Update parser

src/bitfinex_crypto_parser.py

@@ -1,42 +1,41 @@
 #!/usr/bin/python3
 
 """
-Data source: https://www.kaggle.com/code/tencars/bitfinexdataset
+Data source: https://www.kaggle.com/datasets/tencars/392-crypto-currency-pairs-at-minute-resolution
 """
 
 # %%
 import os
+
 import numpy as np
 import pandas as pd
 from sqlalchemy import types
 
 from azure import AzureDbConnection, ConnectionSettings
 
-
 # %%
 
-#> ~/apps/resistance/data
+# In terminal:
+#> kaggle -v # must be >1.15
+#> mkdir data; cd data
 #> kaggle datasets download tencars/392-crypto-currency-pairs-at-minute-resolution
 #> unzip 392-crypto-currency-pairs-at-minute-resolution.zip
 
-input_path = "../data"
+input_dir = "../data"
 
 # Get names and number of available currency pairs
-pair_names = [x[:-4] for x in os.listdir(input_path)]
-n_pairs = len(pair_names)
+pair_names = [x[:-4] for x in os.listdir(input_dir)]
+usd_pairs = [s for s in pair_names if "usd" in s]
 
 # Print the first 50 currency pair names
-print("These are the first 50 out of {} currency pairs in the dataset:".format(n_pairs))
-print(pair_names[0:50])
-
-usd_pairs = [s for s in pair_names if "usd" in s]
-print(usd_pairs)
+print(f"These are the first 10 out of {len(usd_pairs)} currency pairs in the dataset:")
+print(usd_pairs[0:10])
 
 
 # %%
 
-def load_data(symbol, source=input_path):
-    path_name = source + "/" + symbol + ".csv"
+def load_data(symbol: str, input_dir: str) -> pd.DataFrame:
+    path_name = input_dir + "/" + symbol + ".csv"
 
     # Load data
     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})
@@ -50,23 +49,50 @@ def load_data(symbol, source=input_path):
     return df[['open', 'high', 'low', 'close', 'volume']]
 
 
+def calc_ohlcv_1h(df: pd.DataFrame) -> pd.DataFrame:
+    df['hour'] = df.index.to_period('H')
+    
+    return (
+        df
+            .groupby(['hour'])
+            .agg(
+                {
+                    'open': 'first',
+                    'high': max,
+                    'low': min,
+                    'close': 'last',
+                    'volume': sum,
+                    #'time': max
+                }
+            )
+            .reset_index()
+        )
+
+
 # %% ----
-sample_df = load_data("ethusd")
-sample_df
+ethusd_1m = load_data("ethusd", input_dir)
+ethusd_1h = calc_ohlcv_1h(ethusd_1m)
+
+ethusd_1h.tail()
 
 
 
 # %% ----
-db_conn  = AzureDbConnection(conn_settings)
+conn_settings = ConnectionSettings(
+    'datainstinct',
+    'market-data-db',
+    'demo',
+    '0test_test_AND_test'
+)
 
+db_conn = AzureDbConnection(conn_settings)
 db_conn.connect()
+
 for t in db_conn.get_tables():
     print(t)
 
 
 # %%
-min_candels_n = 10000
-
 db_mapping = {
     'FIGI': types.CHAR(length=12),
     'open': types.DECIMAL(precision=19, scale=9),
@@ -75,28 +101,43 @@ db_mapping = {
     'low': types.DECIMAL(precision=19, scale=9),
     'volume': types.DECIMAL(precision=19, scale=9),
     'time': types.DATETIME(),
-    'source_id': types.SMALLINT,
+    'source_id': types.SMALLINT(),
     'version': types.VARCHAR(length=12),
     'interval': types.CHAR(length=2)
 }
 
+
+# %%
+pd.options.mode.chained_assignment = None 
+
+min_candels_n = 10000
+
 for pair in usd_pairs:
-    print(f'Starting read {pair}...')
-    candles_df = load_data(pair)
+    print(f'INFO | {pair} > Starting read dataset...')
 
-    candles_df['FIGI'] = pair
-    candles_df['time'] = candles_df.index
-    candles_df['source_id'] = 128
-    candles_df['version'] = 'v202206'
-    candles_df['interval'] = '1M'
+    candles_df = load_data(pair, input_dir)
 
-    if candles_df.shape[0] > min_candels_n:
-        print('{} rows from {} to {}'.format(candles_df.shape[0], min(candles_df['time']), max(candles_df['time'])))
+    if len(candles_df) > min_candels_n:
 
-        print(f'Starting insert {pair}...')
-        db_conn.insert(candles_df, 'crypto', db_mapping)
+        df = candles_df.loc['2022-07-01':'2022-10-01']
+
+        if len(df) > 0:
+            df = calc_ohlcv_1h(df)
+
+            df['FIGI'] = pair
+            df['time'] = df.hour.apply(lambda h: h.to_timestamp())
+            df['source_id'] = 1
+            df['version'] = 'v20221001'
+            df['interval'] = '1H'
+            df.drop(columns='hour', inplace=True)
+
+            print(f'INFO | {pair} > Starting insert to DB...')
+            print('DEBUG | {} rows from {} to {}'.format(df.shape[0], min(df['time']), max(df['time'])))
+            db_conn.insert(df, 'crypto', db_mapping)
+        else:
+            print(f'WARN | {pair} > No new records')
     else:
-        print(f'WARN: {pair} has only {candles_df.shape[0]} records')
+        print(f'WARN | {pair} > Only {candles_df.shape[0]} records')
 
 
 # %%