def __init__(self, platform, symbol, time_frame):
self.__platform = platform
self.__symbol = symbol
self.__time_frame = time_frame
self.__market = MARKET(self.__platform, self.__symbol,
self.__time_frame)
# pull some data
self.__indicators = INDICATORS(self.__platform, self.__symbol,
self.__time_frame)
self.__kline = platform.get_kline(self.__time_frame)
self.__kline.reverse()
# format it in pandas
try: # dataframe有7列的情况
self.__df = pd.DataFrame(self.__kline,
columns=[
'time', 'open', 'high', 'low',
'close', 'volume', 'currency_volume'
])
self.__df = self.__df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64',
'currency_volume': 'float64'
})
except: # dataframe只有6列的情况，如okex的现货k线数据
self.__df = pd.DataFrame(
self.__kline,
columns=['time', 'open', 'high', 'low', 'close', 'volume'])
self.__df = self.__df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64'
})
# create three plot 创建三层图纸，第一层画k线，第二层画成交量，第三层画一些适宜于副图显示的指标
fplt.foreground = '#FFFFFF' # 前景色
fplt.background = '#333333' # 背景色
fplt.odd_plot_background = '#333333' # 第二层图纸的背景色
fplt.cross_hair_color = "#FFFFFF" # 准星的颜色
self.__ax, self.__ax2, self.__ax3 = fplt.create_plot(symbol, rows=3)
# plot candle sticks
candles = self.__df[['time', 'open', 'close', 'high', 'low']]
fplt.candlestick_ochl(candles, ax=self.__ax)
# overlay volume on the plot
volumes = self.__df[['time', 'open', 'close', 'volume']]
fplt.volume_ocv(volumes, ax=self.__ax2)
fplt.add_legend("VOLUME", self.__ax2) # 增加"VOLUME"图例
def plot_kline(kline):
"""
回测结束时绘制k线图
:param kline: 回测时传入指定的k线数据
:return:
"""
kline = kline
# kline.reverse()
# format it in pandas
try: # dataframe有7列的情况
df = pd.DataFrame(kline,
columns=[
'time', 'open', 'high', 'low', 'close', 'volume',
'currency_volume'
])
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64',
'currency_volume': 'float64'
})
except: # dataframe只有6列的情况，如okex的现货k线数据
df = pd.DataFrame(
kline, columns=['time', 'open', 'high', 'low', 'close', 'volume'])
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'close': 'float64',
'high': 'float64',
'low': 'float64',
'volume': 'float64'
})
# create three plot 创建三层图纸，第一层画k线，第二层画成交量，第三层画一些适宜于副图显示的指标
fplt.foreground = '#FFFFFF' # 前景色
fplt.background = '#333333' # 背景色
fplt.odd_plot_background = '#333333' # 第二层图纸的背景色
fplt.cross_hair_color = "#FFFFFF" # 准星的颜色
ax, ax2 = fplt.create_plot("KLINE", rows=2)
# plot candle sticks
candles = df[['time', 'open', 'close', 'high', 'low']]
fplt.candlestick_ochl(candles, ax=ax)
# overlay volume on the plot
volumes = df[['time', 'open', 'close', 'volume']]
fplt.volume_ocv(volumes, ax=ax2)
fplt.add_legend("VOLUME", ax2) # 增加"VOLUME"图例
fplt.show()
def update():
df = delta_bars.df
# Меняем индекс и делаем его типом datetime
df = df.set_index(
pd.to_datetime(df['date_time'], format='%Y-%m-%d %H:%M:%S'))
df = df.drop(
'date_time', 1
) # Удаляем колонку с датой и временем, т.к. дата у нас теперь в индексе
play_sound(df.tail(2)) # Вызов функции звукового сигнала
print(df)
# pick columns for our three data sources: candlesticks and TD
candlesticks = df['open close high low'.split()]
deltabarsec = df['open close sec'.split()]
delta = df['open close delta'.split()]
maxvolcluster = df['max_vol']
if not plots:
# first time we create the plots
global ax
plots.append(fplt.candlestick_ochl(candlesticks, candle_width=0.8))
plots.append(fplt.volume_ocv(deltabarsec, ax=ax2))
fplt.add_legend('Время формирования бара', ax=ax2)
plots.append(
fplt.volume_ocv(delta, colorfunc=fplt.strength_colorfilter,
ax=ax3))
fplt.add_legend('Дельта', ax=ax3)
plots.append(
