How to Build Technical Indicators in Python

How to Build Technical Indicators in Python

15 mins read

Technical indicators are mathematical formulas that allow traders to predict where the market is going next. There are thousands of technical indicators, but the ones which have stood the test of time and have been used by traders throughout history include moving averages, MACD, Bollinger Bands, Money Flow Index , and more. Mcginley dynamic indicator python, In this guide, we will go over how to build your own technical indicators in Python and then use them to trade on the financial markets!

What are techncial Indicators?

Technical indicators are mathematical transformations of a security’s price and/or volume. They are used to try to predict future price changes. Techncial indicators are often used by traders to help make decisions about when to buy or sell a security.
There are many different types of technical indicators, each with its own strengths and weaknesses. Some common technical indicators include moving averages, Relative Strength Index (RSI), and Bollinger Bands.
Building a technical indicator is not difficult, but it does require some basic knowledge of programming and statistics. One way to create a new technicial indicator is to first understand what the goal of the technicial indicator should be.

For example, if you want an technicial indicator that measures momentum, then you might want your formula to take into account the rate at which prices are increasing or decreasing and the degree to which they have been doing so over a certain period of time.

The next step would be figuring out how these components can be measured mathematically. For example, for the rate at which prices are increasing or decreasing you could use an exponential smoothing algorithm like an Exponential Moving Average (EMA).

Why python for technical indicators

Python is a great language for building technical indicators because of its easy-to-use syntax and robust math library. Plus, there are many helpful libraries and frameworks available that make indicator development quicker and easier. In this post, we’ll walk through how to build three popular technical indicators step-by-step: moving averages, Bollinger Bands, andMACD.

How to Build Technical Indicators in Python


Python is a great language for building technical indicators because it is easy to read and write code in. Plus, there are many free libraries available that can be used to build indicators. These include modules such as pandas (an open source data analysis library), numpy (a library of mathematical functions), matplotlib (a plotting library), and SciPy (a mathematics library). The following steps will show you how to build three popular technical indicators in python: moving averages, Bollinger Bands, and MACD.

Run And Trend Lines

If you’re looking to get started in technical analysis, one of the first things you’ll need to learn is how to construct basic run and trend lines.

Run and trend lines are some of the most basic yet versatile tools in a technical analyst’s toolkit, and they can be used on any time frame from intra-day charts all the way up to long-term monthly charts. In this post, we’ll show you how to construct run and trend lines in Python. Let’s start by defining these two terms:

A run is a series of successive price bars that have risen or fallen together, while a trend represents the general direction of prices over a longer period of time.

To construct either type of line, you need to select three consecutive points at which to plot your line (known as candlesticks). Candlesticks typically represent open, high, low or close prices for stocks or currency pairs traded over a given timeframe.
A common mistake when drawing these lines is drawing them without understanding what they actually represent – generally speaking, if the candlesticks are forming an upward slope then it’s considered bullish while downward sloping candlesticks indicate bearish sentiment.

Moving Average

Image Credits: GeeksforGeeks.org

There are two types of moving averages, simple and exponential. A simple moving average is created by taking the sum of all prices over a certain period of time and then dividing that sum by the number of prices used.

An exponential moving average gives more weight to recent prices, making it more responsive to new information. A simple moving average takes the sum of all prices over a certain period of time and then divides that sum by the number of prices used.
An exponential moving average gives more weight to recent prices, making it more responsive to new information.

A Simple moving average takes the sum of all prices over a certain period of time and then divides that sum by the number of prices used. You can calculate both types using either long-term or short-term averages: The most common use for a moving average is for smoothing price data to filter out day-to-day fluctuations in order to identify trends with more clarity.

Bollinger Bands

Image Credits: Fidelity.com

Bollinger band is indeed a volatility or normal deviation based oscillator which has three components. The central band is a moving average line and the other two bands are specified, typically two, basis points apart from the moving average line.

Even as volatility of the stock prices fluctuates, the distance between the bands likewise changes. During more market volatility the difference grows and during low volatility situations, the gap closes.

