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Caner Irfanoglu
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Feb 12, 2020 · 5 min read ·
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Recognizing over 50 Candlestick Patterns with
Python
An easy to follow guide for leveraging candlestick patterns for ML
Live implementation can be found at www.cryptodashapp.com
Image Credit. Photo by Austin Distel on Unsplash
W
hen making trading decisions, we can utilize several different information
sources on our technical analysis. One of these sources is OHLC (open, high,
low, close) data. Candlestick charts can be plotted to extract patterns from OHLC data
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for any tradable instrument.
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A candlestick pattern is a movement in prices shown graphically on a candlestick chart
that some believe can predict a particular market movement.¹
Image Credit
The full list of simple and complex candlestick patterns with visual examples can be
found in this Wikipedia article.
Candlestick patterns are great candidates to train Machine Learning models for
attempting to predict future prices. In this article, we will go over the feature
engineering steps of creating a predictor using candlestick patterns and then
visualize our results. We will use python, TA-Lib module and the performance
rankings from the www.thepatternsite.com
Three steps of the process are:
1. Extract the patterns using TA-Lib
2. Rank the patterns using “Overall performance rank” from the patternsite
3. Pick the best performance candle
Extracting the patterns using TA-Lib
With TA-Lib, extracting patterns is super simple. We can start by installing the module
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from https://github.com/mrjbq7/ta-lib. The repository contains easy to follow
instructions for the installation process.
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After the installation, we start by importing the module:
import talib
Then, we get a list of available patterns by running:
candle_names = talib.get_function_groups()['Pattern Recognition']
“candle_names” list should look like as follows:
candle_names = [
'CDL2CROWS',
'CDL3BLACKCROWS',
'CDL3INSIDE',
'CDL3LINESTRIKE',
.
.
.
]
We are ready to extract candles! We just need a sample dataset with open, high, low,
close values.
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import requests
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import pandas as pd
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# link for Bitcoin Data
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link = "https://min-api.cryptocompare.com/data/histoday?fsym=BTC&tsym=USD&limit=365&aggregate=1"
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# API request historical
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historical_get = requests.get(link)
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# access the content of historical api request
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historical_json = historical_get.json()
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# extract json data as dictionary
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historical_dict = historical_json['Data']
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# extract Final historical df
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df = pd.DataFrame(historical_dict,
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columns=['close', 'high', 'low', 'open', 'time', 'volumefrom',
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dtype='float64')
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# time column is converted to "YYYY-mm-dd hh:mm:ss" ("%Y-%m-%d %H:%M:%S")
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posix_time = pd.to_datetime(df['time'], unit='s')
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# append posix_time
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df.insert(0, "Date", posix_time)
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# drop unix time stamp
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df.drop("time", axis = 1, inplace = True)
data_prep.py hosted with ❤ by GitHub
view raw
Sample Bitcoin dataset preparation
Voila! Bitcoin dataset is ready. Let’s extract the OHLC data and create pattern columns.
# extract OHLC
op = df['open']
hi = df['high']
lo = df['low']
cl = df['close']
# create columns for each pattern
for candle in candle_names:
# below is same as;
# df["CDL3LINESTRIKE"] = talib.CDL3LINESTRIKE(op, hi, lo, cl)
df[candle] = getattr(talib, candle)(op, hi, lo, cl)
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TA-Lib creates individual columns for each pattern. While 0 corresponds to no pattern,
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positive values represent bullish patterns and negative values represent bearish patterns.
Candlestick Patterns found on Bitcoin Data
Congratulations! We just obtained our first dataset with algorithmically extracted
patterns.
Ranking the patterns
We successfully extracted candlestick patterns using TA-Lib. With few lines of code, we
can condense this sparse information into a single column with pattern labels. But first,
we need to handle the cases where multiple patterns are found for a given candle. To do
that, we need a performance metric to compare patterns. We will use the “Overall
performance rank” from the patternsite.
candle_rankings = {
"CDL3LINESTRIKE_Bull": 1,
"CDL3LINESTRIKE_Bear": 2,
"CDL3BLACKCROWS_Bull": 3,
"CDL3BLACKCROWS_Bear": 3,
"CDLEVENINGSTAR_Bull": 4,
"CDLEVENINGSTAR_Bear": 4,
"CDLTASUKIGAP_Bull": 5,
"CDLTASUKIGAP_Bear": 5,
.
.
.
.
}
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After some manual scraping, the patterns are combined in “candle_rankings” dictionary.