fplt.plot(maxvolcluster,
legend='Max volume',
style='o',
color='#00f'))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(deltabarsec)
plots[2].update_data(delta)
plots[3].update_data(maxvolcluster)
def __init__(self, *args, **kwargs):
super(MainWindow, self).__init__(*args, **kwargs)
uic.loadUi('chart.ui', self)
self.initConnections()
pg.setConfigOptions(foreground=finplot.foreground, background=finplot.background)
fp = finplot.FinWindow(title="chart")
self.ax = finplot.create_plot_widget(fp, init_zoom_periods=100)
fp.ci.addItem(self.ax, row=0, col=0)
fp.show_maximized = True
self.plotWidget.setMaximumHeight(0)
self.plotWidget.axs = [self.ax] # finplot requires this property
self.verticalLayout.addWidget(fp)
self.dayDateEdit.setDate(datetime.date.today())
self.priceLine = None
self.stopPriceLine = None
self.candleItems = None
self.df = None
self.filename = None
self.isFileFirstOpen = True
self.hoverLabel = finplot.add_legend('', ax=self.ax)
finplot.set_time_inspector(self.update_legend_text, ax=self.ax, when='hover')
finplot.display_timezone = gettz('America/New_York')
fplt.set_y_scale(ax=ax1, yscale='log')
fplt.plot(btc.Close, color='#000', legend='Log price', ax=ax1)
btc['ma200'] = btc.Close.rolling(200).mean()
btc['ma50'] = btc.Close.rolling(50).mean()
fplt.plot(btc.ma200, legend='MA200', ax=ax1)
fplt.plot(btc.ma50, legend='MA50', ax=ax1)
btc['one'] = 1
fplt.volume_ocv(btc[['ma200', 'ma50', 'one']],
candle_width=1,
ax=ax1.overlay(scale=0.02))
daily_ret = btc.Close.pct_change() * 100
fplt.plot(daily_ret, width=3, color='#000', legend='Daily returns %', ax=ax2)
fplt.add_legend('Daily % returns histogram', ax=ax3)
fplt.hist(daily_ret, bins=60, ax=ax3)
fplt.add_legend('Yearly returns in %', ax=ax4)
fplt.bar(btc.Close.resample('Y').last().pct_change().dropna() * 100, ax=ax4)
# calculate monthly returns, display as a 4x3 heatmap
months = btc['Adj Close'].resample(
'M').last().pct_change().dropna().to_frame() * 100
months.index = mnames = months.index.month_name().to_list()
mnames = mnames[mnames.index('January'):][:12]
mrets = [months.loc[mname].mean()[0] for mname in mnames]
hmap = pd.DataFrame(columns=[2, 1, 0], data=np.array(mrets).reshape((3, 4)).T)
hmap = hmap.reset_index(
) # use the range index as X-coordinates (if no DateTimeIndex is found, the first column is used as X)
colmap = fplt.ColorMap(
plots.append(fplt.plot(middleband, legend="MIDDLEBAND"))
plots.append(fplt.plot(lowerband, legend="LOWERBAND"))
else:
# every time after we just update the data sources on each plot
plots[0].update_data(candlesticks)
plots[1].update_data(volumes)
plots[2].update_data(upperband)
plots[3].update_data(middleband)
plots[4].update_data(lowerband)
if __name__ == "__main__":
try:
kline = Kline()
plots = []
fplt.foreground = '#FFFFFF' # 前景色
fplt.background = '#333333' # 背景色
fplt.odd_plot_background = '#333333' # 第二层图纸的背景色
fplt.cross_hair_color = "#FFFFFF" # 准星的颜色
ax, ax2 = fplt.create_plot('Realtime kline',
init_zoom_periods=100,
maximize=False,
rows=2)
fplt.add_legend("VOLUME", ax2) # 增加"VOLUME"图例
kline.update()
fplt.timer_callback(
kline.update,
5.0) # update (using synchronous rest call) every N seconds
fplt.show()
except:
logger.error()
signal = macd.ewm(span=9).mean()
df['macd_diff'] = macd - signal
fplt.volume_ocv(df[['Date', 'Open', 'Close', 'macd_diff']],
ax=ax2,
colorfunc=fplt.strength_colorfilter)
fplt.plot(macd, ax=ax2, legend='MACD')
fplt.plot(signal, ax=ax2, legend='Signal')
# change to b/w coloring templates for next plots
fplt.candle_bull_color = fplt.candle_bear_color = '#000'
