketDataSource, CSVOHLCVMarketDataSource, \
     CSVTickerMarketDataSource
 from .create_app import create_app
+from investing_algorithm_framework.indicators import get_rsi, get_peaks, \
+    is_uptrend, is_downtrend, is_crossover, is_crossunder, is_above, \
+    is_below, has_crossed_upward, get_sma, get_up_and_downtrends, \
+    get_rsi, get_ema, get_adx, has_crossed_downward, get_willr, \
+    is_divergence
 
 __all__ = [
     "Algorithm",
@@ -83,6 +88,6 @@
     "get_adx",
     "has_crossed_downward",
     "get_willr",
-    "is_bearish_divergence",
-    "is_bullish_divergence",
+    "is_divergence",
+    "get_backtest_report"
 ]

investing_algorithm_framework/app/app.py

@@ -91,7 +91,10 @@ def initialize(self, sync=False):
 
         Also, it initializes all required services for the algorithm.
 
-        :return: None
+        Args:
+            sync (bool): Whether to sync the portfolio with the exchange
+        Returns:
+            None
         """
         if self.algorithm is None:
             raise OperationalException("No algorithm registered")
@@ -162,8 +165,8 @@ def initialize(self, sync=False):
                     .create_portfolio_from_configuration(
                         portfolio_configuration
                     )
-                self.sync(portfolio)
-                synced_portfolios.append(portfolio)
+                # self.sync(portfolio)
+                # synced_portfolios.append(portfolio)
 
         if sync:
             portfolios = portfolio_service.get_all()
@@ -494,16 +497,15 @@ def run(
         separate thread.
 
         Args:
-            payload: The payload to handle if the app is running in
+            payload (dict): The payload to handle if the app is running in
             stateless mode
-            number_of_iterations: The number of iterations to run the
+            number_of_iterations (int): The number of iterations to run the
             algorithm for
-            sync: Whether to sync the portfolio with the exchange
+            sync (bool): Whether to sync the portfolio with the exchange
 
         Returns:
             None
         """
-
         # Run all on_initialize hooks
         for hook in self._on_after_initialize_hooks:
             hook.on_run(self, self.algorithm)

investing_algorithm_framework/domain/__init__.py

@@ -29,7 +29,8 @@
     add_column_headers_to_csv, get_total_amount_of_rows, \
     load_backtest_report, convert_polars_to_pandas, \
     csv_to_list, StoppableThread, pretty_print_backtest_reports_evaluation, \
-    pretty_print_backtest, load_csv_into_dict, load_backtest_reports
+    pretty_print_backtest, load_csv_into_dict, load_backtest_reports, \
+    get_backtest_report
 from .metrics import get_price_efficiency_ratio
 
 __all__ = [
@@ -117,5 +118,6 @@
     "load_backtest_report",
     "get_price_efficiency_ratio",
     "convert_polars_to_pandas",
-    "DateRange"
+    "DateRange",
+    "get_backtest_report"
 ]

investing_algorithm_framework/domain/models/backtesting/backtest_report.py

@@ -10,6 +10,9 @@
     .backtesting.backtest_date_range import BacktestDateRange
 from investing_algorithm_framework.domain.models.base_model import BaseModel
 from investing_algorithm_framework.domain.models.time_unit import TimeUnit
+from investing_algorithm_framework.domain.models.position import Position
+from investing_algorithm_framework.domain.models.trade import Trade
+from investing_algorithm_framework.domain.models.order import Order
 
 logger = getLogger(__name__)
 
@@ -451,7 +454,7 @@ def from_dict(data):
                 data["backtest_end_date"], DATETIME_FORMAT)
         )
 
-        return BacktestReport(
+        report = BacktestReport(
             name=data["name"],
             strategy_identifiers=data["strategy_identifiers"],
             number_of_runs=data["number_of_runs"],
@@ -477,6 +480,23 @@ def from_dict(data):
             average_trade_size=float(data["average_trade_size"]),
         )
 
+        positions = data["positions"]
+
+        if positions is not None:
+            report.positions = [Position.from_dict(position) for position in positions]
+
+        trades = data["trades"]
+
+        if trades is not None:
+            report.trades = [Trade.from_dict(trade) for trade in trades]
+
+        orders = data["orders"]
+
+        if orders is not None:
+            report.orders = [Order.from_dict(order) for order in orders]
+
+        return report
+
     def get_trades(self, symbol=None):
         """
         Function to get trades. If a symbol is provided, it will

investing_algorithm_framework/domain/models/base_model.py

@@ -23,3 +23,9 @@ def update(self, data):
 
             if value is not None:
                 setattr(self, attr, value)
+
+    @staticmethod
+    def from_dict(data):
+        instance = BaseModel()
+        instance.update(data)
+        return instance

investing_algorithm_framework/domain/models/position/position.py

@@ -2,6 +2,9 @@
 
 
 class Position(BaseModel):
+    """
+    This class represents a position in a portfolio.
+    """
 
     def __init__(
         self,

investing_algorithm_framework/domain/models/time_unit.py

@@ -19,7 +19,9 @@ def from_string(value: str):
                     return entry
 
         raise ValueError(
-            f"Could not convert value {value} to time unit"
+            f"Could not convert value {value} to time unit," +
+            " please make sure that the value is either of type string or" +
+            f"TimeUnit. Its current type is {type(value)}"
         )
 
     def equals(self, other):