def main(data_path: str = None):
'''
Download commodities price history from
https://blog.quandl.com/api-for-commodity-data
Note:
To download this dataset you need to register at quandl
and paste token to `~/.ml_investment/secrets.json`
Parameters
----------
data_path:
path to folder in which downloaded data will be stored.
OR ``None`` (downloading path will be as ``commodities_data_path`` from
`~/.ml_investment/config.json`
'''
if data_path is None:
config = load_config()
data_path = config['commodities_data_path']
downloader = QuandlDownloader(sleep_time=0.8)
for code in tqdm(quandl_commodities_codes):
downloader.single_download(
'datasets/{}'.format(code),
'{}/{}.json'.format(data_path, code.replace('/', '_')))
def main(data_path: str=None):
'''
Download daily price bars for base US stocks and indexes.
Parameters
----------
data_path:
path to folder in which downloaded data will be stored.
OR ``None`` (downloading path will be as ``daily_bars_data_path`` from
`~/.ml_investment/config.json`
'''
if data_path is None:
config = load_config()
data_path = config['daily_bars_data_path']
global _data_path
_data_path = data_path
tickers = load_tickers()['base_us_stocks']
index_tickers = ['SPY', 'TLT', 'QQQ']
os.makedirs(data_path, exist_ok=True)
p = Pool(8)
for _ in tqdm(p.imap(_single_ticker_download,
tickers + index_tickers)):
None
def main(data_path :str=None):
'''
Download quarterly fundamental data from
https://www.quandl.com/databases/SF1/data
Note:
SF1 is paid, so you need to subscribe
and paste quandl token to `~/.ml_investment/secrets.json`
``quandl_api_key``
Parameters
----------
data_path:
path to folder in which downloaded data will be stored.
OR ``None`` (downloading path will be as ``sf1_data_path`` from
`~/.ml_investment/config.json`
'''
if data_path is None:
config = load_config()
data_path = config['sf1_data_path']
downloader = QuandlDownloader(sleep_time=0.8)
downloader.zip_download('datatables/SHARADAR/TICKERS?qopts.export=true',
'{}/tickers.zip'.format(data_path))
base_df = SF1BaseData(data_path).load()
tickers = base_df['ticker'].unique().tolist()
downloader.ticker_download('datatables/SHARADAR/SF1?ticker={ticker}', tickers,
save_dirpath='{}/core_fundamental'.format(data_path),
skip_exists=False, batch_size=10, n_jobs=4)
downloader.ticker_download('datatables/SHARADAR/DAILY?ticker={ticker}', tickers,
save_dirpath='{}/daily'.format(data_path),
skip_exists=False, batch_size=5, n_jobs=4)
def main(data_path: str = None):
'''
Download quarterly and base data from https://finance.yahoo.com
Parameters
----------
data_path:
path to folder in which downloaded data will be stored.
OR ``None`` (downloading path will be as ``yahoo_data_path`` from
`~/.ml_investment/config.json`
'''
if data_path is None:
config = load_config()
data_path = config['yahoo_data_path']
tickers = load_tickers()['base_us_stocks']
downloader = YahooDownloaderV1()
downloader.download_quarterly_data(data_path, tickers)
downloader.download_base_data(data_path, tickers)
def main(data_path: str = None):
'''
Download quarterly and base data from https://finance.yahoo.com
Parameters
----------
data_path:
path to folder in which downloaded data will be stored.
OR ``None`` (downloading path will be as ``yahoo_data_path`` from
`~/.ml_investment/config.json`
'''
if data_path is None:
config = load_config()
data_path = config['yahoo_data_path']
global _data_path
_data_path = data_path
tickers = load_tickers()['base_us_stocks']
os.makedirs('{}/quarterly'.format(data_path), exist_ok=True)
os.makedirs('{}/base'.format(data_path), exist_ok=True)
p = Pool(12)
for _ in tqdm(p.imap(_single_ticker_download, tickers)):
None
import lightgbm as lgbm
import catboost as ctb
from urllib.request import urlretrieve
from ml_investment.utils import load_config, load_tickers
from ml_investment.data_loaders.yahoo import YahooBaseData, YahooQuarterlyData
from ml_investment.data_loaders.daily_bars import DailyBarsData
from ml_investment.features import QuarterlyFeatures, BaseCompanyFeatures, \
FeatureMerger, DailyAggQuarterFeatures, \
QuarterlyDiffFeatures
from ml_investment.targets import DailyAggTarget
from ml_investment.models import TimeSeriesOOFModel, EnsembleModel, LogExpModel
from ml_investment.metrics import median_absolute_relative_error, down_std_norm
from ml_investment.pipelines import Pipeline
from ml_investment.download_scripts import download_yahoo, download_daily_bars
config = load_config()
URL = 'https://github.com/fartuk/ml_investment/releases/download/weights/marketcap_down_std_yahoo.pickle'
OUT_NAME = 'marketcap_down_std_yahoo'
TARGET_HORIZON = 90
MAX_BACK_QUARTER = 2
FOLD_CNT = 5
QUARTER_COUNTS = [1, 2, 4]
COMPARE_QUARTER_IDXS = [1, 4]
CAT_COLUMNS = ["sector"]
QUARTER_COLUMNS = [
'totalRevenue',
'netIncome',
'cash',
'totalAssets',
'costOfRevenue',