fplt.volume_bull_color = fplt.volume_bear_color = '#333'
fplt.candle_bull_body_color = fplt.volume_bull_body_color = '#fff'
# plot price and volume
fplt.candlestick_ochl(df[['Date', 'Open', 'Close', 'High', 'Low']], ax=ax)
hover_label = fplt.add_legend('', ax=ax)
axo = ax.overlay()
fplt.volume_ocv(df[['Date', 'Open', 'Close', 'Volume']], ax=axo)
fplt.plot(df.Volume.ewm(span=24).mean(), ax=axo, color=1)
#######################################################
## update crosshair and legend when moving the mouse ##
def update_legend_text(x, y):
row = df.loc[df.Date == x]
# format html with the candle and set legend
fmt = '<span style="color:#%s">%%.2f</span>' % ('0b0' if (
row.Open < row.Close).all() else 'a00')
rawtxt = '<span style="font-size:13px">%%s %%s</span> O%s C%s H%s L%s' % (
fmt, fmt, fmt, fmt)
def plt_chart(fa_info, save_chart=False, interactive=False):
global df
global symbol
global file_png
# load data and convert date
symbol, sector, industry, website, num_emp, profile = fa_info
return_result = (None, None)
end_t = int(time())
start_t = end_t - NUM_OF_MONTHS * 30 * 24 * 60 * 60 # twelve months
interval = '1d'
url = 'https://query1.finance.yahoo.com/v7/finance/download/%s?period1=%s&period2=%s&interval=%s&events=history' % (
symbol, start_t, end_t, interval)
try:
r = requests.get(url)
df = pd.read_csv(StringIO(r.text))
if df.empty:
print(f"[Warn] symbol {symbol} has no data")
return return_result
if df.shape[0] < MA_SLOW:
print(
f"[Warn] symbol {symbol} has fewer than {MA_SLOW} data points")
return return_result
except Exception as ex:
print(
f"[Error] failed to download quote from Yahoo finance for {symbol}"
)
return return_result
last_date = str(df.tail(1)['Date'].values[0]).split("T")[0]
df['Date'] = pd.to_datetime(df['Date']).astype(
'int64') # use finplot's internal representation, which is ns
log_msg = ""
if sector:
title = f"[{symbol}] {website} - {sector} - {industry} {last_date}"
log_msg += title + "\n" + profile
else:
title = f"[{symbol}] {last_date}"
log_msg += title
log_msg += "\n======================================================="
print(log_msg)
# with open("_finplot-watchlist.log", "a") as f:
# f.write(log_msg)
# print(__file__)
pardir = os.path.dirname(__file__)
chart_dir = os.path.join(pardir, "charts", today_str)
if not os.path.exists(chart_dir):
os.mkdir(chart_dir)
file_png = os.path.join(chart_dir, f"{symbol}_{today_str}.png")
# print(file_png)
ax, ax1, ax2 = fplt.create_plot(title, rows=3)
# plot price
fplt.candlestick_ochl(df[['Date', 'Open', 'Close', 'High', 'Low']], ax=ax)
hover_label = fplt.add_legend(symbol, ax=ax)
ma15 = df.Close.ewm(span=MA_FAST).mean()
ma50 = df.Close.ewm(span=MA_SLOW).mean()
ma200 = df.Close.ewm(span=MA_LONG).mean()
fplt.plot(ma15, ax=ax, color=COLOR_IDX["blue"], width=1)
fplt.plot(ma50, ax=ax, color=COLOR_IDX["red"], width=2)
fplt.plot(ma200, ax=ax, color=COLOR_IDX["green"], width=3)
# plot macd with standard colors first
macd = ma15 - ma50
signal = macd.ewm(span=MACD_AVG).mean()
trend = TREND_FACTOR * (ma50 - ma200)
df['macd_diff'] = MACD_FACTOR * (macd - signal)
fplt.volume_ocv(df[['Date', 'Open', 'Close', 'macd_diff']],
ax=ax1,
colorfunc=fplt.strength_colorfilter)
fplt.plot(macd, ax=ax1)
# fplt.plot(signal, ax=ax1, legend='Signal')
fplt.plot(macd, ax=ax1, width=COLOR_IDX["red"])
fplt.plot(signal, ax=ax1, color=COLOR_IDX["black"])
fplt.plot(trend, ax=ax1, width=2, color=COLOR_IDX["blue"])
