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‎docs/babel.config.js‎

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module.exports = {
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presets: [require.resolve('@docusaurus/core/lib/babel/preset')],
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};

‎docs/docs/Advanced Concepts/_category_.json‎

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{
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"label": "Advanced concepts",
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"position": 1,
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"link": {
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"type": "generated-index",
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"description": "Advanced concepts and features of the framework."
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}
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}

‎docs/docs/Cook Book/how-to-check-open-orders.md.js‎

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‎docs/docs/Cook Book/how-to-structure-your-code.js‎

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‎docs/docs/Cook Book/using-take-profit-and-stop-loss.js‎

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‎docs/docs/Data/__category__.json‎

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{
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"label": "Data",
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"position": 0,
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"link": {
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"type": "generated-index",
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"description": "Learn the basic concepts of using data sources in the investing algorithm framework"
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}
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}

‎docs/docs/Data/market-data-sources.md‎

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---
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sidebar_position: 1
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---
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# Market data sources
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Algorithmic trading needs quick access to real-time data and effective data manipulation for successful analysis.
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To meet these needs, the framework provides a data object that can be used in your trading strategies.
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For data availability, we use a push-based approach. This means we send the desired information directly as
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an argument to each trading strategy handler function or trading strategy class.
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It's easy to use – just annotate your handler with the information you need.
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Here is an example of a handler that uses the `TICKER` data object:
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```python
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# A ticker market data source for the BTC/EUR symbol on the bitvavo exchange
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bitvavo_ticker_btc_eur = CCXTTickerMarketDataSource(
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identifier="BTC-ticker",
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market="BITVAVO",
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symbol="BTC/EUR",
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)
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class MyTradingStrategy(TradingStrategy):
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time_unit = TimeUnit.SECOND
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interval = 5
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market_data_sources = ["BTC-ticker"] # Registering the market data source by using its identifier
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def apply_strategy(self, algorithm: Algorithm, data: dict):
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print(data)
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# Make sure to register your market data sources with the app
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app.add_trading_strategy(MyTradingStrategy)
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app.add_market_data_source(bitvavo_ticker_btc_eur)
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```
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By doing so your handler function parameter data will be assigned a data Object containing ticker for BTC/EUR from
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the bitvavo exchange under the key "BTC-ticker".
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## Accessing data
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You can easily access the data object by using the `identifier` attribute of your MarketDataSource object.
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The following code snippet shows how to access the data object:
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:::tip
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The data object that is passed in your trading strategy is a dictionary. This allows you to access multiple data objects
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in your trading strategy. The key of the dictionary is the identifier of the market data source.
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:::
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```python
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# A ticker market data source for the BTC/EUR symbol on the bitvavo exchange
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bitvavo_ticker_btc_eur = CCXTTickerMarketDataSource(
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identifier="BTC-ticker",
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market="BITVAVO",
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symbol="BTC/EUR",
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)
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class MyTradingStrategy(TradingStrategy):
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time_unit = TimeUnit.SECOND
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interval = 5
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market_data_sources = ["BTC-ticker"] # Registering the market data source by using its identifier
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def apply_strategy(self, algorithm: Algorithm, data):
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ticker_data = data["BTC-ticker"] # Accessing the data object directly by using the identifier
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# Make sure to register your market data sources with the app
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app.add_trading_strategy(MyTradingStrategy)
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app.add_market_data_source(bitvavo_ticker_btc_eur)
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```
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## CCXT market data sources
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The framework comes out of the box with support for the [ccxt](https://github.com/ccxt/ccxt).
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This allows you the use the following ccxt market data sources:
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- CCXTTickerMarketDataSource
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- CCXTOHLCVMarketDataSource
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- CCXTOrderBookMarketDataSource
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### CCXTTickerMarketDataSource
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The CCXTTickerMarketDataSource is used to get the latest ticker data for a symbol. It is based
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on the popular [ccxt](https://github.com/ccxt/ccxt) library.
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```python
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from investing_algorithm_framework import CCXTTickerMarketDataSource, TradingStrategy, \
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Algorithm, TimeUnit
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# A ohlcv market data source for the BTC/EUR symbol on the BITVAVO exchange
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bitvavo_ticker_btc_eur = CCXTTickerMarketDataSource(
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identifier="BTC-ticker",
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market="BITVAVO",
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symbol="BTC/EUR",
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)
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class MyTradingStrategy(TradingStrategy):
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time_unit = TimeUnit.SECOND # The time unit of the strategy
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interval = 5 # The interval of the strategy, runs every 5 seconds
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# Registering the market data source
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market_data_sources = [bitvavo_ticker_btc_eur]
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def apply_strategy(self, algorithm: Algorithm, market_data: Dict[str, Any]):
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print(market_data[bitvavo_ticker_btc_eur.get_identifier()])
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```
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### CCXTOHLCVMarketDataSource
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The CCXTOHLCVMarketDataSource is used to get candle stick/OHLCV data for a symbol. It is based
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on the popular [ccxt](https://github.com/ccxt/ccxt) library.
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:::info
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For ohlcv data you need to specify a start date, and/or an end date.
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If you don't specify an end date, the framework will use the current date as the end date. The daterange between
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the start and end date is used to determine the number of candlesticks in your ohlcv data. E.g. if you
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specify a start date of `start_date=datetime.utcnow() - timedelta(days=17)` and a timeframe of 2h, the framework will
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fetch 216 candlesticks (17 days * 12 candlesticks per day). Keep in mind that by leveraging a function like `datetime.utcnow()`
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you will get the current date in UTC time everytime the market data source is used. This allows you to get the latest data
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everytime the strategy runs.
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:::
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```python
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from investing_algorithm_framework import CCXTOHLCVMarketDataSource, TradingStrategy, \
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Algorithm, TimeUnit
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# A order book market data source for the BTC/EUR symbol on the BITVAVO exchange
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bitvavo_btc_eur_ohlcv_2h = CCXTTickerMarketDataSource(
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identifier="BTC-ohlcv-2h",
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market="BITVAVO",
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symbol="BTC/EUR",
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)
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class MyTradingStrategy(TradingStrategy):
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time_unit = TimeUnit.SECOND # The time unit of the strategy
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interval = 5 # The interval of the strategy, runs every 5 seconds
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# Registering the market data source
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market_data_sources = [bitvavo_btc_eur_ohlcv_2h]
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def apply_strategy(self, algorithm: Algorithm, market_data: Dict[str, Any]):
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print(market_data[bitvavo_btc_eur_ohlcv_2h.get_identifier()])
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```
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### CCXTOrderBookMarketDataSource
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The CCXTOrderBookMarketDataSource is used to get order book data for a symbol. It is based
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on the popular [ccxt](https://github.com/ccxt/ccxt) library.
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```python
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from investing_algorithm_framework import CCXTOrderBookMarketDataSource, TradingStrategy, \
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Algorithm, TimeUnit
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# A ticker market data source for the BTC/EUR symbol on the BITVAVO exchange
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bitvavo_btc_eur_order_book = CCXTOrderBookMarketDataSource(
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identifier="BTC-order-book",
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market="BITVAVO",
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symbol="BTC/EUR",
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)
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class MyTradingStrategy(TradingStrategy):
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time_unit = TimeUnit.SECOND # The time unit of the strategy
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interval = 5 # The interval of the strategy, runs every 5 seconds
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# Registering the market data source
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market_data_sources = [bitvavo_btc_eur_order_book]
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def apply_strategy(self, algorithm: Algorithm, market_data: Dict[str, Any]):
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print(market_data[bitvavo_btc_eur_order_book.get_identifier()])
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```
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‎docs/docs/Data/multiple-market-data-sources.md‎

