give user ability to analyze live trade dataframe inside custom prediction model. Add documentation to explain new functionality

freqtrade/freqai/data_kitchen.py

@@ -2,6 +2,7 @@ import copy
 import datetime
 import logging
 import shutil
+import sqlite3
 from pathlib import Path
 from typing import Any, Dict, List, Tuple
 
@@ -39,7 +40,7 @@ class FreqaiDataKitchen:
     Robert Caulk @robcaulk
 
     Theoretical brainstorming:
-    Elin Törnquist @thorntwig
+    Elin Törnquist @th0rntwig
 
     Code review, software architecture brainstorming:
     @xmatthias
@@ -84,6 +85,12 @@ class FreqaiDataKitchen:
                 config["freqai"]["backtest_period_days"],
             )
 
+        db_url = self.config.get('db_url', None)
+        self.database_path = '' if db_url == 'sqlite://' else str(db_url).split('///')[1]
+        self.trade_database_df: DataFrame = pd.DataFrame()
+
+        self.data['extra_returns_per_train'] = self.freqai_config.get('extra_returns_per_train', {})
+
     def set_paths(
         self,
         pair: str,
@@ -101,7 +108,7 @@ class FreqaiDataKitchen:
 
         self.data_path = Path(
             self.full_path
-            / str("sub-train" + "-" + pair.split("/")[0] + "_" + str(trained_timestamp))
+            / f"sub-train-{pair.split('/')[0]}_{trained_timestamp}"
         )
 
         return
@@ -328,7 +335,7 @@ class FreqaiDataKitchen:
         """
 
         for label in self.label_list:
-            if df[label].dtype == str:
+            if df[label].dtype == object:
                 continue
             df[label] = (
                 (df[label] + 1)
@@ -493,7 +500,6 @@ class FreqaiDataKitchen:
         tc = self.freqai_config.get("model_training_parameters", {}).get("thread_count", -1)
         pairwise = pairwise_distances(self.data_dictionary["train_features"], n_jobs=tc)
         avg_mean_dist = pairwise.mean(axis=1).mean()
-        logger.info(f"avg_mean_dist {avg_mean_dist:.2f}")
 
         return avg_mean_dist
 
@@ -599,10 +605,11 @@ class FreqaiDataKitchen:
         from the training data set.
         """
 
+        tc = self.freqai_config.get("model_training_parameters", {}).get("thread_count", -1)
         distance = pairwise_distances(
             self.data_dictionary["train_features"],
             self.data_dictionary["prediction_features"],
-            n_jobs=-1,
+            n_jobs=tc,
         )
 
         self.DI_values = distance.min(axis=0) / self.data["avg_mean_dist"]
@@ -946,6 +953,19 @@ class FreqaiDataKitchen:
         ]
         return dataframe[to_keep]
 
+    def get_current_trade_database(self) -> None:
+
+        if self.database_path == '':
+            logger.warning('No trade databse found. Skipping analysis.')
+            return
+
+        data = sqlite3.connect(self.database_path)
+        query = data.execute("SELECT * From trades")
+        cols = [column[0] for column in query.description]
+        df = pd.DataFrame.from_records(data=query.fetchall(), columns=cols)
+        self.trade_database_df = df.dropna(subset='close_date')
+        data.close()
+
     def np_encoder(self, object):
         if isinstance(object, np.generic):
             return object.item()