stock-price-patterns

Stock Price Pattern Modeling(Detection)

Available patterns

• 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
• ...

How to use

Dataframe requirements

• Dataframe must contain Open, High, Low and Close prices
• Open, High, Low and Close prices must be in numeric type.

Dataframe Example:

| 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 |

Code

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 != ''])

Result

| 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 |

More details

check Examples.