ttools/tests/data_loader_tryme.ipynb

Load data¶

Make sure you have .env file in ttools or any parent dir with your Alpaca keys.

ACCOUNT1_LIVE_API_KEY=api_key
ACCOUNT1_LIVE_SECRET_KEY=secret_key

Cache directories¶

Daily trade files - DATADIR/tradecache Agg data cache - DATADIR/aggcache

DATADIR - user_data_dir from appdirs library - see config.py

In [1]:

import pandas as pd
import numpy as np
from ttools.utils import AggType
from datetime import datetime
from ttools.aggregator_vectorized import generate_time_bars_nb, aggregate_trades
from ttools.loaders import load_data, prepare_trade_cache
from ttools.utils import zoneNY
import vectorbtpro as vbt
from lightweight_charts import PlotDFAccessor, PlotSRAccessor


vbt.settings.set_theme("dark")
vbt.settings['plotting']['layout']['width'] = 1280
vbt.settings.plotting.auto_rangebreaks = True
# Set the option to display with pagination
pd.set_option('display.notebook_repr_html', True)
pd.set_option('display.max_rows', 10)  # Number of rows per page

TTOOLS: Loaded env variables from file /Users/davidbrazda/Documents/Development/python/.env

Fetching aggregated data¶

Available aggregation types:

• time based bars - AggType.OHLCV
• volume based bars - AggType.OHLCV_VOL, resolution = volume threshold
• dollar based bars - AggType.OHLCV_DOL, resolution = dollar threshold
• renko bars - AggType.OHLCV_RENKO resolution = bricksize

In [15]:

#This is how to call LOAD function
symbol = ["BAC","AAPL"]
#datetime in zoneNY 
day_start = datetime(2024, 10, 14, 9, 45, 0)
day_stop = datetime(2024, 10, 16, 15, 1, 0)
day_start = zoneNY.localize(day_start)
day_stop = zoneNY.localize(day_stop)

#requested AGG
resolution = 12 #12s bars
agg_type = AggType.OHLCV #other types AggType.OHLCV_VOL, AggType.OHLCV_DOL, AggType.OHLCV_RENKO
exclude_conditions = ['C','O','4','B','7','V','P','W','U','Z','F','9','M','6'] #None to defaults
minsize = 100 #min trade size to include
main_session_only = True
force_remote = False

data = load_data(symbol = symbol,
                     agg_type = agg_type,
                     resolution = resolution,
                     start_date = day_start,
                     end_date = day_stop,
                     #exclude_conditions = None,
                     minsize = minsize,
                     main_session_only = main_session_only,
                     force_remote = force_remote,
                     return_vbt = True, #returns vbt object
                     verbose = False
                     )

data.ohlcv.data[symbol[0]].lw.plot()

/Users/davidbrazda/Documents/Development/python/ttools/.venv/lib/python3.10/site-packages/vectorbtpro/data/base.py:1905: UserWarning: Symbols have mismatching index. Setting missing data points to NaN.
  data = cls_or_self.align_index(data, missing=missing_index, silence_warnings=silence_warnings)

In [5]:

data.ohlcv.data[symbol[0]]

Out[5]:

<style scoped=""> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style>

	open	high	low	close	volume
time
2024-10-14 09:45:00-04:00	41.9650	41.970	41.950	41.9500	17895.0
2024-10-14 09:45:12-04:00	41.9589	41.965	41.950	41.9650	6281.0
2024-10-14 09:45:24-04:00	41.9650	42.005	41.965	41.9975	3522.0
2024-10-14 09:45:36-04:00	41.9900	42.005	41.990	42.0000	5960.0
2024-10-14 09:45:48-04:00	42.0050	42.040	42.005	42.0300	9113.0
...	...	...	...	...	...
2024-10-16 15:00:00-04:00	42.9150	42.915	42.910	42.9100	12872.0
2024-10-16 15:00:12-04:00	42.9150	42.920	42.910	42.9200	7574.0
2024-10-16 15:00:24-04:00	42.9200	42.920	42.910	42.9200	1769.0
2024-10-16 15:00:36-04:00	42.9200	42.920	42.905	42.9050	26599.0
2024-10-16 15:00:48-04:00	42.9050	42.905	42.880	42.8800	9216.0

5480 rows × 5 columns

Prepare daily trade cache¶

This is how to prepare trade cache for given symbol and period (if daily trades are not cached they are remotely fetched.)

In [ ]:

symbols = ["BAC", "AAPL"]
#datetime in zoneNY 
day_start = datetime(2024, 10, 1, 9, 45, 0)
day_stop = datetime(2024, 10, 27, 15, 1, 0)
day_start = zoneNY.localize(day_start)
day_stop = zoneNY.localize(day_stop)
force_remote = False

prepare_trade_cache(symbols, day_start, day_stop, force_remote, verbose = True)

Prepare daily trade cache - cli script¶

Python script prepares trade cache for specified symbols and date range.

Usually 1 day takes about 35s. It is stored in /tradescache/ directory as daily file keyed by symbol.

To run this script in the background with specific arguments:

# Running without forcing remote fetch
python3 prepare_cache.py --symbols BAC AAPL --day_start 2024-10-14 --day_stop 2024-10-18 &

# Running with force_remote set to True
python3 prepare_cache.py --symbols BAC AAPL --day_start 2024-10-14 --day_stop 2024-10-18 --force_remote &