Innovative Financial Pattern Recognition in Python — Finding Quality Trading Signals.
Creating a Trading Pattern Recognition Strategy Using K’s Candlesticks.
Candlestick patterns deserve to be studied thoroughly and even though a strategy relying solely on them will be unstable and unprofitable, they can be a valuable addition into a full trading system that uses other techniques. In this article, we will see a full presentation and code of the Doji pattern on an alternative charting system.
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Standard Charting Systems
This section will deal with the import of OHLC historical data and charting candlesticks. One of the most famous trading platforms in the retail community is the MetaTrader5 software. It is a powerful tool that comes with its own programming language and its huge online community support. It also offers the possibility to export its historical short-term and long-term FX data.
The first thing we need to do is to simply download the platform from the official website. Then, after creating the demo account, we are ready to import the library in Python that allows to import the OHLC data from MetaTrader5.
A library is a group of structured functions that can be imported into our Python interpreter from where we can call and use the ones we want.
The easiest way to install the MetaTrader5 library is to go to the Python prompt on our computer and type:
pip install MetaTrader5
This should install the library in our local Python. Now, we want to import it to the Python interpreter (such as Pycharm or SPYDER) so that we can use it. Let us actually import all the libraries we will be using for this:
import datetime # Date acquiring import pytz # Time zone management import pandas as pd # Mostly for Data frame manipulation import MetaTrader5 as mt5 # Importing OHLC data import matplotlib.pyplot as plt # Plotting charts import numpy as np # Mostly for array manipulation
Anything that comes after “as” is a shortcut. The plt shortcut is there so that each time we want to call a function from that library we do not have to type the full matplotlib.pyplot statement. The official documentation for the Metatrader5 libary can be found here.
The first thing we can do is to select which time frame we want to import. Let us suppose that there are only two time frames, the 30-minute and the hourly bars. We can therefore create variables that hold the statement to tell the MetaTrader5 library which time frame we want.
# Choosing the 30-minute time frame frame_M30 = mt5.TIMEFRAME_M30 # Choosing the hourly time frame frame_H1 = mt5.TIMEFRAME_H1
Then, by staying in the spirit of importing variables, we can define the variable that states what date is it now. This helps the algorithm know the stopping date of the import. We can do this by the simple line of code below.
# Defining the variable now to give out the current date now = datetime.datetime.now()
Note that these code snippets are better used chronologically, hence, I encourage you to copy them in order and then execute them one by one so that you understand the evolution of what you are doing. The below is a function that holds which assets we want. Generally, I use 10 or more but for simplicity, let us consider that there are only two currency pairs: EURUSD and USDCHF.
def asset_list(asset_set): if asset_set == 'FX': assets = ['EURUSD', 'USDCHF'] return assets
Now, with the key function that gets us the OHLC data. The below establishes a connection to MetaTrader5, applies the current date, and extracts the needed data. Notice the arguments year, month, and day. These will be filled by us to select from when do we want the data to start. Note, I have inputed Europe/Paris as my time zone, you should use your time zone to get more accurate data.
def get_quotes(time_frame, year = 2005, month = 1, day = 1, asset = "EURUSD"): # Establish connection to MetaTrader 5 if not mt5.initialize(): print("initialize() failed, error code =", mt5.last_error()) quit() timezone = pytz.timezone("Europe/Paris") utc_from = datetime.datetime(year, month, day, tzinfo = timezone) utc_to = datetime.datetime(now.year, now.month, now.day + 1, tzinfo = timezone) rates = mt5.copy_rates_range(asset, time_frame, utc_from, utc_to) rates_frame = pd.DataFrame(rates) return rates_frame
And finally, the last function we will use is the one that uses the below get_quotes function and then cleans the results so that we have a nice array. We have selected data since January 2019 as shown below.
def mass_import(asset, horizon): if horizon == 'M30': data = get_quotes(frame_M30, 2019, 1, 1, asset = assets[asset]) data = data.iloc[:, 1:5].values data = data.round(decimals = 5) return data
Finally, we are done building the blocks necessary to import the data. To import EURUSD OHLC historical data, we simply use the below code line:
# Choosing the horizon horizon = 'M30' # Creating an array called EURUSD having M30 data since 2019 EURUSD = mass_import(0, horizon)
And voila, now we have the EURUSD OHLC data from 2019. Now let us see how to transform the data into candles.
Candlestick charts are among the most famous ways to analyze the time series visually. They contain more information than a simple line chart and have more visual interpretability than bar charts. Many libraries in Python offer charting functions but being someone who suffers from malfunctioning import of libraries and functions alongside their fogginess, I have created my own simple function that charts candlesticks manually with no exogenous help needed.
OHLC data is an abbreviation for Open, High, Low, and Close price. They are the four main ingredients for a timestamp. It is always better to have these four values together so that our analysis reflects more the reality. Here is a table that summarizes the OHLC data of hypothetical security:
Our job now is to plot the data so that we can visually interpret what kind of trend is the price following. We will start with the basic line plot before we move on to candlestick plotting.
Note that you can download the data manually or using Python. In case you have an excel file that has OHLC only data starting from the first row and column, you can import it using the below code snippet:
import numpy as np import pandas as pd # Importing the OHLC Historical Data in Excel format my_ohlc_data = pd.read_excel('my_ohlc_data.xlsx') # Converting to Array my_ohlc_data = np.array(my_ohlc_data)
Plotting basic line plots is extremely easy in Python and requires only one line of code. We have to make sure that we have imported a library called matplotlib and then we will call a function that plots the data for us.
# Importing the necessary charting library import matplotlib.pyplot as plt # The syntax to plot a line chart plt.plot(my_data[-100:, 3], color = 'black', label = 'EURUSD') # The syntax to add the label created above plt.legend() # The syntax to add a grid plt.grid()
Now that we have seen how to create normal line charts, it is time to take it to the next level with candlestick charts. The way to do this with no complications is to think about vertical lines. Here is the intuition (followed by an application of the function below):
Select a lookback period. This is the number of values you want to appear on the chart.
Plot vertical lines for each row representing the highs and lows. For example, on OHLC data, we will use a matplotlib function called vlines which plots a vertical line on the chart using a minimum (low) value and a maximum (high value).
Make a color condition which states that if the closing price is greater than the opening price, then execute the selected block of code (which naturally contains the color green). Do this with the color red (bearish candle) and the color black (Doji candle).
Plot vertical lines using the conditions with the min and max values representing closing prices and opening prices. Make sure to make the line’s width extra big so that the body of the candle appears sufficiently enough that the chart is deemed a candlestick chart.
def ohlc_plot(Data, window, name): Chosen = Data[-window:, ] for i in range(len(Chosen)): plt.vlines(x = i, ymin = Chosen[i, 2], ymax = Chosen[i, 1], color = 'black', linewidth = 1) if Chosen[i, 3] > Chosen[i, 0]: color_chosen = 'green' plt.vlines(x = i, ymin = Chosen[i, 0], ymax = Chosen[i, 3], color = color_chosen, linewidth = 4) if Chosen[i, 3] < Chosen[i, 0]: color_chosen = 'red' plt.vlines(x = i, ymin = Chosen[i, 3], ymax = Chosen[i, 0], color = color_chosen, linewidth = 4) if Chosen[i, 3] == Chosen[i, 0]: color_chosen = 'black' plt.vlines(x = i, ymin = Chosen[i, 3], ymax = Chosen[i, 0], color = color_chosen, linewidth = 4) plt.grid() plt.title(name) # Using the function ohlc_plot(my_ohlc_data, 50, '')
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