mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-01 05:07:47 +00:00
Mark Aron Szulyovszky
cc7061b456
* fix(Core): correct forward returns calculation, classifiers are now working again, only train from when asset returns are available * feat(Utils): added get_first_valid_return_index() * feat(Ensemble): return models from `run_whole_pipeline` * feat(Ensemble): added ensemble step, fixed walk_forward_train_test predictions index confusion, * chore(Pipeline): remove unnecessary extra ensemble results dataframe * refactor(Core): removed unnecessary ensemble_train_predict, moved run_single_asset_trainig_pipeline to a separate file * feat(Training): added scaling on expanding window (the past) to walk_forward_train_test(), now printing out mean sharpe ratio * feat(CI): added environment.yml file * chore(Environment): update env.yml * feat(CI): added testing workflow * fix(CI): renamed enviroment.yml * fix(Tests): added missing new parameter to walk_forward_train_test()
52 lines
2.3 KiB
Python
52 lines
2.3 KiB
Python
# %%
|
|
import requests
|
|
import pandas as pd
|
|
|
|
AV_API_KEY = 'UY5VGSWBE88SHGI6'
|
|
CC_API_KEY = 'bfb8b5f54b21354608020a6654b370617b2fcabd2c8c2ce04ab881682a1d9dc9'
|
|
|
|
# %%
|
|
def get_crypto_price_crypto_compare(symbol: str, exchange: str, days: int) -> pd.DataFrame:
|
|
api_url = f'https://min-api.cryptocompare.com/data/v2/histoday?fsym={symbol}&tsym={exchange}&limit={days}&api_key={CC_API_KEY}'
|
|
raw = requests.get(api_url).json()
|
|
df = pd.DataFrame(raw['Data']['Data'])[['time', 'high', 'low', 'open', 'close']].set_index('time')
|
|
df.index = pd.to_datetime(df.index, unit = 's')
|
|
df.sort_index(inplace = True, ascending= True)
|
|
return df
|
|
|
|
ada = get_crypto_price_crypto_compare('ADA', 'USD', 1500)
|
|
ada
|
|
|
|
|
|
# %%
|
|
|
|
def get_crypto_price_av(symbol: str, exchange: str, start_date = None) -> pd.DataFrame:
|
|
api_url = f'https://www.alphavantage.co/query?function=DIGITAL_CURRENCY_DAILY&symbol={symbol}&market={exchange}&apikey={AV_API_KEY}'
|
|
raw_df = requests.get(api_url).json()
|
|
df = pd.DataFrame(raw_df['Time Series (Digital Currency Daily)']).T
|
|
df = df.rename(columns = {'1a. open (USD)': 'open', '2a. high (USD)': 'high', '3a. low (USD)': 'low', '4a. close (USD)': 'close', '5. volume': 'volume'})
|
|
for i in df.columns:
|
|
df[i] = df[i].astype(float)
|
|
df.index = pd.to_datetime(df.index)
|
|
df = df.iloc[::-1].drop(['1b. open (USD)', '2b. high (USD)', '3b. low (USD)', '4b. close (USD)', '6. market cap (USD)'], axis = 1)
|
|
if start_date:
|
|
df = df[df.index >= start_date]
|
|
df.sort_index(inplace = True, ascending= True)
|
|
return df
|
|
|
|
def get_stock_price_av(symbol: str, start_date: str = None) -> pd.DataFrame:
|
|
api_url = f'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={symbol}&outputsize=full&apikey={AV_API_KEY}'
|
|
raw_df = requests.get(api_url).json()
|
|
df = pd.DataFrame(raw_df['Time Series (Daily)']).T
|
|
df = df.rename(columns = {'1. open': 'open', '2. high': 'high', '3. low': 'low', '5. adjusted close': 'close', '6. volume': 'volume'})
|
|
for i in df.columns:
|
|
df[i] = df[i].astype(float)
|
|
df.index = pd.to_datetime(df.index)
|
|
df = df.iloc[::-1].drop(['4. close', '7. dividend amount', '8. split coefficient'], axis = 1)
|
|
if start_date:
|
|
df = df[df.index >= start_date]
|
|
df.sort_index(inplace = True, ascending= True)
|
|
df = df.rename_axis('time')
|
|
return df
|
|
|