added demo and support for no keys (crypto market data)

README.md

@@ -62,6 +62,35 @@ exchange = cira.Exchange() # methods for exchange
 stock = cira.Stock("TSLA") # a class for one stock
 ```
 
+### DEMO, no keys needed 
+
+Crypto market data can be accessed [without any alpaca keys](https://alpaca.markets/sdks/python/market_data.html#api-keys).
+So there for you can try cira out with out needing to get alpaca keys. 
+
+Needs `cira>=3.2.1`.
+
+```python
+import cira
+from datetime import datetime
+
+assert not cira.auth.check_keys() # No keys are needed
+
+SYMBOL = "BTC/USD"
+ast = cira.Cryptocurrency(SYMBOL)
+
+print(f"The current asking price for {SYMBOL} is {ast.price()}")
+
+
+# alpaca only have BTC data from 2021 and forward 
+data = ast.historical_data_df(datetime(2021, 6, 1), datetime(2024, 6, 1))
+print(data.head())
+
+# All of strategies and backtesting works with out keys as well. 
+strat = cira.strategy.Randomness()
+cira.strategy.back_test_against_buy_and_hold(strat, data, data["open"].to_frame(), 100_000).plot()
+```
+
+
 ### Sci-kit learn + cira 
 
 > only for v3
@@ -106,12 +135,14 @@ With strategies you can run a cira backtests.
 
 ```python
 from cira.strategy import Strategy
+import numpy as np
+import pandas as pd
 
 class MyStrat(Strategy):
     def __init__(self) -> None:
         super().__init__(name="MyStrat")
 
-    def iterate(self, feature_data: DataFrame, prices: DataFrame, portfolio: np.ndarray, cash:float) -> np.ndarray:
+    def iterate(self, feature_data: pd.DataFrame, prices: pd.DataFrame, portfolio: np.ndarray, cash:float) -> np.ndarray:
         # this mehod will be called for each row of data in the backtest 
         # the function should return the change of your portfolio. 
         # -1 means sell one stock, 0 means hold, 1 means buy one stock

cira/__init__.py

@@ -21,6 +21,6 @@
 
 import alpaca
 
-__version__ = "3.2.0"
+__version__ = "3.2.1"
 __author__ = "Axel Gard"
 __credits__ = "alpaca.markets"

cira/assset_cryptocurrency.py

@@ -39,11 +39,16 @@
 class Cryptocurrency(Asset):
     def __init__(self, symbol: str) -> None:
         """Exchange for trading cryptocurrencies"""
-        APCA_ID, APCA_SECRET = auth.get_api_keys()
+        try:
+            APCA_ID, APCA_SECRET = auth.get_api_keys()
+        except ValueError:
+            APCA_ID, APCA_SECRET = "", ""
         self.symbol = symbol
         self.live_client = CryptoDataStream(APCA_ID, APCA_SECRET)
-        self.history: CryptoHistoricalDataClient = CryptoHistoricalDataClient()
-        if APCA_ID != "":
+        self.history: CryptoHistoricalDataClient = CryptoHistoricalDataClient(
+            None, None
+        )
+        if APCA_ID != "" and APCA_SECRET != "":
             self.history = CryptoHistoricalDataClient(APCA_ID, APCA_SECRET)
             self.trade = TradingClient(APCA_ID, APCA_SECRET, paper=config.PAPER_TRADING)
         self.latest_quote_request = CryptoLatestQuoteRequest

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name="cira",
-    version="3.2.0",
+    version="3.2.1",
     description="A simpler library for the alapaca trade api",
     url="https://github.com/AxelGard/cira",
     author="Axel Gard",

