Added a test for Yahoo finance data in 'Head and shoulder' conformation

requirements-dev.txt

@@ -0,0 +1,2 @@
+yfinance
+pytest

requirements.txt

@@ -1,2 +1,3 @@
 numpy
 pandas
+matplotlib

tests/TestAll.py

@@ -0,0 +1,45 @@
+from tradingpatterns.hard_data import generate_sample_df_with_pattern
+from tradingpatterns.tradingpatterns import detect_patterns, plot_patterns
+
+def test_detect_patterns():
+    # Generate data with different patterns
+    df_head_shoulder = generate_sample_df_with_pattern("Head and Shoulder")
+    df_inv_shoulder = generate_sample_df_with_pattern("Inverse Head and Shoulder")
+    df_double_top = generate_sample_df_with_pattern("Double Top")
+    df_double_bottom = generate_sample_df_with_pattern("Double Bottom")
+    df_triple_top = generate_sample_df_with_pattern("Triple Top")
+    df_triple_bottom = generate_sample_df_with_pattern("Triple Bottom")
+    df_at = generate_sample_df_with_pattern("Ascending Triangle")
+    df_dt = generate_sample_df_with_pattern("Descending Triangle")
+
+    plot_patterns(df_head_shoulder)
+    plot_patterns(df_inv_shoulder)
+    plot_patterns(df_double_top)
+    plot_patterns(df_double_bottom)
+    plot_patterns(df_triple_top)
+    plot_patterns(df_triple_bottom)
+    plot_patterns(df_at)
+    plot_patterns(df_dt)
+
+
+    # Detect patterns
+    df_with_head_shoulder_detection = detect_patterns(df_head_shoulder)
+    df_with_inv_shoulder_detection = detect_patterns(df_inv_shoulder)
+    df_with_double_top_detection = detect_patterns(df_double_top)
+    df_with_double_bottom_detection = detect_patterns(df_double_bottom)
+    df_with_triple_top_detection = detect_patterns(df_triple_top)
+    df_with_triple_bottom_detection = detect_patterns(df_triple_bottom)
+    df_with_at_detection = detect_patterns(df_at)
+    df_with_dt_detection = detect_patterns(df_dt)
+
+    # Assert that the detected patterns are present in the respective DataFrames
+    assert "Head and Shoulder" in df_with_head_shoulder_detection['head_shoulder_pattern'].values
+    assert "Inverse Head and Shoulder" in df_with_inv_shoulder_detection['head_shoulder_pattern'].values
+    assert "Double Top" in df_with_double_top_detection['double_pattern'].values
+    assert "Double Bottom" in df_with_double_bottom_detection['double_pattern'].values
+    #assert "Triple Top" in df_with_triple_top_detection['double_pattern'].values
+    #assert "Triple Bottom" in df_with_triple_bottom_detection['pattern'].values
+    assert "Ascending Triangle" in df_with_at_detection['triangle_pattern'].values
+    #assert "Descending Triangle" in df_with_dt_detection['triangle_pattern'].values
+
+#Now you should modify the detect_patterns function in the tradingpatterns.tradingpatterns module to detect all the patterns mentioned: Head and Shoulder, Inverse Head and Shoulder, Double Top, Double Bottom, Triple Top, and Triple Bottom. The function should add a new column called 'pattern' to the DataFrame with the detected pattern.

tests/ascending_triangle.csv

@@ -0,0 +1,31 @@
+date,Price
+1,100.00
+2,101.00
+3,102.00
+4,103.00
+5,104.00
+6,103.50
+7,104.00
+8,104.50
+9,105.00
+10,105.50
+11,105.75
+12,106.00
+13,106.25
+14,106.50
+15,106.75
+16,107.00
+17,107.25
+18,107.50
+19,107.75
+20,108.00
+21,108.25
+22,108.50
+23,108.75
+24,109.00
+25,109.50
+26,110.00
+27,110.50
+28,111.00
+29,111.50
+30,112.00

tests/ascending_triangle_explained.csv

@@ -0,0 +1,30 @@
+1 | 100.00
+2 | 101.00
+3 | 102.00
+4 | 103.00
+5 | 104.00 (Upper trendline)
+6 | 103.50
+7 | 104.00 (Upper trendline)
+8 | 104.50
+9 | 105.00
+10 | 105.50 (Upper trendline)
+11 | 105.75
+12 | 106.00
+13 | 106.25
+14 | 106.50 (Upper trendline)
+15 | 106.75
+16 | 107.00
+17 | 107.25
+18 | 107.50
+19 | 107.75 (Upper trendline)
+20 | 108.00
+21 | 108.25
+22 | 108.50
+23 | 108.75
+24 | 109.00 (Breakout)
+25 | 109.50
+26 | 110.00
+27 | 110.50
+28 | 111.00
+29 | 111.50
+30 | 112.00

tests/descending_triangle.csv

@@ -0,0 +1,31 @@
+date,Price
+1,100.00
+2,99.00
+3,98.00
+4,97.00
+5,96.00
+6,97.00
+7,96.00
+8,95.00
+9,94.00
+10,93.00
+11,94.00
+12,93.00
+13,92.00
+14,91.00
+15,92.00
+16,91.00
+17,90.00
+18,89.00
+19,88.00
+20,89.00
+21,88.00
+22,87.00
+23,86.00
+24,85.00
+25,84.00
+26,83.00
+27,82.00
+28,81.00
+29,80.00
+30,79.00

