K Line Chart
python实现k线图的代码，之前找过matplotlib中文文档但是画k线图的finance方法已经弃用了。所以自己在网上搜寻一下加上改编，很好的实现出k线图，
代码如下：__main__
# conding:utf-8 # 导入聚宽函数库 from jqdatasdk import * import pandas as pd import matplotlib.pyplot as plt from KLineChart.mpl_finance import plt_KLineChart import os ''' :param fields 字符串list, 默认是None(表示['open', 'close', 'high', 'low', 'volume', 'money']这几个标准字段), 支持以下属性 ['open', 'close', 'low', 'high', 'volume', 'money', 'factor', 'high_limit', 'low_limit', 'avg', 'pre_close', 'paused'] :param skip_paused 是否跳过不交易日期 ''' auth('your ID','your password') fields = ['open', 'close', 'high', 'low', 'volume', 'money'] stock_code = ['600519.XSHG','000001.XSHE','IC9999.CCFX'] data = get_price(stock_code,start_date='2018-1-1',end_date='2018-8-29',frequency='1d',fields=fields,skip_paused=False) k,m,n = data.shape # print(k,m,n) for i in range(n): Data = data.iloc[:,:,i] plt_KLineChart(Data,stock_code[i],step=20,fontSize=14) plt.show() # 数据保存 # os.mkdir('data/中国银行.xlsx') # data.to_excel(r'./data/中国银行.xlsx') # sql.to_excel() # print(data.shape)
这下我们需要导入自定义函数：from mpl_finance import plt_KLineChart
""" A collection of functions for analyzing and plotting financial data. User contributions welcome! """ from __future__ import (absolute_import, division, print_function, unicode_literals) import numpy as np import matplotlib.pyplot as plt from matplotlib import colors as mcolors from matplotlib.collections import LineCollection, PolyCollection from matplotlib.lines import TICKLEFT, TICKRIGHT, Line2D from matplotlib.patches import Rectangle from matplotlib.transforms import Affine2D from six.moves import xrange, zip def plot_day_summary_oclh(ax, quotes, ticksize=3, colorup='k', colordown='r'): """Plots day summary Represent the time, open, close, high, low as a vertical line ranging from low to high. The left tick is the open and the right tick is the close. Parameters ---------- ax : `Axes` an `Axes` instance to plot to quotes : sequence of (time, open, close, high, low, ...) sequences data to plot. time must be in float date format - see date2num ticksize : int open/close tick marker in points colorup : color the color of the lines where close >= open colordown : color the color of the lines where close < open Returns ------- lines : list list of tuples of the lines added (one tuple per quote) """ return _plot_day_summary(ax, quotes, ticksize=ticksize, colorup=colorup, colordown=colordown, ochl=True) def plot_day_summary_ohlc(ax, quotes, ticksize=3, colorup='k', colordown='r'): """Plots day summary Represent the time, open, high, low, close as a vertical line ranging from low to high. The left tick is the open and the right tick is the close. Parameters ---------- ax : `Axes` an `Axes` instance to plot to quotes : sequence of (time, open, high, low, close, ...) sequences data to plot. time must be in float date format - see date2num ticksize : int open/close tick marker in points colorup : color the color of the lines where close >= open colordown : color the color of the lines where close < open Returns ------- lines : list list of tuples of the lines added (one tuple per quote) """ return _plot_day_summary(ax, quotes, ticksize=ticksize, colorup=colorup, colordown=colordown, ochl=False) def _plot_day_summary(ax, quotes, ticksize=3, colorup='k', colordown='r', ochl=True): """Plots day summary Represent the time, open, high, low, close as a vertical line ranging from low to high. The left tick is the open and the right tick is the close. Parameters ---------- ax : `Axes` an `Axes` instance to plot to quotes : sequence of quote sequences data to plot. time must be in float date format - see date2num (time, open, high, low, close, ...) vs (time, open, close, high, low, ...) set by `ochl` ticksize : int open/close tick marker in points colorup : color the color of the lines where close >= open colordown : color the color of the lines where close < open ochl: bool argument to select between ochl and ohlc ordering of quotes Returns ------- lines : list list of tuples of the lines added (one tuple per quote) """ # unfortunately this has a different return type than plot_day_summary2_* lines = [] for q in quotes: if ochl: t, open, close, high, low = q[:5] else: t, open, high, low, close = q[:5] if close >= open: color = colorup else: color = colordown vline = Line2D(xdata=(t, t), ydata=(low, high), color=color, antialiased=False, # no need to antialias vert lines ) oline = Line2D(xdata=(t, t), ydata=(open, open), color=color, antialiased=False, marker=TICKLEFT, markersize=ticksize, ) cline = Line2D(xdata=(t, t), ydata=(close, close), color=color, antialiased=False, markersize=ticksize, marker=TICKRIGHT) lines.extend((vline, oline, cline)) ax.add_line(vline) ax.add_line(oline) ax.add_line(cline) ax.autoscale_view() return lines def candlestick_ochl(ax, quotes, width=0.2, colorup='k', colordown='r', alpha=1.0): """ Plot the time, open, close, high, low as a vertical line ranging from low to high. Use a rectangular bar to represent the open-close span. If close >= open, use colorup to color the bar, otherwise use colordown Parameters ---------- ax : `Axes` an Axes instance to plot to quotes : sequence of (time, open, close, high, low, ...) sequences As long as the first 5 elements are these values, the record can be as long as you want (e.g., it may store volume). time must be in float days format - see date2num width : float fraction of a day for the rectangle width colorup : color the color of the rectangle where close >= open colordown : color the color of the rectangle where close < open alpha : float the rectangle alpha level Returns ------- ret : tuple returns (lines, patches) where lines is a list of lines added and patches is a list of the rectangle patches added """ return _candlestick(ax, quotes, width=width, colorup=colorup, colordown=colordown, alpha=alpha, ochl=True) def plt_KLineChart(data,stockCode,step=20,fontSize=13,figsize=(12,6)): ''' :param data: type for DataFrame :param stockCode: stock code :param step: xticks step :param fontSize: font size :param figsize: window size :return: ''' prices = data[['open', 'high', 'low', 'close']] fig = plt.figure(figsize=figsize) ax = fig.add_axes([0.06, 0.15, 0.9, 0.75]) # margin_left margin_bottom width height candlestick_ohlc(ax, prices, width=0.5, colorup='r', colordown='b') dataIndex = [str(i).split(' ')[0] for i in data.iloc[::step, :].index] location = list(range(0,len(data.iloc[:,0]),step)) plt.xticks(location,dataIndex,rotation=45) font_size = {'size': fontSize} plt.ylabel('prices',font_size) plt.title('stock：%s'%(stockCode),font_size) plt.rcParams['font.sans-serif'] = ['SimHei'] # 设置字体为SimHei显示中文 plt.rcParams['axes.unicode_minus'] = False # 设置正常显示符号 def candlestick_ohlc(ax, quotes, width=0.2, colorup='k', colordown='r', alpha=1.0): """ Plot the time, open, high, low, close as a vertical line ranging from low to high. Use a rectangular bar to represent the open-close span. If close >= open, use colorup to color the bar, otherwise use colordown Parameters ---------- ax : `Axes` an Axes instance to plot to quotes : sequence of (time, open, high, low, close, ...) sequences As long as the first 5 elements are these values, the record can be as long as you want (e.g., it may store volume). time must be in float days format - see date2num width : float fraction of a day for the rectangle width colorup : color the color of the rectangle where close >= open colordown : color the color of the rectangle where close < open alpha : float the rectangle alpha level Returns ------- ret : tuple returns (lines, patches) where lines is a list of lines added and patches is a list of the rectangle patches added """ return _candlestick(ax, quotes, width=width, colorup=colorup, colordown=colordown, alpha=alpha, ochl=False) def _candlestick(ax, quotes, width=0.2, colorup='k', colordown='r', alpha=1.0, ochl=True): """ Plot the time, open, high, low, close as a vertical line ranging from low to high. Use a rectangular bar to represent the open-close span. If close >= open, use colorup to color the bar, otherwise use colordown Parameters ---------- ax : `Axes` an Axes instance to plot to quotes : sequence of quote sequences data to plot. time must be in float date format - see date2num (time, open, high, low, close, ...) vs (time, open, close, high, low, ...) set by `ochl` width : float fraction of a day for the rectangle width colorup : color the color of the rectangle where close >= open colordown : color the color of the rectangle where close < open alpha : float the rectangle alpha level ochl: bool argument to select between ochl and ohlc ordering of quotes Returns ------- ret : tuple returns (lines, patches) where lines is a list of lines added and patches is a list of the rectangle patches added """ OFFSET = width / 2.0 lines = [] patches = [] quotes = np.column_stack([list(range(len(quotes))), quotes]) for q in quotes: if ochl: t, open, close, high, low = q[:5] else: t, open, high, low, close = q[:5] if close >= open: color = colorup lower = open height = close - open else: color = colordown lower = close height = open - close vline = Line2D( xdata=(t, t), ydata=(low, high), color=color, linewidth=0.5, antialiased=True, ) rect = Rectangle( xy=(t - OFFSET, lower), width=width, height=height, facecolor=color, edgecolor=color, ) rect.set_alpha(alpha) lines.append(vline) patches.append(rect) ax.add_line(vline) ax.add_patch(rect) ax.autoscale_view() return lines, patches def _check_input(opens, closes, highs, lows, miss=-1): """Checks that *opens*, *highs*, *lows* and *closes* have the same length. NOTE: this code assumes if any value open, high, low, close is missing (*-1*) they all are missing Parameters ---------- ax : `Axes` an Axes instance to plot to opens : sequence sequence of opening values highs : sequence sequence of high values lows : sequence sequence of low values closes : sequence sequence of closing values miss : int identifier of the missing data Raises ------ ValueError if the input sequences don't have the same length """ def _missing(sequence, miss=-1): """Returns the index in *sequence* of the missing data, identified by *miss* Parameters ---------- sequence : sequence to evaluate miss : identifier of the missing data Returns ------- where_miss: numpy.ndarray indices of the missing data """ return np.where(np.array(sequence) == miss)[0] same_length = len(opens) == len(highs) == len(lows) == len(closes) _missopens = _missing(opens) same_missing = ((_missopens == _missing(highs)).all() and (_missopens == _missing(lows)).all() and (_missopens == _missing(closes)).all()) if not (same_length and same_missing): msg = ("*opens*, *highs*, *lows* and *closes* must have the same" " length. NOTE: this code assumes if any value open, high," " low, close is missing (*-1*) they all must be missing.") raise ValueError(msg) def plot_day_summary2_ochl(ax, opens, closes, highs, lows, ticksize=4, colorup='k', colordown='r'): """Represent the time, open, close, high, low, as a vertical line ranging from low to high. The left tick is the open and the right tick is the close. Parameters ---------- ax : `Axes` an Axes instance to plot to opens : sequence sequence of opening values closes : sequence sequence of closing values highs : sequence sequence of high values lows : sequence sequence of low values ticksize : int size of open and close ticks in points colorup : color the color of the lines where close >= open colordown : color the color of the lines where close < open Returns ------- ret : list a list of lines added to the axes """ return plot_day_summary2_ohlc(ax, opens, highs, lows, closes, ticksize, colorup, colordown) def plot_day_summary2_ohlc(ax, opens, highs, lows, closes, ticksize=4, colorup='k', colordown='r'): """Represent the time, open, high, low, close as a vertical line ranging from low to high. The left tick is the open and the right tick is the close. *opens*, *highs*, *lows* and *closes* must have the same length. NOTE: this code assumes if any value open, high, low, close is missing (*-1*) they all are missing Parameters ---------- ax : `Axes` an Axes instance to plot to opens : sequence sequence of opening values highs : sequence sequence of high values lows : sequence sequence of low values closes : sequence sequence of closing values ticksize : int size of open and close ticks in points colorup : color the color of the lines where close >= open colordown : color the color of the lines where close < open Returns ------- ret : list a list of lines added to the axes """ _check_input(opens, highs, lows, closes) rangeSegments = [((i, low), (i, high)) for i, low, high in zip(xrange(len(lows)), lows, highs) if low != -1] # the ticks will be from ticksize to 0 in points at the origin and # we'll translate these to the i, close location openSegments = [((-ticksize, 0), (0, 0))] # the ticks will be from 0 to ticksize in points at the origin and # we'll translate these to the i, close location closeSegments = [((0, 0), (ticksize, 0))] offsetsOpen = [(i, open) for i, open in zip(xrange(len(opens)), opens) if open != -1] offsetsClose = [(i, close) for i, close in zip(xrange(len(closes)), closes) if close != -1] scale = ax.figure.dpi * (1.0 / 72.0) tickTransform = Affine2D().scale(scale, 0.0) colorup = mcolors.to_rgba(colorup) colordown = mcolors.to_rgba(colordown) colord = {True: colorup, False: colordown} colors = [colord[open < close] for open, close in zip(opens, closes) if open != -1 and close != -1] useAA = 0, # use tuple here lw = 1, # and here rangeCollection = LineCollection(rangeSegments, colors=colors, linewidths=lw, antialiaseds=useAA, ) openCollection = LineCollection(openSegments, colors=colors, antialiaseds=useAA, linewidths=lw, offsets=offsetsOpen, transOffset=ax.transData, ) openCollection.set_transform(tickTransform) closeCollection = LineCollection(closeSegments, colors=colors, antialiaseds=useAA, linewidths=lw, offsets=offsetsClose, transOffset=ax.transData, ) closeCollection.set_transform(tickTransform) minpy, maxx = (0, len(rangeSegments)) miny = min([low for low in lows if low != -1]) maxy = max([high for high in highs if high != -1]) corners = (minpy, miny), (maxx, maxy) ax.update_datalim(corners) ax.autoscale_view() # add these last ax.add_collection(rangeCollection) ax.add_collection(openCollection) ax.add_collection(closeCollection) return rangeCollection, openCollection, closeCollection def candlestick2_ochl(ax, opens, closes, highs, lows, width=4, colorup='k', colordown='r', alpha=0.75): """Represent the open, close as a bar line and high low range as a vertical line. Preserves the original argument order. Parameters ---------- ax : `Axes` an Axes instance to plot to opens : sequence sequence of opening values closes : sequence sequence of closing values highs : sequence sequence of high values lows : sequence sequence of low values width : int size of open and close ticks in points colorup : color the color of the lines where close >= open colordown : color the color of the lines where close < open alpha : float bar transparency Returns ------- ret : tuple (lineCollection, barCollection) """ return candlestick2_ohlc(ax, opens, highs, lows, closes, width=width, colorup=colorup, colordown=colordown, alpha=alpha) def candlestick2_ohlc(ax, opens, highs, lows, closes, width=4, colorup='k', colordown='r', alpha=0.75): """Represent the open, close as a bar line and high low range as a vertical line. NOTE: this code assumes if any value open, low, high, close is missing they all are missing Parameters ---------- ax : `Axes` an Axes instance to plot to opens : sequence sequence of opening values highs : sequence sequence of high values lows : sequence sequence of low values closes : sequence sequence of closing values width : int size of open and close ticks in points colorup : color the color of the lines where close >= open colordown : color the color of the lines where close < open alpha : float bar transparency Returns ------- ret : tuple (lineCollection, barCollection) """ _check_input(opens, highs, lows, closes) delta = width / 2. barVerts = [((i - delta, open), (i - delta, close), (i + delta, close), (i + delta, open)) for i, open, close in zip(xrange(len(opens)), opens, closes) if open != -1 and close != -1] rangeSegments = [((i, low), (i, high)) for i, low, high in zip(xrange(len(lows)), lows, highs) if low != -1] colorup = mcolors.to_rgba(colorup, alpha) colordown = mcolors.to_rgba(colordown, alpha) colord = {True: colorup, False: colordown} colors = [colord[open < close] for open, close in zip(opens, closes) if open != -1 and close != -1] useAA = 0, # use tuple here lw = 0.5, # and here rangeCollection = LineCollection(rangeSegments, colors=colors, linewidths=lw, antialiaseds=useAA, ) barCollection = PolyCollection(barVerts, facecolors=colors, edgecolors=colors, antialiaseds=useAA, linewidths=lw, ) minx, maxx = 0, len(rangeSegments) miny = min([low for low in lows if low != -1]) maxy = max([high for high in highs if high != -1]) corners = (minx, miny), (maxx, maxy) ax.update_datalim(corners) ax.autoscale_view() # add these last ax.add_collection(rangeCollection) ax.add_collection(barCollection) return rangeCollection, barCollection def volume_overlay(ax, opens, closes, volumes, colorup='k', colordown='r', width=4, alpha=1.0): """Add a volume overlay to the current axes. The opens and closes are used to determine the color of the bar. -1 is missing. If a value is missing on one it must be missing on all Parameters ---------- ax : `Axes` an Axes instance to plot to opens : sequence a sequence of opens closes : sequence a sequence of closes volumes : sequence a sequence of volumes width : int the bar width in points colorup : color the color of the lines where close >= open colordown : color the color of the lines where close < open alpha : float bar transparency Returns ------- ret : `barCollection` The `barrCollection` added to the axes """ colorup = mcolors.to_rgba(colorup, alpha) colordown = mcolors.to_rgba(colordown, alpha) colord = {True: colorup, False: colordown} colors = [colord[open < close] for open, close in zip(opens, closes) if open != -1 and close != -1] delta = width / 2. bars = [((i - delta, 0), (i - delta, v), (i + delta, v), (i + delta, 