Say I have the following df called df_trading_pair which contains the following data from the BTC/USDT trading pair:
Start Date Open Price High Price Low Price Close Price End Date
0 2022-08-11 11:25:00 24512.25 24548.24 24507.78 24542.74 2022-08-11 11:29:59.999
1 2022-08-11 11:30:00 24543.54 24576.21 24524.36 24545.57 2022-08-11 11:34:59.999
2 2022-08-11 11:35:00 24545.08 24598.75 24541.00 24582.48 2022-08-11 11:39:59.999
3 2022-08-11 11:40:00 24583.40 24587.86 24552.52 24585.19 2022-08-11 11:44:59.999
4 2022-08-11 11:45:00 24586.40 24629.78 24578.02 24605.81 2022-08-11 11:49:59.999
5 2022-08-11 11:50:00 24605.81 24685.20 24597.07 24605.73 2022-08-11 11:54:59.999
6 2022-08-11 11:55:00 24605.73 24627.63 24533.40 24559.37 2022-08-11 11:59:59.999
7 2022-08-11 12:00:00 24559.37 24629.27 24546.00 24605.93 2022-08-11 12:04:59.999
8 2022-08-11 12:05:00 24606.71 24629.72 24592.89 24608.49 2022-08-11 12:09:59.999
9 2022-08-11 12:10:00 24608.49 24690.00 24582.14 24643.84 2022-08-11 12:14:59.999
10 2022-08-11 12:15:00 24644.79 24734.49 24602.37 24653.81 2022-08-11 12:19:59.999
11 2022-08-11 12:20:00 24653.81 24711.33 24635.80 24658.57 2022-08-11 12:24:59.999
12 2022-08-11 12:25:00 24658.57 24686.70 24593.81 24612.47 2022-08-11 12:29:59.999
13 2022-08-11 12:30:00 24613.83 24918.54 24531.05 24614.16 2022-08-11 12:34:59.999
14 2022-08-11 12:35:00 24617.40 24770.00 24567.41 24738.36 2022-08-11 12:39:59.999
15 2022-08-11 12:40:00 24737.14 24766.82 24640.00 24687.93 2022-08-11 12:44:59.999
16 2022-08-11 12:45:00 24690.17 24780.00 24687.21 24729.00 2022-08-11 12:49:59.999
17 2022-08-11 12:50:00 24730.52 24779.32 24698.54 24768.73 2022-08-11 12:54:59.999
When running df_trading_pair.dtypes, the following output is returned:
Start Date datetime64[ns]
Open Price float64
High Price float64
Low Price float64
Close Price float64
End Date datetime64[ns]
dtype: object
I'm interested in plotting the data above to make it look like this:
So, I decided to build the following code to do so:
import matplotlib.pyplot as plt
import numpy as np
def candlestick(t, o, h, l, c):
plt.figure(figsize=(16, 12), dpi=80)
color = ["green" if close_price >= open_price else "red" for close_price, open_price in zip(c, o)]
plt.bar(x=t, height=np.abs(o-c), bottom=np.min((o,c), axis=0), width=0.6, color=color)
plt.bar(x=t, height=h-l, bottom=l, width=0.1, color=color)
candlestick(df_trading_pair["Start Date"],
df_trading_pair["Open Price"],
df_trading_pair["High Price"],
df_trading_pair["Low Price"],
df_trading_pair["Close Price"])
plt.grid(alpha=0.2)
plt.show()
In theory, it should work as expected, but in reality it ended up being pretty disappointing:
I came here to politely request a feedback and alternative to make it work.
I think the sentence yaxis.set_label_position("right") could be added at the beginning of the candlestick function to set the y label to the right, but I don't know how could the x label be set to show only the time format in HH:MM:00 (i.e. 11:25:00) from the Start Date column.
May I get some assistance here?
CodePudding user response：
Is matplotlib the only library that you can use? If possible, take a look at the Plotly 
Regarding matplotlib, I have found 
CodePudding user response：
I decided to go for an alternative:
import mplfinance as mpf
import matplotlib.pyplot as plt
import pandas as pd
from binance.client import Client
client = Client(api_key= api_key, api_secret= secret_key, tld= "com")
def klines_to_df(df_trading_pair):
#drop unnecesary columns
df_trading_pair.drop(5, inplace = True, axis=1)
df_trading_pair.drop(7, inplace = True, axis=1)
df_trading_pair.drop(8, inplace = True, axis=1)
df_trading_pair.drop(9, inplace = True, axis=1)
df_trading_pair.drop(10, inplace = True, axis=1)
df_trading_pair.drop(11, inplace = True, axis=1)
# Rename the column names for best practices
df_trading_pair.rename(columns = { 0 : 'Start Date',
1 : 'Open Price',
2 : 'High Price',
3 : 'Low Price',
4 :'Close Price',
6 :'End Date',
}, inplace = True)
# Convert Unix Time values to actual dates
df_trading_pair['Start Date'] = pd.to_datetime(df_trading_pair['Start Date'], unit='ms')
df_trading_pair['End Date'] = pd.to_datetime(df_trading_pair['End Date'], unit='ms')
df_trading_pair = df_trading_pair.astype({'Open Price': 'float'})
df_trading_pair = df_trading_pair.astype({'High Price': 'float'})
df_trading_pair = df_trading_pair.astype({'Low Price': 'float'})
df_trading_pair = df_trading_pair.astype({'Close Price': 'float'})
return df_trading_pair
def set_DateTimeIndex(df_trading_pair):
df_trading_pair = df_trading_pair.set_index('Start Date', inplace=False)
# Rename the column names for best practices
df_trading_pair.rename(columns = { "Open Price" : 'Open',
"High Price" : 'High',
"Low Price" : 'Low',
"Close Price" :'Close',
}, inplace = True)
return df_trading_pair
#get historical klines
klines = client.get_historical_klines(symbol="BTCUSDT", interval="5m", start_str = "1660235100000", end_str="1660240200000")
# Customize the df_trading_pair that stored klines
df_trading_pair = klines_to_df(pd.DataFrame(klines))
df_trading_pair_date_time_index = set_DateTimeIndex(df_trading_pair)
# Plotting
# Create my own `marketcolors` style:
mc = mpf.make_marketcolors(up='#2fc71e',down='#ed2f1a',inherit=True)
# Create my own `MatPlotFinance` style:
s = mpf.make_mpf_style(base_mpl_style=['bmh','dark_background'],marketcolors=mc, y_on_right=True)
# Plot it
btc_plot, axlist = mpf.plot(df_trading_pair_date_time_index,
figratio=(10, 6),
type="candle",
style=s,
tight_layout=True,
datetime_format = '%H:%M',
ylabel = "Precio ($)",
returnfig=True)
# Add Title
axlist[0].set_title("BTC/USDT - 5m", fontsize=25, style='italic', fontfamily='fantasy' )
Output:
