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Update parser
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codez0mb1e
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3882efc9a2
@ -1,42 +1,41 @@
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#!/usr/bin/python3
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"""
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Data source: https://www.kaggle.com/code/tencars/bitfinexdataset
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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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#> ~/apps/resistance/data
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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_path = "../data"
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input_dir = "../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_path)]
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n_pairs = len(pair_names)
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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("These are the first 50 out of {} currency pairs in the dataset:".format(n_pairs))
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print(pair_names[0:50])
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usd_pairs = [s for s in pair_names if "usd" in s]
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print(usd_pairs)
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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, source=input_path):
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path_name = source + "/" + symbol + ".csv"
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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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@ -50,23 +49,50 @@ def load_data(symbol, source=input_path):
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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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sample_df = load_data("ethusd")
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sample_df
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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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db_conn = AzureDbConnection(conn_settings)
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conn_settings = ConnectionSettings(
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'datainstinct',
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'market-data-db',
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'demo',
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'0test_test_AND_test'
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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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min_candels_n = 10000
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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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@ -75,28 +101,43 @@ db_mapping = {
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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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'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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for pair in usd_pairs:
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print(f'Starting read {pair}...')
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candles_df = load_data(pair)
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print(f'INFO | {pair} > Starting read dataset...')
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candles_df['FIGI'] = pair
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candles_df['time'] = candles_df.index
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candles_df['source_id'] = 128
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candles_df['version'] = 'v202206'
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candles_df['interval'] = '1M'
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candles_df = load_data(pair, input_dir)
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if candles_df.shape[0] > min_candels_n:
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print('{} rows from {} to {}'.format(candles_df.shape[0], min(candles_df['time']), max(candles_df['time'])))
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if len(candles_df) > min_candels_n:
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print(f'Starting insert {pair}...')
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db_conn.insert(candles_df, 'crypto', db_mapping)
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df = candles_df.loc['2022-07-01':'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...')
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print('DEBUG | {} rows from {} to {}'.format(df.shape[0], min(df['time']), max(df['time'])))
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db_conn.insert(df, 'crypto', db_mapping)
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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} has only {candles_df.shape[0]} records')
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print(f'WARN | {pair} > Only {candles_df.shape[0]} records')
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# %%
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