Fix issue with data sources parameters passing (#284)

* Fix issue with data sources parameters passing

* Fix flake8 issues

* Add azure storage state handler support

* Fix flake8 warnings

* Add poetry lock

* Pin poetry version to 1.7.1

* Fix failing tests

* Fix flake 8 warnings

* Fix flask test base

Author: MDUYN

.github/workflows/test.yml

@@ -69,6 +69,7 @@ jobs:
       - name: Install Poetry
         uses: snok/install-poetry@v1
         with:
+          version: 1.7.1
           virtualenvs-create: true
           virtualenvs-in-project: true
       #----------------------------------------------

.gitignore

@@ -2,8 +2,10 @@
 ### Python template
 # Byte-compiled / optimized / DLL files
 __pycache__/
+*__pycache__/
 *.py[cod]
 *$py.class
+.DS_Store
 
 # C extensions
 *.so

README.md

@@ -7,6 +7,7 @@
 [![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!
 
 ###### Sponsors
+
 <p align="left">
 <a href="https://finterion.com">
   <img alt="Finterion" src="static/sponsors/finterion.png" width="200px" />
@@ -19,7 +20,7 @@ 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)
@@ -36,23 +37,13 @@ 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 pathlib
+import logging
 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 
-# symbols on the market
-symbols = ["BTC/EUR"]
+    TimeUnit, CCXTOHLCVMarketDataSource, Algorithm, \
+    CCXTTickerMarketDataSource, MarketCredential, DEFAULT_LOGGING_CONFIG
 
-# Define resource directory and the symbols you want to trade
-config = {
-    RESOURCE_DIRECTORY: pathlib.Path(__file__).parent.resolve(),
-    SYMBOLS: symbols
-}
+logging.config.dictConfig(DEFAULT_LOGGING_CONFIG)
 
-# Define market data sources
 # OHLCV data for candles
 bitvavo_btc_eur_ohlcv_2h = CCXTOHLCVMarketDataSource(
     identifier="BTC-ohlcv",
@@ -67,13 +58,10 @@ bitvavo_btc_eur_ticker = CCXTTickerMarketDataSource(
     market="BITVAVO",
     symbol="BTC/EUR",
 )
-app = create_app(config=config)
+app = create_app()
 algorithm = Algorithm()
-app.add_market_credential(MarketCredential(
-    market="bitvavo",
-    api_key="<your api key>",
-    secret_key="<your secret key>",
-))
+# Bitvavo market credentials are read from .env file
+app.add_market_credential(MarketCredential(market="bitvavo"))
 app.add_portfolio_configuration(
     PortfolioConfiguration(
         market="bitvavo",
@@ -83,42 +71,39 @@ app.add_portfolio_configuration(
 )
 app.add_algorithm(algorithm)
 
+# Run every two hours and register the data sources
 @algorithm.strategy(
-    # Run every two hours
-    time_unit=TimeUnit.HOUR, 
-    interval=2, 
-    # Specify market data sources that need to be passed to the 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):
-    # By default, ohlcv data is passed as polars df in the form of
-    # {"<identifier>": <dataframe>}  https://pola.rs/, 
-    # call to_pandas() to convert to pandas
-    polars_df = market_data["BTC-ohlcv"]  
-    print(f"I have access to {len(polars_df)} candles of ohlcv data")
+    # Access the data sources with the indentifier
+    polars_df = market_data["BTC-ohlcv"]
 
-    # Ticker data is passed as {"<identifier>": <ticker dict>}
+    # Convert the polars dataframe to a pandas dataframe
+    pandas_df = polars_df.to_pandas()
     ticker_data = market_data["BTC-ticker"]
     unallocated_balance = algorithm.get_unallocated()
     positions = algorithm.get_positions()
     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 +121,14 @@ 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 +141,10 @@ 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 +205,8 @@ 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).
@@ -237,16 +222,15 @@ 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 +251,27 @@ 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 +286,12 @@ 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.

