Stock Price Pattern Modeling(Detection)
- Inverted Hammer
- Hammer
- Hanging man
- Bearish/Bullish Harami
- Dark cloud cover
- Doji
- Doji Star
- Bearish engulfing
- Bullish engulfing
- Morning star
- Morning star doji
- Piercing pattern
- Star
- Shooting star
- ...
- Dataframe must contain Open, High, Low and Close prices
- Open, High, Low and Close prices must be in numeric type.
| Date | Open | High | Low | Close |
|---|---|---|---|---|
| 2019-12-24 00:00:00 | 7317.3 | 7436.68 | 7157.04 | 7255.77 |
| 2019-12-25 00:00:00 | 7255.77 | 7271.77 | 7128.86 | 7204.63 |
| 2019-12-26 00:00:00 | 7205.01 | 7435 | 7157.12 | 7202 |
| 2019-12-27 00:00:00 | 7202 | 7275.86 | 7076.42 | 7254.74 |
| 2019-12-28 00:00:00 | 7254.77 | 7365.01 | 7238.67 | 7316.14 |
| 2019-12-29 00:00:00 | 7315.36 | 7528.45 | 7288 | 7388.24 |
import pandas as pd
import os
from pathlib import Path
from stock_price_patterns.candlestick import CandlestickPatterns
notebook_path = os.getcwd()
current_dir = Path(notebook_path)
csv_file = str(current_dir) + '/VN30F1M_5minutes.csv'
is_file = os.path.isfile(csv_file)
if is_file:
dataset = pd.read_csv(csv_file, index_col='Date', parse_dates=True)
else:
print(csv_file)
print("File not found")
exit()
csp = CandlestickPatterns(dataset)
csp._add('reversal')
modeling_data = csp.pattern_modeling()
print(modeling_data[modeling_data.model != ''])| Date | model | Open | High | Low | Close |
|---|---|---|---|---|---|
| 2019-12-24 00:00:00 | 7317.3 | 7436.68 | 7157.04 | 7255.77 | |
| 2019-12-25 00:00:00 | 7255.77 | 7271.77 | 7128.86 | 7204.63 | |
| 2019-12-26 00:00:00 | 7205.01 | 7435 | 7157.12 | 7202 | |
| 2019-12-27 00:00:00 | fair.. | 7202 | 7275.86 | 7076.42 | 7254.74 |
| 2019-12-28 00:00:00 | bull.. | 7254.77 | 7365.01 | 7238.67 | 7316.14 |
| 2019-12-29 00:00:00 | bear.. | 7315.36 | 7528.45 | 7288 | 7388.24 |
check Examples.