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????????????????Matplotlib???????????????????????????????????????????????

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?????matplotlib?????????????????????????????????

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import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
def date2num(date):
    converter = mdates.strpdate2num('%Y-%m-%d')
    return converter(date)
class StockTradingGraph:
    """A stock trading visualization using matplotlib made to render
    OpenAI gym environments"""
    
    def __init__(self, df, title=None):
        self.df = df
        self.net_worths = np.zeros(len(df['Date']))
        fig = plt.figure()
        fig.suptitle(title)
        self.net_worth_ax = plt.subplot2grid((6, 1), (0, 0), rowspan=2, colspan=1)
        self.price_ax = plt.subplot2grid((6, 1), (2, 0), rowspan=8, colspan=1, sharex=self.net_worth_ax)
        self.volume_ax = self.price_ax.twinx()
        plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0)
        plt.show(block=False)
def render(self, current_step, net_worth, trades, window_size=40):
    self.net_worths[current_step] = net_worth
    window_start = max(current_step - window_size, 0)
    step_range = range(window_start, current_step + 1)
    dates = np.array([date2num(x) for x in self.df['Date'].values[step_range]])
    self._render_net_worth(current_step, net_worth, step_range, dates)
    self._render_price(current_step, net_worth, dates, step_range)
    self._render_volume(current_step, net_worth, dates, step_range)
    self._render_trades(current_step, trades, step_range)
    self.price_ax.set_xticklabels(self.df['Date'].values[step_range], rotation=45, horizontalalignment='right')
    plt.setp(self.net_worth_ax.get_xticklabels(), visible=False)
    plt.pause(0.001)
def _render_net_worth(self, current_step, net_worth, step_range, dates):
    self.net_worth_ax.clear()
    self.net_worth_ax.plot_date(dates, self.net_worths[step_range], '-')
    self.net_worth_ax.legend()
    legend = self.net_worth_ax.legend(loc=2, ncol=2, prop={'size': 8})
    legend.get_frame().set_alpha(0.4)
    last_date = date2num(self.df['Date'].values[current_step])
    last_net_worth = self.net_worths[current_step]
    self.net_worth_ax.annotate('{0:.2f}'.format(net_worth), 
                            (last_date, last_net_worth),
                            xytext=(last_date, last_net_worth),
                            bbox=dict(boxstyle='round', fc='w', ec='k', lw=1),
                            color="black", 
                            fontsize="small")
    self.net_worth_ax.set_ylim(
        min(self.net_worths[np.nonzero(self.net_worths)]) / 1.25,
        max(self.net_worths) * 1.25)
def _render_price(self, current_step, net_worth, dates, step_range):
    self.price_ax.clear()
    candlesticks = zip(dates,
                        self.df['Open'].values[step_range],
                        self.df['Close'].values[step_range],
                        self.df['High'].values[step_range],
                        self.df['Low'].values[step_range])
    candlestick(self.price_ax, candlesticks, width=1, colorup=UP_COLOR, colordown=DOWN_COLOR)
    last_date = date2num(self.df['Date'].values[current_step])
    last_close = self.df['Close'].values[current_step]
    last_high = self.df['High'].values[current_step]
    self.price_ax.annotate('{0:.2f}'.format(last_close),
                         (last_date, last_close),
                         xytext=(last_date, last_high),
                         bbox=dict(boxstyle='round', fc='w', ec='k', lw=1),
                         color="black",
                         fontsize="small")
    ylim = self.price_ax.get_ylim()
    self.price_ax.set_ylim(ylim[0] - (ylim[1] - ylim[0]) * VOLUME_CHART_HEIGHT, ylim[1])
def _render_volume(self, current_step, net_worth, dates, step_range):
    self.volume_ax.clear()
    volume = np.array(self.df['Volume'].values[step_range])
    pos = self.df['Open'].values[step_range] - self.df['Close'].values[step_range] < 0
    neg = self.df['Open'].values[step_range] - self.df['Close'].values[step_range] > 0
    self.volume_ax.bar(dates[pos], volume[pos], color=UP_COLOR, alpha=0.4, width=1, align='center')
    self.volume_ax.bar(dates[neg], volume[neg], color=DOWN_COLOR, alpha=0.4, width=1, align='center')
    self.volume_ax.set_ylim(0, max(volume) / VOLUME_CHART_HEIGHT)
    self.volume_ax.yaxis.set_ticks([])
def _render_trades(self, current_step, trades, step_range):
    for trade in trades:
        if trade['step'] in step_range:
            date = date2num(self.df['Date'].values[trade['step']])
            high = self.df['High'].values[trade['step']]
            low = self.df['Low'].values[trade['step']]
            if trade['type'] == 'buy':
                high_low = low
                color = UP_TEXT_COLOR
            else:
                high_low = high
                color = DOWN_TEXT_COLOR
            total = '{0:.2f}'.format(trade['total'])
            self.price_ax.annotate('$' + total,
                                 (date, high_low),
                                 xytext=(date, high_low),
                                 color=color,
                                 fontsize=8,
                                 arrowprops=(dict(color=color)))

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• date2num??? matplotlib.dates ??????????????????????
• StockTradingGraph????????????????????????????
• render???????????????????????
• **`render???????????????????????????????????

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????????????????????????????????????????????BTC????????????????????????????

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