examples/app.py

@@ -0,0 +1,49 @@
+from dotenv import load_dotenv
+
+from investing_algorithm_framework import create_app, PortfolioConfiguration, \
+    TimeUnit, CCXTOHLCVMarketDataSource, Algorithm, \
+    CCXTTickerMarketDataSource, MarketCredential, AzureBlobStorageStateHandler
+
+load_dotenv()
+
+# Define market data sources
+# OHLCV data for candles
+bitvavo_btc_eur_ohlcv_2h = CCXTOHLCVMarketDataSource(
+    identifier="BTC-ohlcv",
+    market="BITVAVO",
+    symbol="BTC/EUR",
+    time_frame="2h",
+    window_size=200
+)
+# Ticker data for orders, trades and positions
+bitvavo_btc_eur_ticker = CCXTTickerMarketDataSource(
+    identifier="BTC-ticker",
+    market="BITVAVO",
+    symbol="BTC/EUR",
+)
+app = create_app(state_handler=AzureBlobStorageStateHandler())
+app.add_market_data_source(bitvavo_btc_eur_ohlcv_2h)
+algorithm = Algorithm()
+app.add_market_credential(MarketCredential(market="bitvavo"))
+app.add_portfolio_configuration(
+    PortfolioConfiguration(
+        market="bitvavo",
+        trading_symbol="EUR",
+        initial_balance=20
+    )
+)
+app.add_algorithm(algorithm)
+
+@algorithm.strategy(
+    # Run every two hours
+    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, "BTC-ohlcv"]
+)
+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/,
+    # call to_pandas() to convert to pandas
+    polars_df = market_data["BTC-ohlcv"]
+    print(f"I have access to {len(polars_df)} candles of ohlcv data")

examples/bitvavo_trading_bot/bitvavo.py

@@ -1,31 +1,28 @@
-import os
-
 from investing_algorithm_framework import MarketCredential, TimeUnit, \
     CCXTOHLCVMarketDataSource, CCXTTickerMarketDataSource, TradingStrategy, \
-    create_app, PortfolioConfiguration, Algorithm, SYMBOLS, RESOURCE_DIRECTORY
+    create_app, PortfolioConfiguration, Algorithm
 
 """
 Bitvavo trading bot example with market data sources of bitvavo.
-Bitvavo does not requires you to have an API key and secret key to access 
-their market data. If you just want to backtest your strategy, 
+Bitvavo does not requires you to have an API key and secret key to access
+their market data. If you just want to backtest your strategy,
 you don't need to add a market credential. If your running your strategy live,
-you need to add a market credential to the app, that accesses your 
+you need to add a market credential to the app, that accesses your
 account on bitvavo.
 """
 
-
 # Define your market credential for bitvavo
 bitvavo_market_credential = MarketCredential(
-    api_key="<your_api_key>",
-    secret_key="<your_secret_key>",
     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(
     identifier="BTC/EUR-ohlcv",
     market="bitvavo",
     symbol="BTC/EUR",
-    timeframe="2h",
+    time_frame="2h",
     window_size=200
 )
 bitvavo_btc_eur_ticker = CCXTTickerMarketDataSource(
@@ -36,26 +33,20 @@
 
 
 class BitvavoTradingStrategy(TradingStrategy):
-    time_unit = TimeUnit.HOUR
-    interval = 2
+    time_unit = TimeUnit.SECOND
+    interval = 10
     market_data_sources = [bitvavo_btc_eur_ohlcv_2h, bitvavo_btc_eur_ticker]
 
     def apply_strategy(self, algorithm, market_data):
         print(market_data["BTC/EUR-ohlcv"])
         print(market_data["BTC/EUR-ticker"])
 
-
-config = {
-    SYMBOLS: ["BTC/EUR"],
-    RESOURCE_DIRECTORY: os.path.join(os.path.dirname(__file__), "resources")
-}
-
 # Create an algorithm and link your trading strategy to it
 algorithm = Algorithm()
 algorithm.add_strategy(BitvavoTradingStrategy)
 
 # Create an app and add the market data sources and market credentials to it
-app = create_app(config=config)
+app = create_app()
 app.add_market_credential(bitvavo_market_credential)
 app.add_market_data_source(bitvavo_btc_eur_ohlcv_2h)
 app.add_market_data_source(bitvavo_btc_eur_ticker)

examples/deployments/azure_function/.funcignore

@@ -0,0 +1 @@
+