# # change to b/w coloring templates for next plots
# fplt.candle_bull_color = fplt.candle_bear_color = '#000'
# fplt.volume_bull_color = fplt.volume_bear_color = '#333'
# fplt.candle_bull_body_color = fplt.volume_bull_body_color = '#fff'
# plot volume
# axo = ax.overlay()
vol_factor = 100000
df['Volume'] = df['Volume'] / vol_factor
fplt.volume_ocv(df[['Date', 'Open', 'Close', 'Volume']], ax=ax2)
fplt.plot(df.Volume.ewm(span=20).mean(),
ax=ax2,
color=COLOR_IDX["black"],
width=2)
# if interactive:
# fplt.set_time_inspector(update_legend_text, ax=ax, when='hover')
# fplt.add_crosshair_info(update_crosshair_text, ax=ax)
if save_chart:
fplt.timer_callback(save, 0.5,
single_shot=True) # wait some until we're rendered
# print(chart_info)
return_result = (fplt, file_png)
return return_result
daily_plot = fplt.candlestick_ochl(dfd.dropna(), candle_width=5)
daily_plot.colors.update(
dict(bull_body='#bfb',
bull_shadow='#ada',
bear_body='#fbc',
bear_shadow='#dab'))
daily_plot.x_offset = 3.1 # resample() gets us start of day, offset +1.1 (gap+off center wick)
# plot high resolution on top
fplt.candlestick_ochl(df[['Open', 'Close', 'High', 'Low']])
# scatter plot correlation between Google and Microsoft stock
df['ret_alphabet'] = df.Close.pct_change()
df['ret_microsoft'] = dfms.Close.pct_change()
dfc = df.dropna().reset_index(drop=True)[['ret_alphabet', 'ret_microsoft']]
fplt.plot(dfc, style='o', color=1, ax=ax2)
# draw least-square line
errfun = lambda arr: [
y - arr[0] * x + arr[1]
for x, y in zip(dfc.ret_alphabet, dfc.ret_microsoft)
]
line = scipy.optimize.least_squares(errfun, [0.01, 0.01]).x
linex = [dfc.ret_alphabet.min(), dfc.ret_alphabet.max()]
liney = [linex[0] * line[0] + line[1], linex[1] * line[0] + line[1]]
fplt.add_line((linex[0], liney[0]), (linex[1], liney[1]), color='#993', ax=ax2)
fplt.add_text((linex[1], liney[1]), 'k=%.2f' % line[0], color='#993', ax=ax2)
fplt.add_legend('Alphabet vs. Microsft 90m correlation', ax=ax2)
fplt.show()
def updateCandlePane(self, quotes):
self.ax.reset()
finplot.candlestick_ochl(quotes)
finplot.refresh()
self.hoverLabel = finplot.add_legend('', ax=self.ax)
finplot.set_time_inspector(self.updateLegend, ax=self.ax, when='hover')
def plot_signal(kline, buy_signal=None, sell_signal=None, *args):
"""回测完成后调用此函数绘制k线图与指标"""
fplt.foreground = '#FFFFFF'
fplt.background = '#333333'
fplt.odd_plot_background = '#333333'
fplt.cross_hair_color = "#FFFFFF"
ax, ax2, ax3 = fplt.create_plot('历史K线图',
init_zoom_periods=100,
maximize=True,
rows=3)
fplt.add_legend("K线主图", ax)
fplt.add_legend("成交量", ax2)
fplt.add_legend("指标副图", ax3)
df = pd.DataFrame(kline)
df = df[[0, 1, 2, 3, 4, 5]]
columns = ['time', 'open', 'high', 'low', 'close', 'volume']
df.columns = columns
df = df.astype({
'time': 'datetime64[ns]',
'open': 'float64',
'high': 'float64',
'low': 'float64',
'close': 'float64',
'volume': 'float64'
})
candlesticks = df['time open close high low'.split()]
volumes = df['time open close volume'.split()]
fplt.candlestick_ochl(candlesticks)
fplt.volume_ocv(volumes, ax=ax2)
if args:
count = 1
for i in args:
indicators = pd.Series(i)
fplt.plot(df['time'],
indicators,
legend="指标{}".format(count),
ax=ax3)
count += 1
if buy_signal:
for i in buy_signal:
df.loc[df['high'] == buy_signal[buy_signal.index(i)],
"buy_signal"] = df['high']
fplt.plot(df['time'],
df['buy_signal'],
ax=ax,
color="#FF0000",
style='^',
width=2,
legend='买入信号')
if sell_signal:
for i in sell_signal:
df.loc[df['low'] == sell_signal[sell_signal.index(i)],
"sell_signal"] = df['low']
fplt.plot(df['time'],
df['sell_signal'],
ax=ax,
color="#00FF00",
style='v',
width=2,
legend='卖出信号')
fplt.show()
