mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
81c217a401
* feat(DataLoader): added load_only_returns() method * feat(Portfolio): load predictions * feat(Portfolio): normalize weights * feat(Portfolio): started integrating with portfoliobt * feat(Portfolio): include fees in the portfolio construction * feat(Portfolio): demo of pyportfolioopt * feat(Portfolio): get efficient frontier calculation to work * feat(Portfolio): add a few strategies to create weights * chore(Dependencies): remove pyportfolioopt for now * fix(Dependencies): try to install all dependencies with pip * fix(Dependencies): indentation * fix(Dependencies): corrected pytorch module name * fix(Dependencies): try to have as many modules installed by conda for the sake of sanity? * fix(Dependencies): put fracdiff into pip modules * fix(Dependencies): revert to using pip almost exclusively * feat(Portfolio): added alphalens * fix(Portfolio): got limited weights working * feat(Portfolio): trying to get alphalens to work * feat(Portfolio): alphalens working * fix(Dependencies): removed vectorbt * fix(Dependencies): use alphalens-reloaded * fix(Dependencies): added conda source for alphalens-reloaded * refactor(Portfolio): removed traces of vectorbt * feat(Reporting): factor reporting done * feat(Portfolio): added pyfolio reporting (fails bc alphalens is not working properly lol)
39 lines
807 B
Python
39 lines
807 B
Python
# #%%
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# import pandas as pd
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# import pandas_ta as ta
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# from config.config import get_default_level_2_daily_config
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# from config.preprocess import preprocess_config
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# from data_loader.load_data import load_data
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# # %%
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# model_config, training_config, data_config = get_default_level_2_daily_config()
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# model_config, training_config, data_config = preprocess_config(model_config, training_config, data_config)
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# data_config['target_asset'] = data_config['assets'][0]
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# X, y, target_returns = load_data(**data_config)
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# # %%
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# X.ta.donchian()
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# # %%
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# X.ta.ema()
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# # %%
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# X.ta.adjusted = "ADA_USD_returns"
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# # %%
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# X.ta.sma(length=10)
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# # %%
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# X
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# # %%
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# X.ta.categories
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# # %%
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# ind_list = X.ta.indicators(as_list=True)
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# # %%
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# ind_list
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# # %%
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# X.ta.ao('ADA_USD_returns', length=10)
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# # %%
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# ta.ao() |