From 4b0578623002912023815665cbc459a8e5ef6f78 Mon Sep 17 00:00:00 2001
From: EmbraysiteReal <46327982+EmbraysiteReal@users.noreply.github.com>
Date: Thu, 9 Dec 2021 12:17:51 +0100
Subject: [PATCH] Mdd almost final push

Correction bug close mark & compute mdd for each pair
---
 freqtrade/data/btanalysis.py | 38 +++++++++++++++++++++++++++---------
 1 file changed, 29 insertions(+), 9 deletions(-)

diff --git a/freqtrade/data/btanalysis.py b/freqtrade/data/btanalysis.py
index 2c1995165..7d32ab0f1 100644
--- a/freqtrade/data/btanalysis.py
+++ b/freqtrade/data/btanalysis.py
@@ -409,25 +409,45 @@ def calculate_mdd(data: dict, trades: pd.DataFrame, *, date_col: str = 'close_da
     
     mdd_df = pd.DataFrame()
 
+    mdd_pair_list = []
     
     for pair, df in data.items():
 
-        trades_aux = trades.loc[trades['pair']==pair][["open_date","close_date"]]
-        trades_aux = pd.concat([trades_aux.rename(columns={'open_date':'date'})[['date']],
-                                trades_aux.rename(columns={'close_date':'date'})[['date']]]
-                               ).sort_values(by='date')
-        trades_aux['open_close_mark'] = np.ones(len(trades_aux), dtype=int)
-        data_join = df.set_index('date').join(trades_aux.set_index('date'))
-        data_join["open_close_mark"] = data_join["open_close_mark"].fillna(0).astype(int)
+        open_close_trades = trades.loc[trades['pair']==pair][["open_date","close_date"]]
+        open_close_trades = pd.concat([open_close_trades.rename(columns={'open_date':'date'})[['date']],
+                                       open_close_trades.rename(columns={'close_date':'date'})[['date']]]
+                                     ).sort_values(by='date')
+        open_close_trades['open_close_mark'] = np.ones(len(open_close_trades), dtype=int)
+        data_join = df.set_index('date').join(open_close_trades.set_index('date'))
+        del open_close_trades
+
+        close_trades = trades.loc[trades['pair']==pair][["close_date"]]
+        close_trades = close_trades.rename(columns={'close_date':'date'})
+        close_trades['close_mark'] = np.ones(len(close_trades), dtype=int)
+        data_join = data_join.join(close_trades.set_index('date'))
+        del close_trades
+
+        data_join[["open_close_mark",'close_mark']] = data_join[["open_close_mark",'close_mark']].fillna(0).astype(int)
         data_join['is_in_trade'] = data_join.open_close_mark.cumsum()&1 # &1 <=> %2
         data_join.loc[data_join['open_close_mark'] == 1, 'is_in_trade'] = 1
         data_join['close_cummax'] = 0
 
+        data_join["open_close_mark"] = data_join["open_close_mark"] - data_join["close_mark"]
+        
         data_join['close_cummax'] = data_join.groupby(data_join.open_close_mark.cumsum()).close.cummax()
         data_join.loc[data_join['is_in_trade'] == 0, 'close_cummax'] = 0
-        
+
+        data_join = data_join.rename(columns={'close_mark':'drawdown'})
+        data_join['drawdown'] = data_join['close_cummax'] - data_join['close']
+
+        mdd_pair = data_join['drawdown'].max()
+        mdd_pair_list.append(mdd_pair)
         # print(mdd_df)
-    return mdd_df
+    
+    mdd_pair_list = np.ndarray(mdd_pair_list)
+
+
+    return mdd_pair_list.max()
 
 def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:
     """