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Remove redundant
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codez0mb1e
parent
5c6ea177dd
commit
37ec6bef93
66
src/azure.py
66
src/azure.py
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# %% Import dependencies ----
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from dataclasses import dataclass
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from typing import Dict, Any
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from sqlalchemy import create_engine, inspect
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import pandas as pd
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import urllib
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# %% Models
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@dataclass(frozen=True)
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class ConnectionSettings:
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"""Connection Settings"""
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server: str
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database: str
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username: str
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password: str
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driver: str = '{ODBC Driver 18 for SQL Server}'
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timeout: int = 30
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# %% Connection
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class AzureDbConnection:
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"""Azure SQL database connection."""
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def __init__(self, conn_settings: ConnectionSettings, echo: bool = False) -> None:
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conn_params = urllib.parse.quote_plus(
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'Driver=%s;' % conn_settings.driver +
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'Server=tcp:%s.database.windows.net,1433;' % conn_settings.server +
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'Database=%s;' % conn_settings.database +
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'Uid=%s;' % conn_settings.username +
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'Pwd=%s;' % conn_settings.password +
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'Encrypt=yes;' +
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'TrustServerCertificate=no;' +
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'Connection Timeout=%s;' % conn_settings.timeout
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)
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conn_string = f'mssql+pyodbc:///?odbc_connect={conn_params}'
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self._db = create_engine(conn_string, echo=echo)
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def connect(self) -> None:
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"""Estimate connection"""
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self._conn = self._db.connect()
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def get_tables(self) -> list[str]:
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"""Get list of tables"""
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inspector = inspect(self._db)
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return [t for t in inspector.get_table_names()]
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def insert(self, inserted_data: pd.DataFrame, target_table: str, db_mapping: Dict[str, Any], chunksize: int = 10000) -> None:
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inserted_data.to_sql(
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con=self._db,
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schema='dbo',
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name=target_table,
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if_exists='append', # or replace
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index=False,
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chunksize=chunksize,
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dtype=db_mapping
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)
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def dispose(self) -> None:
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"""Dispose opened connections"""
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self._conn.close()
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self._db.dispose()
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@ -1,150 +0,0 @@
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#!/usr/bin/python3
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"""
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Data source: https://www.kaggle.com/datasets/tencars/392-crypto-currency-pairs-at-minute-resolution
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"""
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# %%
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import os
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import numpy as np
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import pandas as pd
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from sqlalchemy import types
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from azure import AzureDbConnection, ConnectionSettings
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# %%
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# In terminal:
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#> kaggle -v # must be >1.15
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#> mkdir data; cd data
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#> kaggle datasets download tencars/392-crypto-currency-pairs-at-minute-resolution
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#> unzip 392-crypto-currency-pairs-at-minute-resolution.zip
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input_dir: str = "../data"
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# Get names and number of available currency pairs
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pair_names = [x[:-4] for x in os.listdir(input_dir)]
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usd_pairs = [s for s in pair_names if "usd" in s]
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# Print the first 50 currency pair names
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print(f"These are the first 10 out of {len(usd_pairs)} currency pairs in the dataset:")
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print(usd_pairs[0:10])
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# %%
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def load_data(symbol: str, input_dir: str) -> pd.DataFrame:
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path_name = input_dir + "/" + symbol + ".csv"
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# Load data
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df = pd.read_csv(path_name, index_col='time', dtype={'open': np.float64, 'high': np.float64, 'low': np.float64, 'close': np.float64, 'volume': np.float64})
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df.index = pd.to_datetime(df.index, unit='ms')
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df = df[~df.index.duplicated(keep='first')]
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# As mentioned in the description, bins without any change are not recorded.
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# We have to fill these gaps by filling them with the last value until a change occurs.
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#df = df.resample('1T').pad()
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return df[['open', 'high', 'low', 'close', 'volume']]
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def calc_ohlcv_1h(df: pd.DataFrame) -> pd.DataFrame:
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df['hour'] = df.index.to_period('H')
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return (
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df
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.groupby(['hour'])
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.agg(
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{
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'open': 'first',
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'high': max,
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'low': min,
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'close': 'last',
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'volume': sum,
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#'time': max
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}
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)
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.reset_index()
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)
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# %% ----
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ethusd_1m = load_data("ethusd", input_dir)
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ethusd_1h = calc_ohlcv_1h(ethusd_1m)
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ethusd_1h.tail()
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# %% ----
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conn_settings = ConnectionSettings(
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'<server_name>',
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'<db_name>',
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'<user_name>',
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'****'
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)
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db_conn = AzureDbConnection(conn_settings)
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db_conn.connect()
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for t in db_conn.get_tables():
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print(t)
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# %%
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db_mapping = {
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'FIGI': types.CHAR(length=12),
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'open': types.DECIMAL(precision=19, scale=9),
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'high': types.DECIMAL(precision=19, scale=9),
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'close': types.DECIMAL(precision=19, scale=9),
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'low': types.DECIMAL(precision=19, scale=9),
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'volume': types.DECIMAL(precision=19, scale=9),
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'time': types.DATETIME(),
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'source_id': types.SMALLINT(),
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'version': types.VARCHAR(length=12),
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'interval': types.CHAR(length=2)
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}
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# %%
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pd.options.mode.chained_assignment = None
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min_candels_n = 10000
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i = 1
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for pair in usd_pairs:
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print(f'INFO | {pair} > Starting read dataset...')
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candles_df = load_data(pair, input_dir)
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if len(candles_df) > min_candels_n:
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df = candles_df.loc[:'2022-10-01']
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if len(df) > 0:
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df = calc_ohlcv_1h(df)
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df['FIGI'] = pair
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df['time'] = df.hour.apply(lambda h: h.to_timestamp())
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df['source_id'] = 1
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df['version'] = 'v20221001'
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df['interval'] = '1H'
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df.drop(columns='hour', inplace=True)
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print(f'INFO | {pair} > Starting insert to DB ({i} of {len(usd_pairs)})...')
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print('DEBUG | {} rows from {} to {}'.format(df.shape[0], min(df['time']), max(df['time'])))
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try:
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db_conn.insert(df, 'crypto_1h', db_mapping)
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except Exception as ex:
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print(f'ERROR | {pair} > {ex}')
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else:
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print(f'WARN | {pair} > No new records')
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else:
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print(f'WARN | {pair} > Only {candles_df.shape[0]} records')
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i += 1
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# %%
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db_conn.dispose()
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