Merge pull request #393 from gcarq/balancing_hyperopt_2

Balancing hyperopt objective

freqtrade/optimize/hyperopt.py

@@ -30,18 +30,19 @@ logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
 logger = logging.getLogger(__name__)
 
 # set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data
-TARGET_TRADES = 1100
+TARGET_TRADES = 600
 TOTAL_TRIES = 0
 _CURRENT_TRIES = 0
 CURRENT_BEST_LOSS = 100
 
 # max average trade duration in minutes
 # if eval ends with higher value, we consider it a failed eval
-MAX_ACCEPTED_TRADE_DURATION = 240
+MAX_ACCEPTED_TRADE_DURATION = 300
 
 # this is expexted avg profit * expected trade count
 # for example 3.5%, 1100 trades, EXPECTED_MAX_PROFIT = 3.85
-EXPECTED_MAX_PROFIT = 3.85
+# check that the reported Σ% values do not exceed this!
+EXPECTED_MAX_PROFIT = 3.0
 
 # Configuration and data used by hyperopt
 PROCESSED = None  # optimize.preprocess(optimize.load_data())
@@ -133,10 +134,11 @@ def log_results(results):
 
     if results['loss'] < CURRENT_BEST_LOSS:
         CURRENT_BEST_LOSS = results['loss']
-        logger.info('{:5d}/{}: {}'.format(
+        logger.info('{:5d}/{}: {}. Loss {:.5f}'.format(
             results['current_tries'],
             results['total_tries'],
-            results['result']))
+            results['result'],
+            results['loss']))
     else:
         print('.', end='')
         sys.stdout.flush()
@@ -144,9 +146,9 @@ def log_results(results):
 
 def calculate_loss(total_profit: float, trade_count: int, trade_duration: float):
     """ objective function, returns smaller number for more optimal results """
-    trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2)
+    trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
     profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
-    duration_loss = min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
+    duration_loss = 0.7 + 0.3 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
     return trade_loss + profit_loss + duration_loss
 
 
@@ -190,12 +192,13 @@ def optimizer(params):
 
 def format_results(results: DataFrame):
     return ('{:6d} trades. Avg profit {: 5.2f}%. '
-            'Total profit {: 11.8f} BTC. Avg duration {:5.1f} mins.').format(
+            'Total profit {: 11.8f} BTC ({:.4f}Σ%). Avg duration {:5.1f} mins.').format(
                 len(results.index),
                 results.profit_percent.mean() * 100.0,
                 results.profit_BTC.sum(),
+                results.profit_percent.sum(),
                 results.duration.mean() * 5,
-    )
+            )
 
 
 def buy_strategy_generator(params):