anansi-toolkit

Anansi

Dependencies

Python, Pip, Poetry.

To install poetry, on osx, linux or bashonwindows terminals, type it:

curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python -

Alternatively, poetry could be installed by pip (supposing you have python and pip already installed):

pip install poetry

Consuming on Jupyter notebook

That is only a suggestion, you could run anansi on any python terminal. Only tested on linux.

Perform the commands:

poetry install
poetry run python -m ipykernel install --user --name=$(basename $(pwd))
poetry run jupyter notebook > jupyterlog 2>&1 &

Straight to the point: Running Default Back Testing Operation

Importing Dependencies

from anansi.tradingbot.models import *
from anansi.tradingbot import traders
from anansi.tradingbot.views import create_user, create_default_operation

Add a new user

my_user_first_name = "John"

create_user(first_name=my_user_first_name,
                   last_name="Doe",
                   email = "{}@email.com".format(my_user_first_name.lower()))

Creating a default operation

my_user = User[1]
create_default_operation(user=my_user)

Instantiating a trader

my_op = Operation.get(id=1)
my_trader = traders.DefaultTrader(operation=my_op)

Run the trader

my_trader.run()

Playing with the database models

Getting all users

users = select(user for user in User)
users.show()

id|first_name|last_name|login_displayed_name|email         
--+----------+---------+--------------------+--------------
1 |John      |Doe      |                    |[e-mail]

my_user.first_name

'John'

Some operation attribute

my_op.stop_loss.name

'StopTrailing3T'

Some trader attribute

my_trader.Classifier.parameters.time_frame

'6h'

Updating some attributes

before_update = my_trader.operation.position.side, my_trader.operation.position.exit_reference_price

my_trader.operation.position.update(side="Long", exit_reference_price=1020.94)

after_update = my_trader.operation.position.side, my_trader.operation.position.exit_reference_price

before_update, after_update

(('Zeroed', None), ('Long', 1020.94))

Requesting klines

Klines treated and ready for use, including market indicators methods

The example below uses the 'KlinesFromBroker' class from the 'handlers' module ('marketdata' package), which works as an abstraction over the data brokers, not only serializing requests (in order to respect brokers' limits), but also conforming the klines like a pandas dataframe, extended with market indicator methods.

from anansi.marketdata.handlers import KlinesFromBroker

BinanceKlines = KlinesFromBroker(
  broker_name="binance", ticker_symbol="BTCUSDT", time_frame="1h")

newest_klines = BinanceKlines.newest(2167)

newest_klines

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	Open_time	Open	High	Low	Close	Volume
0	2020-06-17 11:00:00	9483.25	9511.53	9466.00	9478.61	1251.802697
1	2020-06-17 12:00:00	9478.61	9510.88	9477.35	9499.25	1120.426332
2	2020-06-17 13:00:00	9499.24	9565.00	9432.00	9443.48	4401.693008
3	2020-06-17 14:00:00	9442.50	9464.83	9366.09	9410.95	4802.211120
4	2020-06-17 15:00:00	9411.27	9436.54	9388.43	9399.24	2077.135281
...	...	...	...	...	...	...
2162	2020-09-15 13:00:00	10907.94	10917.96	10834.00	10834.71	3326.420940
2163	2020-09-15 14:00:00	10834.71	10879.00	10736.63	10764.19	4382.021477
2164	2020-09-15 15:00:00	10763.37	10815.47	10745.63	10784.46	3531.309654
2165	2020-09-15 16:00:00	10785.23	10827.61	10700.00	10784.23	3348.735166
2166	2020-09-15 17:00:00	10784.23	10812.44	10738.33	10794.84	1931.035921

2167 rows × 6 columns

Applying simple moving average indicators

indicator = newest_klines.apply_indicator.trend.simple_moving_average(number_of_candles=35)

indicator.name, indicator.last(), indicator.serie

('sma_ohlc4_35',
 10669.49407142858,
 0                NaN
 1                NaN
 2                NaN
 3                NaN
 4                NaN
             ...     
 2162    10619.190500
 2163    10632.213571
 2164    10644.682643
 2165    10657.128857
 2166    10669.494071
 Length: 2167, dtype: float64)

newest_klines

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	Open_time	Open	High	Low	Close	Volume
0	2020-06-17 11:00:00	9483.25	9511.53	9466.00	9478.61	1251.802697
1	2020-06-17 12:00:00	9478.61	9510.88	9477.35	9499.25	1120.426332
2	2020-06-17 13:00:00	9499.24	9565.00	9432.00	9443.48	4401.693008
3	2020-06-17 14:00:00	9442.50	9464.83	9366.09	9410.95	4802.211120
4	2020-06-17 15:00:00	9411.27	9436.54	9388.43	9399.24	2077.135281
...	...	...	...	...	...	...
2162	2020-09-15 13:00:00	10907.94	10917.96	10834.00	10834.71	3326.420940
2163	2020-09-15 14:00:00	10834.71	10879.00	10736.63	10764.19	4382.021477
2164	2020-09-15 15:00:00	10763.37	10815.47	10745.63	10784.46	3531.309654
2165	2020-09-15 16:00:00	10785.23	10827.61	10700.00	10784.23	3348.735166
2166	2020-09-15 17:00:00	10784.23	10812.44	10738.33	10794.84	1931.035921