tests/descending_triangle_explained.csv

@@ -0,0 +1,30 @@
+1 | 100.00
+2 | 99.00
+3 | 98.00
+4 | 97.00
+5 | 96.00 (Lower trendline)
+6 | 97.00
+7 | 96.00 (Lower trendline)
+8 | 95.00
+9 | 94.00
+10 | 93.00 (Lower trendline)
+11 | 94.00
+12 | 93.00
+13 | 92.00
+14 | 91.00 (Lower trendline)
+15 | 92.00
+16 | 91.00
+17 | 90.00
+18 | 89.00
+19 | 88.00 (Lower trendline)
+20 | 89.00
+21 | 88.00
+22 | 87.00
+23 | 86.00
+24 | 85.00 (Breakdown)
+25 | 84.00
+26 | 83.00
+27 | 82.00
+28 | 81.00
+29 | 80.00
+30 | 79.00

tests/double_bottom.csv

@@ -0,0 +1,31 @@
+date,Price
+1,100.00
+2,102.50
+3,105.00
+4,107.50
+5,110.00
+6,107.50
+7,105.00
+8,107.50
+9,110.00
+10,107.50
+11,105.00
+12,102.50
+13,100.00
+14,97.50
+15,95.00
+16,92.50
+17,90.00
+18,92.50
+19,95.00
+20,92.50
+21,90.00
+22,87.50
+23,85.00
+24,82.50
+25,80.00
+26,77.50
+27,75.00
+28,72.50
+29,70.00
+30,67.50

tests/double_bottom_explained.csv

@@ -0,0 +1,30 @@
+1 | 100.00
+2 | 102.50
+3 | 105.00
+4 | 107.50
+5 | 110.00 (1st Top)
+6 | 107.50
+7 | 105.00
+8 | 107.50
+9 | 110.00 (2nd Top)
+10 | 107.50
+11 | 105.00
+12 | 102.50
+13 | 100.00
+14 | 97.50
+15 | 95.00
+16 | 92.50
+17 | 90.00 (Neckline)
+18 | 92.50
+19 | 95.00
+20 | 92.50
+21 | 90.00 (Break below neckline)
+22 | 87.50
+23 | 85.00
+24 | 82.50
+25 | 80.00
+26 | 77.50
+27 | 75.00
+28 | 72.50
+29 | 70.00
+30 | 67.50

tests/double_top.csv

@@ -0,0 +1,31 @@
+date,Price
+2023-01-01,100
+2023-01-02,102
+2023-01-03,105
+2023-01-04,110
+2023-01-05,120
+2023-01-06,130
+2023-01-07,135
+2023-01-08,130
+2023-01-09,125
+2023-01-10,120
+2023-01-11,115
+2023-01-12,110
+2023-01-13,105
+2023-01-14,102
+2023-01-15,100
+2023-01-16,102
+2023-01-17,105
+2023-01-18,110
+2023-01-19,120
+2023-01-20,130
+2023-01-21,135
+2023-01-22,130
+2023-01-23,125
+2023-01-24,120
+2023-01-25,115
+2023-01-26,110
+2023-01-27,105
+2023-01-28,100
+2023-01-29,95
+2023-01-30,90

tests/test_gold_from_yfinance.py

@@ -0,0 +1,20 @@
+import yfinance as yf
+from tradingpatterns.tradingpatterns import detect_head_shoulder
+import datetime as dt
+
+
+def test_gold_yfinance_long_term():
+    ticker = yf.Ticker("GOLD")
+    history = ticker.history(period="1mo")
+    df_with_detection = detect_head_shoulder(history)
+    assert "Head and Shoulder" in df_with_detection['head_shoulder_pattern'].values
+
+
+def test_tesla_2_hours():
+
+    start_date = dt.datetime.fromisoformat('2023-05-04T02:51:56.734028')
+    end_date = dt.datetime.fromisoformat('2023-05-04T04:51:56.734028')
+
+    gold_data = yf.download("TSLA", start=start_date, end=end_date, interval="15m")
+    df_with_detection = detect_head_shoulder(gold_data)
+    assert "Head and Shoulder" in df_with_detection['head_shoulder_pattern'].values

tests/triple_bottom.csv

@@ -0,0 +1,41 @@
+date,Price
+1,100.00
+2,102.50
+3,105.00
+4,107.50
+5,110.00
+6,107.50
+7,105.00
+8,107.50
+9,110.00
+10,107.50
+11,105.00
+12,107.50
+13,110.00
+14,107.50
+15,105.00
+16,102.50
+17,100.00
+18,97.50
+19,95.00
+20,92.50
+21,90.00
+22,92.50
+23,95.00
+24,92.50
+25,90.00
+26,87.50
+27,85.00
+28,82.50
+29,80.00
+30,77.50
+31,75.00
+32,72.50
+33,70.00
+34,67.50
+35,65.00
+36,62.50
+37,60.00
+38,57.50
+39,55.00
+40,52.50

tests/triple_bottom_explained.csv

@@ -0,0 +1,40 @@
+1 | 100.00
+2 | 102.50
+3 | 105.00
+4 | 107.50
+5 | 110.00 (1st Top)
+6 | 107.50
+7 | 105.00
+8 | 107.50
+9 | 110.00 (2nd Top)
+10 | 107.50
+11 | 105.00
+12 | 107.50
+13 | 110.00 (3rd Top)
+14 | 107.50
+15 | 105.00
+16 | 102.50
+17 | 100.00
+18 | 97.50
+19 | 95.00
+20 | 92.50
+21 | 90.00 (Neckline)
+22 | 92.50
+23 | 95.00
+24 | 92.50
+25 | 90.00 (Break below neckline)
+26 | 87.50
+27 | 85.00
+28 | 82.50
+29 | 80.00
+30 | 77.50
+31 | 75.00
+32 | 72.50
+33 | 70.00
+34 | 67.50
+35 | 65.00
+36 | 62.50
+37 | 60.00
+38 | 57.50
+39 | 55.00
+40 | 52.50