Integrated recommendation strategy

app/models/backtest.py

@@ -8,7 +8,7 @@ class Backtest(db.Model):
     start_date = db.Column(db.Date)
     end_date = db.Column(db.Date)
     inital_cash = db.Column(db.Integer)
-    fee = db.Column(db.Integer)
+    fee = db.Column(db.Float)
     # status = db.Column(db.String(50))
     created_at = db.Column(db.DateTime, default=db.func.current_timestamp())
 
@@ -36,6 +36,7 @@ class Result(db.Model):
     losing_trades = db.Column(db.Integer)
     max_drawdown = db.Column(db.Numeric(10, 2))
     sharpe_ratio = db.Column(db.Numeric(10, 2))
+    is_best = db.Column(db.Boolean, default=False, nullable=True)
 
 class Metric(db.Model):
     __tablename__ = 'metrics'

app/routes/backtest.py

@@ -80,7 +80,8 @@ def get_backtest_results(backtest_id):
             'winning_trades': result.winning_trades,
             'losing_trades': result.losing_trades,
             'max_drawdown': float(result.max_drawdown),
-            'sharpe_ratio': float(result.sharpe_ratio)
+            'sharpe_ratio': float(result.sharpe_ratio),
+            'is_best': result.is_best
         })
 
     return jsonify({'results': result_list}), 200

app/services/backtest_service.py

@@ -41,19 +41,16 @@ def run_and_evaluate_backtest(backtest_id, symbol, initial_cash, fee, start_date
     ]
 
     results = []
+    result_objects = []
     for strategy in strategies:
-
         print(strategy, symbol, initial_cash, fee, start_date, end_date)
         result = run_backtest(strategy, symbol, initial_cash, fee, start_date, end_date)
         result['backtest_id'] = backtest_id
-        if(strategy == RsiBollingerBandsStrategy):
-            result['strategy'] = 'RsiBollingerBandsStrategy'
-        elif (strategy == MacdStrategy):
-            result['strategy'] = 'MacdStrategy'
-        elif(strategy == StochasticOscillatorStrategy):
-            result['strategy'] = 'StochasticOscillatorStrategy'
+        result['strategy'] = strategy.__name__
+
         result_obj = Result(**result)
         db.session.add(result_obj)
+        result_objects.append(result_obj)
 
         metrics = {
             "total_return": result['total_return'],
@@ -65,11 +62,11 @@ def run_and_evaluate_backtest(backtest_id, symbol, initial_cash, fee, start_date
         }
         mlflow_service.log_metrics(run_name=f"Backtest_{backtest_id}", metrics=metrics)
 
-        # Uncomment to publish results to Kafka
-        # kafka_service.produce('backtest_results', {
-        #     "backtest_id": backtest_id,
-        #     "metrics": metrics
-        # })
+        # publish results to Kafka
+        kafka_service.produce('backtest_results', {
+            "backtest_id": backtest_id,
+            "metrics": metrics
+        })
 
         db.session.commit()
         results.append(result)
@@ -84,10 +81,14 @@ def run_and_evaluate_backtest(backtest_id, symbol, initial_cash, fee, start_date
     scores = [score_backtest(result, min_return, max_return, min_sharpe, max_sharpe, min_drawdown, max_drawdown) for result in results]
 
     best_strategy_index = scores.index(max(scores))
-    best_strategy = strategies[best_strategy_index]
 
+    for idx, result_obj in enumerate(result_objects):
+        result_obj.is_best = (idx == best_strategy_index)
+
+    db.session.commit()
+
     print("Best Strategy:")
-    print(best_strategy.__name__)
+    print(strategies[best_strategy_index].__name__)
     print("Score:")
     print(scores[best_strategy_index])
     print("Metrics:")