v3.3.0

Added support for fractional shares for back test.

Full Changelog: v3.2.2...v3.3.0

v3.2.2

Fixed so that Matplotlib is included so that demo works straight out the box.

Full Changelog: v3.2.1...v3.2.2

v3.2.1

Added demo and support for no keys when accessing crypto market data.

Full Changelog: v3.2.0...v3.2.1

v3.2.0

Removed old SDK for alpaca (https://pypi.org/project/alpaca-trade-api/) due to deprecation.

Some older functions are removed and will return None and gives a warning if used.

Full Changelog: v3.1.0...v3.2.0

v3.1.0

Fixed crypto so that it works, know, but might have bugs.
Added option asset but is just a template, it does not work for now.

Full Changelog: v3.0.4...v3.1.0

v3.0.4

• moved all strategy stuff into one file so not to deal with import problem

v3.0.3

• Fix with imports

v3.0.2

• fixed import problem

v3.0.1

• fixed bug so now you can access the strategy module

v3.0.0

cira v3.0.0 is out!

After a long time of almost no updates v3 is finaly out.
V3 brings some much needed features to the cira library.

Sadly there are some breaking changes.
But they should be easly fixed.

cira v2 vs v3

most code from cira v2 will still work the only change that might be needed is that some things that was prevusaly a class @property such as price is
now a accessed through a function call to make it more clear when the api is called. This also makes the docutmention easyer to read.

So in cira v2

import cira
stock = cira.Stock("TSLA")
print(stock.price)

Now in cira v3

import cira
stock = cira.Stock("TSLA")
print(stock.price())

Alpaca file is now in auth

there is now also a function for checking that you key is working.

In v2

import cira
cira.alpaca.KEY_FILE = "../mypath/key.json"
stock = cira.Stock("TSLA")

but now in v3:

import cira
cira.auth.KEY_FILE = "../mypath/key.json"
assert cira.auth.check_keys(), "the set keys dose not work"

Cira Strategies

A new module for cira v3 is strategies.
The cira strategies lets you backtest the models and set them into production in a simple way.

the strategies have some sub modules.

• strategy class, for you model
• backtest, for checking how your model preformce on historical data.
• scheduler, for scheduling of execution.

An full example of how to use the strategy is example/linear.

from cira.strategy import Strategy

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:
        # 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
        return np.array([ portfolio_change_as_int ]) 

cira comes with two strategies:

• Randomness, which is will just return random values for each iteration
• BuyAndHold, which will buy as much as possible in the frist iteration then hold.

more might be added later on.

backtest

with cira v3 there is a new backtest function.
there are some in v3.0.0 three types of backtests:

• backtest
• backtest against buy and hold strategi
• multi strategy backtest

a example of how to use them is:

import cira
from cira.strategy.strategy import Randomness
from cira.strategy.backtest import back_test
from datetime import datetime
import pandas as pd

cira.auth.KEY_FILE = "../../alpc_key.json"
assert cira.auth.check_keys(), "the set keys dose not work"

stock = cira.Stock("AAPL")
df = stock.historical_data_df(datetime(2022, 1, 1), datetime(2024, 1, 1))
prices = pd.DataFrame()
prices["AAPL"] = df["close"]

strat = Randomness(-10,10, seed=23323)
bt = back_test(strat, df.copy(), prices.copy(), 10_000, True)
bt.plot()

If you want more full example of how to use the backtest checkout
multiassets and
linear.

cryptocurncies

Alpaca.py have support for cryptob but in cira v3.0.0 the crypto support is very minmal.

More suppor is comming...

Assets class / Stock class

There are new methods in the assets classes such as:

import cira
from datetime import datetime

stock = cira.Stock("TSLA")
df = stock.historical_data_df(datetime(2019, 1, 1), datetime(2024, 1, 1))
df # df will be a pandas data frame, were he index is a timestamp