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When there exist multiple patterns, we will use the values in the above dictionary to
decide best performance pattern. Full dictionary of the patterns and the explanations of
the naming and ranking decisions can be found here.
Picking the best performance candle
Here comes the fun part. We will code the logic for creating the labels. We basically have
3 cases.
• No Pattern: Fill the cell with “NO_PATTERN”
• Single Pattern: Fill the cell with Pattern Name
• Multiple Patterns: Fill the cell with lowest (best) ranking Pattern Name
Below is the code for creating the pattern labels and found pattern counts.
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df['candlestick_pattern'] = np.nan
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df['candlestick_match_count'] = np.nan
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for index, row in df.iterrows():
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# no pattern found
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if len(row[candle_names]) - sum(row[candle_names] == 0) == 0:
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df.loc[index,'candlestick_pattern'] = "NO_PATTERN"
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df.loc[index, 'candlestick_match_count'] = 0
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# single pattern found
elif len(row[candle_names]) - sum(row[candle_names] == 0) == 1:
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# bull pattern 100 or 200
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if any(row[candle_names].values > 0):
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pattern = list(compress(row[candle_names].keys(), row[candle_names].values !=
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df.loc[index, 'candlestick_pattern'] = pattern
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df.loc[index, 'candlestick_match_count'] = 1
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# bear pattern -100 or -200
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else:
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pattern = list(compress(row[candle_names].keys(), row[candle_names].values !=
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df.loc[index, 'candlestick_pattern'] = pattern
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df.loc[index, 'candlestick_match_count'] = 1
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# multiple patterns matched -- select best performance
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else:
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# filter out pattern names from bool list of values
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patterns = list(compress(row[candle_names].keys(), row[candle_names].values != 0
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container = []
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for pattern in patterns:
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if row[pattern] > 0:
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container.append(pattern + '_Bull')
else:
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container.append(pattern + '_Bear')
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rank_list = [candle_rankings[p] for p in container]
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if len(rank_list) == len(container):
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rank_index_best = rank_list.index(min(rank_list))
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df.loc[index, 'candlestick_pattern'] = container[rank_index_best]
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df.loc[index, 'candlestick_match_count'] = len(container)
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# clean up candle columns
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df.drop(candle_names, axis = 1, inplace = True)
Logic for picking best pattern for each candle
Visualizing and validating the results
So far, we extracted many candlestick patterns using TA-Lib (supports 61 patterns as of
Feb 2020). We ranked them based on the “Overall performance rank” and selected the
best performance pattern for each candle. Next, we can validate our results by plotting
the candles and visually check against the patterns found. Below is a sample script for
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visualizing the data using Plotly. The dataset and the plot can be compared side by side
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and the patterns can be validated easily by matching the indexes.
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from plotly.offline import plot
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import plotly.graph_objs as go
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import pandas as pd
Get started
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df = pd.read_csv('./btc_data.csv')
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o = df['open'].astype(float)
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h = df['high'].astype(float)
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l = df['low'].astype(float)
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c = df['close'].astype(float)
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trace = go.Candlestick(
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open=o,
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high=h,
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low=l,
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close=c)
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data = [trace]
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plot(data, filename='go_candle1.html')
Code plotly for visualizing candlesticks
Candlesticks chart for bitcoin data using plotly
You may find this Tableau Viz more convenient to inspect the patterns with the
annotations quickly.
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When the patterns found on our dataset are compared to the actual patterns, the results
email We can test on larger datasets as part of the future work. Also, since
lookYour
consistent.
some patterns only have a single version, ‘Bull’ and ‘Bear’ tags can be removed from
them.
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about our privacy practices.
All scripts and contents of this post including the recognize_candlestick function, can be
found at https://github.com/CanerIrfanoglu/medium.
You can also see candlestick recognition in action as part of the crypto currency
technical analysis dashboard “Crypto Dash” at https://youtu.be/HS3gAmtET9k?t=121.
Content here is mainly based on the work of the creators of the TA-Lib module and
Thomas Bulkowsi’s long time studies on candlestick patterns. I would like to thank them
for making their work publicly available.
If you enjoyed or found my work valuable, please make sure to stay synced and feel free
to connect on linkedin. I would be delighted to hear your comments and suggestions.
Cheers!
References
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[1]Candlestick pattern. (n.d.). In Wikipedia. Retrieved February 11, 2020 from
https://en.wikipedia.org/wiki/Candlestick_pattern
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