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---
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sidebar_position: 2
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---
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# Multiple data sources
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The framework allows you to configure multiple data sources for your trading strategies.
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You can use this to combine different data sources. This allowes you to easily use different exchanges
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or brokers in your trading strategy.
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A quick example of how to use multiple data sources in your trading strategy:
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```python
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# A ticker market data source for the BTC/EUR symbol on the bitvavo exchange
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bitvavo_ticker_btc_eur = CCXTTickerMarketDataSource(
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identifier="BTC-ticker",
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market="BITVAVO",
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symbol="BTC/EUR",
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)
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# A ohlcv market data source for the BTC/EUR symbol on the BITVAVO exchange
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bitvavo_btc_eur_ohlcv_2h = CCXTOHLCVMarketDataSource(
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identifier="BTC-ohlcv",
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market="BITVAVO",
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symbol="BTC/EUR",
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timeframe="2h",
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start_date_func=lambda : datetime.utcnow() - timedelta(days=17)
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)
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class MyTradingStrategy(TradingStrategy):
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time_unit = TimeUnit.SECOND
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interval = 5
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market_data_sources = ["BTC-ticker", "BTC-ohlcv"] # Registering the market data sources by using their identifiers
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def apply_strategy(self, algorithm: Algorithm, data):
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ticker_data = data["BTC-ticker"] # Accessing the ticker data object directly by using the identifier
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ohlcv_data = data["BTC-ohlcv"] # Accessing the ohlcv data object directly by using the identifier
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# Make sure to register your market data sources with the app
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app.add_trading_strategy(MyTradingStrategy)
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app.add_market_data_source(bitvavo_ticker_btc_eur)
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app.add_market_data_source(bitvavo_btc_eur_ohlcv_2h)
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```
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‎docs/docs/Getting Started/__category__.json‎

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{
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"label": "Getting Started",
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"position": 0,
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"link": {
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"type": "generated-index",
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"description": "Learn the basic concepts of the investing algorithm framework"
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}
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}

‎docs/docs/Getting Started/application-setup.md‎

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---
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sidebar_position: 2
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---
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# Application Setup
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The best way to get started with the framework is to create an application through the init cli command.
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You can do this by running the following command in your terminal:
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```bash
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investing-algorithm-framework init
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```
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This will create the following directory structure:
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```bash
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.
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├── README.md
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├── app.py
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├── strategies
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│ └── my_trading_strategy.py
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└── gitignore
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```
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The `app.py` file is the main entry point for your application. Ideally, you should only use this file to register your
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strategies, portfolio configurations and market data sources. Its also
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not recommended to add any logic to this file because the framework will use this file to run your application.
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The `strategies` directory is where you can add your trading strategies. You can create multiple files in this directory
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and add your trading strategies to them. The framework will use this directory to save and link your
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trading strategies to your backtest runs. This allows you to easily run multiple backtests with different trading strategies
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and bundle them with your backtest results.
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By default the REST API and UI are disabled. You can enable them by running the init command with the `--web` flag:
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```bash
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investing-algorithm-framework init --web
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```
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or you can enable them later by adding the following lines to your `app.py` file:
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```python
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import logging.config
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from dotenv import load_dotenv
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from investing_algorithm_framework import create_app, DEFAULT_LOGGING_CONFIG
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load_dotenv()
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logging.config.dictConfig(DEFAULT_LOGGING_CONFIG)
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app = create_app(
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web=True, # Enable the REST API and UI
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)
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# Register your trading strategies here
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...
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# Register your market data sources here
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...
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# Register your portfolio configurations here
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...
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# Run the app
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if __name__ == "__main__":
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app.run()
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```

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