Best Tools to Create Candlestick Charts to Buy in December 2025

1

Day Trading Flash Cards - Stock Market Chart & Candlestick Patterns, Instructions to Trade Like a Pro!

• MASTER TRADING PATTERNS WITH 67 COMPREHENSIVE, HIGH-QUALITY FLASH CARDS.

• PERFECT FOR ALL SKILL LEVELS-BOOST CONFIDENCE AND DECISION-MAKING!

• DURABLE, PORTABLE DESIGN FOR ON-THE-GO LEARNING AND TRADING SUCCESS.

BUY & SAVE

$19.95

2

Candlestick Pattern Cheat Sheet for Trading – 3-Page Durable Cardstock with 190+ Chart Patterns – Includes Candlestick and Traditional Technical Analysis for Stock, Crypto, and Forex Traders

• ACCESS 190+ PATTERNS FOR FASTER, SMARTER TRADING DECISIONS.
• CLEAR VISUALS HELP SPOT TRENDS AND REVERSALS AT A GLANCE.
• DURABLE, PORTABLE DESIGN ENSURES LONG-LASTING MARKET INSIGHTS.

BUY & SAVE

$19.99

3

DON'T TRADE BEFORE LEARNING THESE 14 CANDLESTICK PATTERNS: These 14 most reliable candlestick patterns provide to traders more than 85% of trade opportunities emanating from candlesticks trading.

BUY & SAVE

$7.99

4

Candlesticks, Fibonacci, and Chart Pattern Trading Tools: A Synergistic Strategy to Enhance Profits and Reduce Risk (Wiley Trading)

BUY & SAVE

$61.27 $116.00

Save 47%

5

12Pcs Trading Chart Pattern Posters Candlestick Pattern Poster Bulletin Board Crypto and Stock Market Trading Poster Office Decorations for Trader Investor Supplies Wall Door Decor 11 x 15.7 Inches

• COMPLETE SET: 12 POSTERS + 100 DOTS FOR HASSLE-FREE SETUP.
• CLEAR, SLEEK DESIGN ENHANCES READABILITY IN ANY WORKSPACE.
• DURABLE & WATER-RESISTANT: PERFECT FOR LONG-TERM USE AND STUDY.

BUY & SAVE

$14.99

6

Candlestick Charting Explained: Timeless Techniques for Trading Stocks and Futures

BUY & SAVE

$31.40 $61.00

Save 49%

7

Technical Analysis of Candlestick and Chart Patterns: 13 Custom Technical Indicators, 5 Winning Trading Setups and a Customizable Trading Robot to automate your day and swing trading strategies

BUY & SAVE

$24.99

8

Limited Edition Trader’s Desk Mat - Stock Market Mouse Pad with Key Indicators - Large Size with Candlestick Chart Patterns - Gifts for Traders

• PROFESSIONAL-GRADE TOOL FOR SERIOUS TRADERS WITH ESSENTIAL MARKET INDICATORS.
• DURABLE 4MM CUSHIONED SURFACE ENSURES COMFORT DURING EXTENDED TRADING SESSIONS.
• PERFECT GIFT FOR TRADERS-IDEAL FOR BEGINNERS & SEASONED PROS ALIKE!

BUY & SAVE

$31.97 $39.97

Save 20%

9

The AutoStory Stock Market Trading Flashcards | 72 Trading Candlestick Flashcards | Financial Literacy Tool

• MASTER TRADING QUICKLY WITH FLASHCARDS FOR KEY CONCEPTS & STRATEGIES.

• PORTABLE DESIGN LETS YOU LEARN ANYTIME, ANYWHERE-PERFECT FOR BUSY LIVES.

• IDEAL GIFT FOR TRADERS, BLENDING EDUCATION AND ENTERTAINMENT SEAMLESSLY.

BUY & SAVE

$34.99

+

ONE MORE?

To create a candlestick chart using matplotlib, you first need to import the necessary libraries, such as matplotlib, matplotlib.finance, and matplotlib.dates. Next, you would read in your financial data and convert it into a format that is compatible with matplotlib. Then, you can use the candlestick_ohlc function to plot the candlestick chart based on your data. Make sure to customize your chart by adding labels, titles, and adjusting the colors and styles of the candlesticks as needed. Finally, show the chart using plt.show(). Experiment with different parameters and settings to create a visually appealing and informative candlestick chart.

How to add axis labels to a candlestick chart in matplotlib?

To add axis labels to a candlestick chart in matplotlib, you can use the following code:

import matplotlib.pyplot as plt from mpl_finance import candlestick_ohlc import pandas as pd import matplotlib.dates as mdates

Create a dataframe with your data

For example:

data = {'date': ['2021-01-01', '2021-01-02', '2021-01-03'], 'open': [100, 110, 105], 'high': [120, 115, 118], 'low': [90, 105, 100], 'close': [110, 112, 115]} df = pd.DataFrame(data)

Convert date to matplotlib dates

df['date'] = pd.to_datetime(df['date']) df['date'] = df['date'].map(mdates.date2num)

Create the candlestick chart

fig, ax = plt.subplots() candlestick_ohlc(ax, zip(df['date'], df['open'], df['high'], df['low'], df['close']), width=0.6, colorup='g', colordown='r')

Set the x-axis labels

ax.xaxis_date() ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))

Set the y-axis labels

plt.xlabel('Date') plt.ylabel('Price')

plt.show()

This code will create a candlestick chart using the data from the dataframe df and add axis labels for the x-axis and y-axis. You can customize the labels further by changing the plt.xlabel() and plt.ylabel() functions to suit your needs.

