Merge branch 'develop' into db_keep_orders

tests/rpc/test_rpc_apiserver.py

@@ -435,7 +435,7 @@ def test_api_logs(botclient):
     assert len(rc.json) == 2
     assert 'logs' in rc.json
     # Using a fixed comparison here would make this test fail!
-    assert rc.json['log_count'] > 10
+    assert rc.json['log_count'] > 1
     assert len(rc.json['logs']) == rc.json['log_count']
 
     assert isinstance(rc.json['logs'][0], list)
@@ -471,6 +471,7 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
     assert rc.json == {"error": "Error querying _edge: Edge is not enabled."}
 
 
+@pytest.mark.usefixtures("init_persistence")
 def test_api_profit(botclient, mocker, ticker, fee, markets, limit_buy_order, limit_sell_order):
     ftbot, client = botclient
     patch_get_signal(ftbot, (True, False))
@@ -498,6 +499,7 @@ def test_api_profit(botclient, mocker, ticker, fee, markets, limit_buy_order, li
     assert rc.json['best_pair'] == ''
     assert rc.json['best_rate'] == 0
 
+    trade = Trade.query.first()
     trade.update(limit_sell_order)
 
     trade.close_date = datetime.utcnow()

tests/strategy/test_strategy_helpers.py

@@ -0,0 +1,88 @@
+import pandas as pd
+import numpy as np
+
+from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes
+
+
+def generate_test_data(timeframe: str, size: int):
+    np.random.seed(42)
+    tf_mins = timeframe_to_minutes(timeframe)
+
+    base = np.random.normal(20, 2, size=size)
+
+    date = pd.period_range('2020-07-05', periods=size, freq=f'{tf_mins}min').to_timestamp()
+    df = pd.DataFrame({
+        'date': date,
+        'open': base,
+        'high': base + np.random.normal(2, 1, size=size),
+        'low': base - np.random.normal(2, 1, size=size),
+        'close': base + np.random.normal(0, 1, size=size),
+        'volume': np.random.normal(200, size=size)
+    }
+    )
+    df = df.dropna()
+    return df
+
+
+def test_merge_informative_pair():
+    data = generate_test_data('15m', 40)
+    informative = generate_test_data('1h', 40)
+
+    result = merge_informative_pair(data, informative, '15m', '1h', ffill=True)
+    assert isinstance(result, pd.DataFrame)
+    assert len(result) == len(data)
+    assert 'date' in result.columns
+    assert result['date'].equals(data['date'])
+    assert 'date_1h' in result.columns
+
+    assert 'open' in result.columns
+    assert 'open_1h' in result.columns
+    assert result['open'].equals(data['open'])
+
+    assert 'close' in result.columns
+    assert 'close_1h' in result.columns
+    assert result['close'].equals(data['close'])
+
+    assert 'volume' in result.columns
+    assert 'volume_1h' in result.columns
+    assert result['volume'].equals(data['volume'])
+
+    # First 4 rows are empty
+    assert result.iloc[0]['date_1h'] is pd.NaT
+    assert result.iloc[1]['date_1h'] is pd.NaT
+    assert result.iloc[2]['date_1h'] is pd.NaT
+    assert result.iloc[3]['date_1h'] is pd.NaT
+    # Next 4 rows contain the starting date (0:00)
+    assert result.iloc[4]['date_1h'] == result.iloc[0]['date']
+    assert result.iloc[5]['date_1h'] == result.iloc[0]['date']
+    assert result.iloc[6]['date_1h'] == result.iloc[0]['date']
+    assert result.iloc[7]['date_1h'] == result.iloc[0]['date']
+    # Next 4 rows contain the next Hourly date original date row 4
+    assert result.iloc[8]['date_1h'] == result.iloc[4]['date']
+
+
+def test_merge_informative_pair_same():
+    data = generate_test_data('15m', 40)
+    informative = generate_test_data('15m', 40)
+
+    result = merge_informative_pair(data, informative, '15m', '15m', ffill=True)
+    assert isinstance(result, pd.DataFrame)
+    assert len(result) == len(data)
+    assert 'date' in result.columns
+    assert result['date'].equals(data['date'])
+    assert 'date_15m' in result.columns
+
+    assert 'open' in result.columns
+    assert 'open_15m' in result.columns
+    assert result['open'].equals(data['open'])
+
+    assert 'close' in result.columns
+    assert 'close_15m' in result.columns
+    assert result['close'].equals(data['close'])
+
+    assert 'volume' in result.columns
+    assert 'volume_15m' in result.columns
+    assert result['volume'].equals(data['volume'])
+
+    # Dates match 1:1
+    assert result['date_15m'].equals(result['date'])