2167 rows × 6 columns

Same as above, but showing indicator column

indicator = newest_klines.apply_indicator.trend.simple_moving_average(
  number_of_candles=35, indicator_column="SMA_OHLC4_n35")

newest_klines

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	Open_time	Open	High	Low	Close	Volume	SMA_OHLC4_n35
0	2020-06-17 11:00:00	9483.25	9511.53	9466.00	9478.61	1251.802697	NaN
1	2020-06-17 12:00:00	9478.61	9510.88	9477.35	9499.25	1120.426332	NaN
2	2020-06-17 13:00:00	9499.24	9565.00	9432.00	9443.48	4401.693008	NaN
3	2020-06-17 14:00:00	9442.50	9464.83	9366.09	9410.95	4802.211120	NaN
4	2020-06-17 15:00:00	9411.27	9436.54	9388.43	9399.24	2077.135281	NaN
...	...	...	...	...	...	...	...
2162	2020-09-15 13:00:00	10907.94	10917.96	10834.00	10834.71	3326.420940	10619.190500
2163	2020-09-15 14:00:00	10834.71	10879.00	10736.63	10764.19	4382.021477	10632.213571
2164	2020-09-15 15:00:00	10763.37	10815.47	10745.63	10784.46	3531.309654	10644.682643
2165	2020-09-15 16:00:00	10785.23	10827.61	10700.00	10784.23	3348.735166	10657.128857
2166	2020-09-15 17:00:00	10784.23	10812.44	10738.33	10794.84	1931.035921	10669.494071

2167 rows × 7 columns

Raw klines, using the low level abstraction module "data_brokers"

DISCLAIMER: Requests here are not queued! There is a risk of banning the IP or even blocking API keys if some limits are exceeded. Use with caution.

from anansi.marketdata import data_brokers

BinanceBroker = data_brokers.BinanceDataBroker()

my_klines = BinanceBroker.get_klines(ticker_symbol="BTCUSDT", time_frame="1m")

my_klines

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	Open_time	Open	High	Low	Close	Volume
0	1600165560	10688.12	10691.14	10684.88	10684.88	21.529835
1	1600165620	10684.88	10686.15	10681.84	10685.99	18.487428
2	1600165680	10686.00	10687.65	10684.92	10687.09	22.246376
3	1600165740	10687.09	10689.54	10683.86	10687.26	18.818481
4	1600165800	10687.26	10687.26	10683.71	10685.76	38.281582
...	...	...	...	...	...	...
494	1600195200	10762.43	10763.48	10760.35	10760.75	8.572210
495	1600195260	10760.75	10762.48	10759.30	10759.31	11.089815
496	1600195320	10759.30	10762.22	10755.39	10761.26	27.070820
497	1600195380	10761.26	10761.26	10751.74	10756.02	15.482246
498	1600195440	10755.61	10756.57	10748.03	10748.04	61.153777

499 rows × 6 columns

Same as above, but returning all information get from the data broker

my_klines = BinanceBroker.get_klines(ticker_symbol="BTCUSDT", time_frame="1m", show_only_desired_info=False)

my_klines

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	Open_time	Open	High	Low	Close	Volume	Close_time	Quote_asset_volume	Number_of_trades	Taker_buy_base_asset_volume	Taker_buy_quote_asset_volume	Ignore
0	1600165560	10688.12	10691.14	10684.88	10684.88	21.529835	1600165619	230126.587773	373.0	10.279415	109864.149822	0.0
1	1600165620	10684.88	10686.15	10681.84	10685.99	18.487428	1600165679	197536.180849	336.0	8.256498	88223.566054	0.0
2	1600165680	10686.00	10687.65	10684.92	10687.09	22.246376	1600165739	237738.839831	415.0	13.378805	142975.243246	0.0
3	1600165740	10687.09	10689.54	10683.86	10687.26	18.818481	1600165799	201100.293663	539.0	9.062957	96849.611844	0.0
4	1600165800	10687.26	10687.26	10683.71	10685.76	38.281582	1600165859	409068.511314	534.0	16.799813	179523.708531	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...
494	1600195200	10762.43	10763.48	10760.35	10760.75	8.572210	1600195259	92253.016477	292.0	2.394778	25771.715413	0.0
495	1600195260	10760.75	10762.48	10759.30	10759.31	11.089815	1600195319	119341.014647	277.0	3.064458	32976.256534	0.0
496	1600195320	10759.30	10762.22	10755.39	10761.26	27.070820	1600195379	291245.877535	490.0	14.654896	157679.926758	0.0
497	1600195380	10761.26	10761.26	10751.74	10756.02	15.482246	1600195439	166520.446192	353.0	7.390407	79491.160961	0.0
498	1600195440	10755.61	10756.57	10748.03	10748.04	61.153777	1600195499	657520.935924	585.0	13.436657	144474.084684	0.0

499 rows × 12 columns