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About the developer

highfestiva
178 Stars 38 Forks MIT License 203 Commits 2 Opened issues

Description

Performant and effortless finance plotting for Python

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# 282,786
Shell
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196 commits

Finance Plot

Finance Plotter, or finplot, is a performant library with a clean api to help you with your backtesting. It's optionated with good defaults, so you can start doing your work without having to setup plots, colors, scales, autoscaling, keybindings, handle panning+vertical zooming (which all non-finance libraries have problems with). And best of all: it can show hundreds of thousands of datapoints without batting an eye.

Features

  • Great performance compared to mpl_finance, plotly and Bokeh
  • Clean api
  • Works with both stocks as well as cryptocurrencies on any time resolution
  • Show as many charts as you want on the same time axis, zoom on all of them at once
  • Auto-reload position where you were looking last run
  • Overlays, fill between, value bands, symbols, labels, legend, volume profile, heatmaps, etc.
  • Can show real-time updates, including orderbook. Save screenshot.
  • Comes with a dozen great examples.

feature1

feature2

feature3

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What it is not

finplot is not a web app. It does not help you create an homebrew exchange. It does not work with Jupyter Labs.

It is only intended for you to do backtesting in. That is not to say that you can't create a ticker or a trade widget yourself. The library is based on the eminent pyqtgraph, which is fast and flexible, so feel free to hack away if that's what you want.

Easy installation

$ pip install finplot

Example

It's straight-forward to start using. This shows every daily candle of Apple since the 80'ies:

import finplot as fplt
import yfinance

df = yfinance.download('AAPL') fplt.candlestick_ochl(df[['Open', 'Close', 'High', 'Low']]) fplt.show()

Example 2

sample

This 25-liner pulls some BitCoin data off of Bittrex and shows the above:

import finplot as fplt
import numpy as np
import pandas as pd
import requests

pull some data

symbol = 'USDT-BTC' url = 'https://bittrex.com/Api/v2.0/pub/market/GetTicks?marketName=%s&tickInterval=fiveMin' % symbol data = requests.get(url).json()

format it in pandas

df = pd.DataFrame(data['result']) df = df.rename(columns={'T':'time', 'O':'open', 'C':'close', 'H':'high', 'L':'low', 'V':'volume'}) df = df.astype({'time':'datetime64[ns]'})

create two axes

ax,ax2 = fplt.create_plot(symbol, rows=2)

plot candle sticks

candles = df[['time','open','close','high','low']] fplt.candlestick_ochl(candles, ax=ax)

overlay volume on the top plot

volumes = df[['time','open','close','volume']] fplt.volume_ocv(volumes, ax=ax.overlay())

put an MA on the close price

fplt.plot(df['time'], df['close'].rolling(25).mean(), ax=ax, legend='ma-25')

place some dumb markers on low wicks

lo_wicks = df[['open','close']].T.min() - df['low'] df.loc[(lo_wicks>lo_wicks.quantile(0.99)), 'marker'] = df['low'] fplt.plot(df['time'], df['marker'], ax=ax, color='#4a5', style='^', legend='dumb mark')

draw some random crap on our second plot

fplt.plot(df['time'], np.random.normal(size=len(df)), ax=ax2, color='#927', legend='stuff') fplt.set_y_range(-1.4, +3.7, ax=ax2) # hard-code y-axis range limitation

restore view (X-position and zoom) if we ever run this example again

fplt.autoviewrestore()

we're done

fplt.show()

Realtime updating with realistic indicator

Included in this repo are a 40-liner Bitfinex example and a slightly longer BitMEX websocket example, which both update in realtime with Bitcoin/Dollar pulled from the exchange. They also shows realistic and useful indicators (TD Sequential for BFX; and Bollinger Bands and orderbook for BitMEX). The S&P500 example shows you how to display MACD.

Snippets

Background color

# finplot uses no background (i.e. white) on even rows and a slightly different color on odd rows.
# Set your own before creating the plot.
fplt.background = '#ff0' # yellow
fplt.odd_plot_background = '#f0f' # purple
fplt.plot(df.Close)
fplt.show()

Unordered time series

finplot requires time-ordered time series - otherwise you'll get a crosshair and an X-axis showing the millisecond epoch instead of the actual time. See my comment here and issue 50 for more info.

It is also imperative that you either put your datetimes in your index, or in the first column. If your datetime is in the first column, you normally want to have a zero-based range index,

df.reset_index(drop=True)
, before plotting.

Restore the zoom at startup

# By default finplot shows all or a subset of your time series at startup. To store/restore zoom position:
fplt.autoviewrestore()
fplt.show() # will load zoom when showing, and save zoom when closing

Time zone

# Pandas normally reads datetimes in UTC time zone.
# finplot by default use the local time zone of your computer (for crosshair and X-axis)
from dateutil.tz import gettz
fplt.display_timezone = gettz('Asia/Jakarta')

... or in UTC = "display same as timezone-unaware data"

import datetime finplot.display_timezone = datetime.timezone.utc

Scatter plot with X-offset

To offset your scatter markers (say 0.2 time intervals to the left), see my comment here.

Align X-axes

See issue 27, and possibly (rarely a problem) issue 4.

Disable zoom/pan sync between axes

# finplot assumes all your axes are in the same time span. To decouple the zoom/pan link, use:
ax2.decouple()

Move viewport along X-axis (and autozoom)

Use

fplt.set_x_pos(xmin, xmax, ax)
. See example-animate.py.

Place Region of Interest (ROI) markers

For placing ellipses, see issue 57. For drawing lines, see example-line.py. (Interactively use Ctrl+drag for lines and Ctrl+mbutton-drag for ellipses.)

More than one Y-axis in same viewbox

fplt.candlestick_ochl(df2[['Open','Close','High','Low']], ax=ax.overlay(scale=1.0, yaxis='linear'))

The

scale
parameter means it goes all the way to the top of the axis (volume normally stays at the bottom). The
yaxis
parameter can be one of
False
(hidden which is default),
'linear'
or
'log'
. See issue 52 for more info.

Plot non-timeseries

finplot is made for plotting time series. To plot something different use

ax.disable_x_index()
. See second axis of example-overlay-correlate.py.

Custom crosshair and legend

S&P500 example shows how to set crosshair texts and update legend text+color as a result of mouse hover.

Custom axes ticks

To use your own labels on the X-axis see comment on issue 50. If you want to roll your own Y-axis, inherit

fplt.YAxisItem
.

Saving screenshot

See example-line.py. To keep screenshot in RAM see issue 28.

For creating multiple screenshots see issue 71.

Scaling plot heights

See issue 56. Changing the default window size can be achieved by setting

fplt.winw = 900; fplt.winh = 500;
before creating your plot.

Threading

See issue 55.

Titles on axes

See issue 41. To show grid and further adapt axes, etc:

ax.set_visible(crosshair=False, xaxis=False, yaxis=True, xgrid=True, ygrid=True)

Beep

fplt.play_sound('bot-happy.wav') # Ooh! Watch me - I just made a profit!

Keys

Esc
,
Home
,
End
,
g
,
Left arrow
,
Right arrow
.
Ctrl+drag
.

Missing snippets

Plot valign on mouse hover, update an orderbook, etc.

Coffee

For future support and features, consider a small donation.

BTC: bc1qk8m8yh86l2pz4eypflchr0tkn5aeud6cmt426m

ETH: 0x684d7d4C52ed428AE9a36B2407ba909D896cDB67

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