Add btanalysis file

freqtrade/data/btanalysis.py

@@ -0,0 +1,67 @@
+"""
+Helpers when analyzing backtest data
+"""
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from freqtrade.misc import json_load
+
+
+def load_backtest_data(filename) -> pd.DataFrame:
+    """
+    Load backtest data file.
+    :param filename: pathlib.Path object, or string pointing to the file.
+    :return a dataframe with the analysis results
+    """
+    if isinstance(filename, str):
+        filename = Path(filename)
+
+    if not filename.is_file():
+        raise ValueError("File {filename} does not exist.")
+
+    with filename.open() as file:
+        data = json_load(file)
+
+        # must align with columns in backtest.py
+    columns = ["pair", "profitperc", "open_time", "close_time", "index", "duration",
+               "open_rate", "close_rate", "open_at_end", "sell_reason"]
+
+    df = pd.DataFrame(data, columns=columns)
+
+    df['open_time'] = pd.to_datetime(df['open_time'],
+                                     unit='s',
+                                     utc=True,
+                                     infer_datetime_format=True
+                                     )
+    df['close_time'] = pd.to_datetime(df['close_time'],
+                                      unit='s',
+                                      utc=True,
+                                      infer_datetime_format=True
+                                      )
+    df['profitabs'] = df['close_rate'] - df['open_rate']
+    df = df.sort_values("open_time").reset_index(drop=True)
+    return df
+
+
+def evaluate_result_multi(results: pd.DataFrame, freq: str, max_open_trades: int) -> pd.DataFrame:
+    """
+    Find overlapping trades by expanding each trade once per period it was open
+    and then counting overlaps
+    :param results: Results Dataframe - can be loaded
+    :param freq: Frequency used for the backtest
+    :param max_open_trades: parameter max_open_trades used during backtest run
+    :return: dataframe with open-counts per time-period in freq
+    """
+    dates = [pd.Series(pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
+             for row in results[['open_time', 'close_time']].iterrows()]
+    deltas = [len(x) for x in dates]
+    dates = pd.Series(pd.concat(dates).values, name='date')
+    df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0), columns=results.columns)
+
+    df2 = df2.astype(dtype={"open_time": "datetime64", "close_time": "datetime64"})
+    df2 = pd.concat([dates, df2], axis=1)
+    df2 = df2.set_index('date')
+    df_final = df2.resample(freq)[['pair']].count()
+    return df_final[df_final['pair'] > max_open_trades]