integrate hyperopt and implement subcommand

freqtrade/optimize/__init__.py

@@ -1 +1,41 @@
-from . import backtesting
+# pragma pylint: disable=missing-docstring
+
+
+import json
+import os
+from typing import Optional, List, Dict
+
+from pandas import DataFrame
+
+from freqtrade.analyze import populate_indicators, parse_ticker_dataframe
+
+
+def load_data(ticker_interval: int = 5, pairs: Optional[List[str]] = None) -> Dict[str, List]:
+    """
+    Loads ticker history data for the given parameters
+    :param ticker_interval: ticker interval in minutes
+    :param pairs: list of pairs
+    :return: dict
+    """
+    path = os.path.abspath(os.path.dirname(__file__))
+    result = {}
+    _pairs = pairs or [
+        'BTC_BCC', 'BTC_ETH', 'BTC_DASH', 'BTC_POWR', 'BTC_ETC',
+        'BTC_VTC', 'BTC_WAVES', 'BTC_LSK', 'BTC_XLM', 'BTC_OK',
+    ]
+    for pair in _pairs:
+        with open('{abspath}/../tests/testdata/{pair}-{ticker_interval}.json'.format(
+            abspath=path,
+            pair=pair,
+            ticker_interval=ticker_interval,
+        )) as tickerdata:
+            result[pair] = json.load(tickerdata)
+    return result
+
+
+def preprocess(tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
+    """Creates a dataframe and populates indicators for given ticker data"""
+    processed = {}
+    for pair, pair_data in tickerdata.items():
+        processed[pair] = populate_indicators(parse_ticker_dataframe(pair_data))
+    return processed