How to create a candlestick chart with moving averages in matplotlib?

To create a candlestick chart with moving averages in matplotlib, you can follow these steps:

1. Import the necessary libraries:

import matplotlib.pyplot as plt import pandas as pd import matplotlib.dates as mdates from mpl_finance import candlestick_ohlc

1. Read the data into a pandas dataframe:

data = pd.read_csv('data.csv') data['Date'] = pd.to_datetime(data['Date']) data = data.set_index('Date')

1. Calculate the moving averages:

data['MA_20'] = data['Close'].rolling(window=20).mean() data['MA_50'] = data['Close'].rolling(window=50).mean()

1. Create the candlestick chart with moving averages:

fig, ax = plt.subplots() ax.xaxis_date()

candlestick_ohlc(ax, zip(mdates.date2num(data.index.to_pydatetime()), data['Open'], data['High'], data['Low'], data['Close']), width=0.6, colorup='g', colordown='r')

plt.plot(data.index, data['MA_20'], label='MA 20', color='blue') plt.plot(data.index, data['MA_50'], label='MA 50', color='orange')

plt.legend() plt.show()

This code reads the data from a CSV file, calculates the moving averages, and then plots a candlestick chart with the closing prices, along with the 20-day and 50-day moving averages. You can customize the chart further by adjusting the window size for the moving averages, changing the colors, or adding additional indicators.

How to add a legend to a candlestick chart in matplotlib?

To add a legend to a candlestick chart in matplotlib, you can use the matplotlib.pyplot.legend() function. Here is an example code snippet to demonstrate how to add a legend to a candlestick chart:

import matplotlib.pyplot as plt from mplfinance.original_flavor import candlestick_ohlc import matplotlib.dates as mdates import pandas as pd

Sample data

data = { 'date': pd.date_range(start='1/1/2021', periods=10), 'open': [100, 110, 120, 130, 140, 150, 160, 170, 180, 190], 'high': [120, 130, 140, 150, 160, 170, 180, 190, 200, 210], 'low': [90, 100, 110, 120, 130, 140, 150, 160, 170, 180], 'close': [110, 120, 130, 140, 150, 160, 170, 180, 190, 200] }

df = pd.DataFrame(data)

Create a figure and an axes

fig, ax = plt.subplots()

Plot the candlestick chart

candlestick_ohlc(ax, zip(mdates.date2num(df['date']), df['open'], df['high'], df['low'], df['close']), width=0.6, colorup='g', colordown='r')

Add legend

plt.legend(['Open', 'High', 'Low', 'Close'], loc='upper left')

Display the plot

plt.show()

In this example, we first create a candlestick chart using the candlestick_ohlc() function from the mplfinance.original_flavor module. Then, we use the plt.legend() function to add a legend to the plot, specifying the labels for each data series. Finally, we display the plot using plt.show().

What is a shooting star candlestick pattern?

A shooting star candlestick pattern is a bearish reversal pattern that typically occurs at the end of an uptrend. It is characterized by a small body at the top of the candle with a long upper shadow, and little to no lower shadow. The long upper shadow indicates that the price opened high, but was unable to sustain its momentum and closed near the low of the session, suggesting a potential reversal in the trend. Traders often see the shooting star as a sign of weakness and a potential signal to sell or go short.

How to add annotations to a candlestick chart in matplotlib?

You can add annotations to a candlestick chart in matplotlib by using the annotate function. Here is an example code snippet to add annotations to a candlestick chart:

import matplotlib.pyplot as plt from mpl_finance import candlestick_ohlc import pandas as pd import numpy as np

Create a sample dataframe with OHLC data

data = {'Date': pd.date_range(start='1/1/2021', periods=10), 'Open': np.random.randint(100, 200, 10), 'High': np.random.randint(200, 300, 10), 'Low': np.random.randint(50, 100, 10), 'Close': np.random.randint(100, 200, 10)} df = pd.DataFrame(data) df['Date'] = df['Date'].apply(lambda x: x.toordinal())

Create a figure and axis

fig, ax = plt.subplots()

Plot the candlestick chart

candlestick_ohlc(ax, df.values, width=0.6, colorup='g', colordown='r')

Add annotations to the chart

for i in range(len(df)): ax.annotate(str(df['Close'][i]), (df['Date'][i], df['High'][i]), textcoords="offset points", xytext=(0,10), ha='center')

plt.show()

In this code snippet, we first create a sample dataframe with OHLC data. We then plot the candlestick chart using the candlestick_ohlc function. Next, we loop through each data point in the dataframe and add annotations to the chart using the annotate function. The annotate function takes the text to display, the position of the annotation, the position of the text relative to the position, and the horizontal alignment of the text.

You can adjust the position and formatting of the annotations as needed to customize the appearance of the annotations on your candlestick chart.