0)) for i, v in enumerate(volumes) if v != -1] barCollection = PolyCollection(bars, facecolors=colors, edgecolors=((0, 0, 0, 1),), antialiaseds=(0,), linewidths=(0.5,), ) ax.add_collection(barCollection) corners = (0, 0), (len(bars), max(volumes)) ax.update_datalim(corners) ax.autoscale_view() # add these last return barCollection def volume_overlay2(ax, closes, volumes, colorup='k', colordown='r', width=4, alpha=1.0): """ Add a volume overlay to the current axes. The closes are used to determine the color of the bar. -1 is missing. If a value is missing on one it must be missing on all nb: first point is not displayed - it is used only for choosing the right color Parameters ---------- ax : `Axes` an Axes instance to plot to closes : sequence a sequence of closes volumes : sequence a sequence of volumes width : int the bar width in points colorup : color the color of the lines where close >= open colordown : color the color of the lines where close < open alpha : float bar transparency Returns ------- ret : `barCollection` The `barrCollection` added to the axes """ return volume_overlay(ax, closes[:-1], closes[1:], volumes[1:], colorup, colordown, width, alpha) def volume_overlay3(ax, quotes, colorup='k', colordown='r', width=4, alpha=1.0): """Add a volume overlay to the current axes. quotes is a list of (d, open, high, low, close, volume) and close-open is used to determine the color of the bar Parameters ---------- ax : `Axes` an Axes instance to plot to quotes : sequence of (time, open, high, low, close, ...) sequences data to plot. time must be in float date format - see date2num width : int the bar width in points colorup : color the color of the lines where close1 >= close0 colordown : color the color of the lines where close1 < close0 alpha : float bar transparency Returns ------- ret : `barCollection` The `barrCollection` added to the axes """ colorup = mcolors.to_rgba(colorup, alpha) colordown = mcolors.to_rgba(colordown, alpha) colord = {True: colorup, False: colordown} dates, opens, highs, lows, closes, volumes = list(zip(*quotes)) colors = [colord[close1 >= close0] for close0, close1 in zip(closes[:-1], closes[1:]) if close0 != -1 and close1 != -1] colors.insert(0, colord[closes[0] >= opens[0]]) right = width / 2.0 left = -width / 2.0 bars = [((left, 0), (left, volume), (right, volume), (right, 0)) for d, open, high, low, close, volume in quotes] sx = ax.figure.dpi * (1.0 / 72.0) # scale for points sy = ax.bbox.height / ax.viewLim.height barTransform = Affine2D().scale(sx, sy) dates = [d for d, open, high, low, close, volume in quotes] offsetsBars = [(d, 0) for d in dates] useAA = 0, # use tuple here lw = 0.5, # and here barCollection = PolyCollection(bars, facecolors=colors, edgecolors=((0, 0, 0, 1),), antialiaseds=useAA, linewidths=lw, offsets=offsetsBars, transOffset=ax.transData, ) barCollection.set_transform(barTransform) minpy, maxx = (min(dates), max(dates)) miny = 0 maxy = max([volume for d, open, high, low, close, volume in quotes]) corners = (minpy, miny), (maxx, maxy) ax.update_datalim(corners) # print 'datalim', ax.dataLim.bounds # print 'viewlim', ax.viewLim.bounds ax.add_collection(barCollection) ax.autoscale_view() return barCollection def index_bar(ax, vals, facecolor='b', edgecolor='l', width=4, alpha=1.0, ): """Add a bar collection graph with height vals (-1 is missing). Parameters ---------- ax : `Axes` an Axes instance to plot to vals : sequence a sequence of values facecolor : color the color of the bar face edgecolor : color the color of the bar edges width : int the bar width in points alpha : float bar transparency Returns ------- ret : `barCollection` The `barrCollection` added to the axes """ facecolors = (mcolors.to_rgba(facecolor, alpha),) edgecolors = (mcolors.to_rgba(edgecolor, alpha),) right = width / 2.0 left = -width / 2.0 bars = [((left, 0), (left, v), (right, v), (right, 0)) for v in vals if v != -1] sx = ax.figure.dpi * (1.0 / 72.0) # scale for points sy = ax.bbox.height / ax.viewLim.height barTransform = Affine2D().scale(sx, sy) offsetsBars = [(i, 0) for i, v in enumerate(vals) if v != -1] barCollection = PolyCollection(bars, facecolors=facecolors, edgecolors=edgecolors, antialiaseds=(0,), linewidths=(0.5,), offsets=offsetsBars, transOffset=ax.transData, ) barCollection.set_transform(barTransform) minpy, maxx = (0, len(offsetsBars)) miny = 0 maxy = max([v for v in vals if v != -1]) corners = (minpy, miny), (maxx, maxy) ax.update_datalim(corners) ax.autoscale_view() # add these last ax.add_collection(barCollection) return barCollection