tests/test_demo.py

@@ -0,0 +1,22 @@
+import cira
+from datetime import datetime
+from pandas import Timestamp
+import numpy as np
+import pandas as pd
+
+def test_demo():
+    cira.auth.KEY_FILE = "" # NO KEY 
+    assert not cira.auth.check_keys()
+
+    SYMBOL = "BTC/USD"
+    ast = cira.Cryptocurrency(SYMBOL)
+    assert ast.price() > 0.0 # send requst to alpaca but small 
+
+    data = pd.DataFrame({'symbol': {Timestamp('2023-07-01 05:00:00+0000', tz='UTC'): 'BTC/USD', Timestamp('2023-07-02 05:00:00+0000', tz='UTC'): 'BTC/USD', Timestamp('2023-07-03 05:00:00+0000', tz='UTC'): 'BTC/USD', Timestamp('2023-07-04 05:00:00+0000', tz='UTC'): 'BTC/USD', Timestamp('2023-07-05 05:00:00+0000', tz='UTC'): 'BTC/USD'}, 'open': {Timestamp('2023-07-01 05:00:00+0000', tz='UTC'): 30385.895956, Timestamp('2023-07-02 05:00:00+0000', tz='UTC'): 30528.37, Timestamp('2023-07-03 05:00:00+0000', tz='UTC'): 30697.0715105075, Timestamp('2023-07-04 05:00:00+0000', tz='UTC'): 31063.25735, Timestamp('2023-07-05 05:00:00+0000', tz='UTC'): 30841.5055}, 'high': {Timestamp('2023-07-01 05:00:00+0000', tz='UTC'): 30710.060945, Timestamp('2023-07-02 05:00:00+0000', tz='UTC'): 30813.870461679, Timestamp('2023-07-03 05:00:00+0000', tz='UTC'): 31390.039548221, Timestamp('2023-07-04 05:00:00+0000', tz='UTC'): 31123.8403, Timestamp('2023-07-05 05:00:00+0000', tz='UTC'): 30883.247817}, 'low': {Timestamp('2023-07-01 05:00:00+0000', tz='UTC'): 30374.97, Timestamp('2023-07-02 05:00:00+0000', tz='UTC'): 30231.36435, Timestamp('2023-07-03 05:00:00+0000', tz='UTC'): 30485.7, Timestamp('2023-07-04 05:00:00+0000', tz='UTC'): 30629.9067, Timestamp('2023-07-05 05:00:00+0000', tz='UTC'): 30204.959}, 'close': {Timestamp('2023-07-01 05:00:00+0000', tz='UTC'): 30529.743525, Timestamp('2023-07-02 05:00:00+0000', tz='UTC'): 30701.6205105075, Timestamp('2023-07-03 05:00:00+0000', tz='UTC'): 31056.9905094905, Timestamp('2023-07-04 05:00:00+0000', tz='UTC'): 30842.05240964, Timestamp('2023-07-05 05:00:00+0000', tz='UTC'): 30457.98348348}, 'volume': {Timestamp('2023-07-01 05:00:00+0000', tz='UTC'): 0.544617957, Timestamp('2023-07-02 05:00:00+0000', tz='UTC'): 1.010968445, Timestamp('2023-07-03 05:00:00+0000', tz='UTC'): 1.339711015, Timestamp('2023-07-04 05:00:00+0000', tz='UTC'): 0.421326377, Timestamp('2023-07-05 05:00:00+0000', tz='UTC'): 1.431555108}, 'trade_count': {Timestamp('2023-07-01 05:00:00+0000', tz='UTC'): 100.0, Timestamp('2023-07-02 05:00:00+0000', tz='UTC'): 104.0, Timestamp('2023-07-03 05:00:00+0000', tz='UTC'): 197.0, Timestamp('2023-07-04 05:00:00+0000', tz='UTC'): 79.0, Timestamp('2023-07-05 05:00:00+0000', tz='UTC'): 364.0}, 'vwap': {Timestamp('2023-07-01 05:00:00+0000', tz='UTC'): 30565.780444418, Timestamp('2023-07-02 05:00:00+0000', tz='UTC'): 30540.5968780593, Timestamp('2023-07-03 05:00:00+0000', tz='UTC'): 30950.9446485936, Timestamp('2023-07-04 05:00:00+0000', tz='UTC'): 30957.1352892724, Timestamp('2023-07-05 05:00:00+0000', tz='UTC'): 30443.6923107624}})
+    #data = ast.historical_data_df(datetime(2023, 7, 1), datetime(2023, 7, 6)) # to not request data for each , but should be the same result 
+    assert data.shape == (5, 8)
+    assert data.keys().to_list() == ['symbol', 'open', 'high', 'low', 'close', 'volume', 'trade_count', 'vwap']
+
+    strat = cira.strategy.Randomness()
+    bt = cira.strategy.back_test_against_buy_and_hold(strat, data, data["open"].to_frame(), 100_000, True)
+    assert np.allclose(bt[cira.strategy.ByAndHold().name].head(3).to_numpy(), np.array([99635.369248528, 100062.791380528, 100568.8959120505]))