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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
b656f790f5
* feat(Data): resample exogenous/other datasource when their frequency is different * fix(Linter): ran * fix(Data): resampling done properly
65 lines
1.3 KiB
Python
65 lines
1.3 KiB
Python
from typing import Literal
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from .types import DataSource, DataCollection
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def transform_to_data_collection(
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path: str, file_names: list[str], freq: Literal["5m", "1h", "1d"]
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) -> DataCollection:
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return [DataSource(path, file_name, freq) for file_name in file_names]
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__daily_etf = ["GLD", "IEF", "QQQ", "SPY", "TLT"]
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__5min_crypto = [
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"TRXUSDT",
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"XRPUSDT",
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"ADAUSDT",
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"SOLUSDT",
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"AVAXUSDT",
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"DOTUSDT",
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"ETHUSDT",
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"LTCUSDT",
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"BNBUSDT",
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"BTCUSDT",
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"ETCUSDT",
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]
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__daily_glassnode = [
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"rhodl_ratio",
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# 'cvdd',
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"nvt_ratio",
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"nvt_signal",
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"velocity",
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"supply_adjusted_cdd",
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"binary_cdd",
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"supply_adjusted_dormancy",
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"puell_multiple",
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"asopr",
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"reserve_risk",
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"sopr",
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"cdd",
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"asol",
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"msol",
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"dormancy",
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"liveliness",
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"relative_unrealized_profit",
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"relative_unrealized_loss",
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"nupl",
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"sth_nupl",
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"lth_nupl",
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"ssr",
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"bvin",
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# 'hash_rate'
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]
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data_collections = dict(
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daily_etf=transform_to_data_collection("data/daily_etf", __daily_etf, "1d"),
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fivemin_crypto=transform_to_data_collection(
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"data/5min_crypto", __5min_crypto, "5m"
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),
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daily_glassnode=transform_to_data_collection(
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"data/daily_glassnode", __daily_glassnode, "1d"
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),
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)
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