In general, the goal for traders is to buy when the price falls below one band and sell when it rises above another.
It’s worth noting that there are different types of strategies for using Bollinger Bands: full-filling strategies (taking profits at first touch), overbought/oversold strategies (profit when asset goes back into bands) or reversal strategies (buy when asset leaves upper band).

Keltner Channel

The Keltner Channel is a technical indicator used to measure market volatility. It is comprised of three lines: an upper line, a lower line, and a midline. The upper and lower lines are typically set 2 standard deviations above and below the midline, respectively.

The Keltner Channel was initially created by Chester Keltner in the 1960s. The original technique employed simple moving averages (SMA) and the high-low price range to construct the bands. In the 1980s, a new formula was devised that employed average true range (ATR) (ATR). The ATR approach is extensively utilised nowadays.

The Keltner Channel is indeed a volatility-based technical indicator comprised of three different lines. The centre line is an exponential moving average (EMA) of a price. Additional lines are put above and below the EMA. The upper band is normally set two times the ATR above the EMA, while the lower band is statements indicating two times the ATR below the EMA. The bands expand and shrink as volatility (measured by ATR) grows and contracts.

Money Flow Index

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

The Money Flow Index (MFI) is a technical indicator that measures the strength of money flowing in and out of a security. It’s used to identify market tops and bottoms, as well as overbought and oversold conditions. MFI calculates the value by summing up all positive and negative trades for a given time period, then dividing it by the total volume.

The Money Flow Index (MFI) is indeed a momentum indicator that tracks the flow of money from and to an asset over a given period of time. It is connected to the Relative Strength Index (RSI) it involves volume, whereas the RSI simply analyses price.

Positive trades include long positions executed within the specified time frame while negative trades include short positions within the same time frame. The MFI is plotted on a scale from 0-100. In general, an MFI below 50 signals an oversold condition and an MFI above 50 indicates an overbought condition

Average True Range

Image Credits: LearnPythonwithrune.org

The Average True Range (ATR) is a technical indicator that measures market volatility. It was introduced by Welles Wilder in his book, New Concepts in Technical Trading Systems. What’s the ATR?
The average true range of a stock or index tells you how volatile it has been over the past 20 days.
It is defined as the highest high minus the lowest low over the last 20 trading sessions.

For example, if an index goes from 250 points to 260 points and then back down to 240 points before going up again to 260 points and so on, we can say that its average true range for this period would be 10 points (-260 + 260).
A higher ATR typically indicates more significant price movements than a lower ATR does. However, there are some limitations with the ATR calculation which are well documented in literature.

One limitation is that it only takes into account the previous 20 days worth of data whereas some traders may prefer other time periods such as 50 or 100 days worth of data. Another limitation is that it doesn’t take into account momentum; therefore traders should use other indicators when trying to identify trends such as support and resistance levels or candlestick patterns.

Force Index

Image Credits: blog.quantinsti.com

A technical indicator is a mathematical calculation that can be applied to a security’s price and volume to help predict future price movements.

The Force Index is one such indicator, and it can be used to day trade or swing trade. It was originally developed by J. Peter Steidlmayer in the 1970s as an oscillator of the rate of change of a stock’s price relative to its average true range (ATR).
The Force Index is calculated by subtracting the 14-day ATR from the current stock price, then dividing by two times the ATR multiplied by the square root of 252.
A higher reading indicates strong demand for a stock and thus high volatility, while lower readings indicate lower volatility or weak demand for stocks on average. As an oscillator, any significant shift should trigger some sort of action: either opening new positions when buying pressure has increased or closing existing positions when selling pressure has increased.

Ease of Movement

Richard Arms’ Ease of Movement (EOM or EMV) indicator is a technical tudy that aims to quantify a blend of momentum and volume information into one number.

The goal is to utilise this figure to detect whether prices are able to climb, or decrease, with minimal opposition in the directional movement.

Conceivably, if price levels move easily, they would then proceed to do so for an period of time that can be tried to trade effectively.
Ease of movement relates to the amount of price change happens every unit of trading activity. If less volume was able to move values further, there is more ease of movement.

Note: The output of the program shown above should be tried by yourself in order to get good.

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