def candlesticks(ticker, date, interval, tickers=[]):
if len(tickers) == 0:
SPX = priceHistoryConfigs(ticker, date, interval)
mc = mpf.make_marketcolors(up='g', down='r')
s = mpf.make_mpf_style(marketcolors=mc)
setup = dict(type='candle',
mav=(7, 11, 20),
volume=True,
figratio=(11, 8),
figscale=0.85,
style=s)
mpf.plot(SPX.iloc[0:len(SPX) - 1], **setup)
plt.show()
else:
for k in tickers:
SPX = priceHistoryConfigs(k[0], date, interval)
mc = mpf.make_marketcolors(up='g', down='r')
s = mpf.make_mpf_style(marketcolors=mc)
setup = dict(type='candle',
mav=(7, 11, 20),
volume=True,
figratio=(11, 8),
figscale=0.85,
style=s)
plt.figure(runner)
mpf.plot(SPX.iloc[0:len(SPX) - 1], **setup)
plt.show()
def ohlc_to_image(tickers,
start=0,
end=0,
category="daily",
dpi=300,
type="candle"):
for ticker in tickers:
path = f'./datasets/{ticker}/'
if not os.path.isdir(path):
os.mkdir(path)
try:
print(f"{ticker} : start -> ", end="")
csv_data = pd.read_csv(f"./datasets/{category}/{ticker}.csv",
index_col=0,
parse_dates=True)
if (start != 0) or (end != 0):
csv_data = csv_data[start:end]
csv_data.index.name = "Date"
mc = mpf.make_marketcolors(up='r', down='b')
#up과 down에는 색상 지정할수 있다. 예 #ffffff
s = mpf.make_mpf_style(marketcolors=mc)
file_path = path + f'{ticker}_{type}'
savefig = dict(fname=file_path, dpi=300, pad_inches=0.25)
mpf.plot(csv_data, type=type, style=s, savefig=savefig)
except err:
print(err)
print(f"{ticker} 에서 오류발생")
def make_mav_candlestick_mplfinance(df, save_png: str) -> None:
"""
mplfinanceを使って移動平均線+ローソク足画像作成する
※論文では出来高入れない方が精度良さそうだったから出来高は入れない
"""
import sys
current_dir = pathlib.Path(__file__).resolve().parent
sys.path.append(str(current_dir) + "/../GitHub/mplfinance/src")
import mplfinance as mpf
kwargs = dict(
type="candle",
figratio=(1.5, 1.5),
figscale=1,
title="",
ylabel="",
ylabel_lower="",
mav=(5, 10),
)
mc = mpf.make_marketcolors(up="#FF0000", down="#0000FF", inherit=True)
s = mpf.make_mpf_style(base_mpf_style="nightclouds", marketcolors=mc, gridstyle="")
mpf.plot(
df, **kwargs, style=s, is_not_set_visible=True, savefig=save_png,
)
def plot(df: pd.DataFrame, title: str, mav: Tuple[int, int]):
"""
Args:
df
title:
mav:
Returns:
"""
style = mpf.make_mpf_style(base_mpf_style='yahoo',
marketcolors=mpf.make_marketcolors(
up='r',
down='g',
edge='',
wick='inherit',
volume='inherit'))
mpf.plot(df,
type='candle',
mav=mav,
volume=True,
style=style,
title=title,
ylabel_lower='Shares')
def ohlc_plot_protype(ohlc_data, code=None, codename=None, title=None):
# 暗色主题
plt.style.use('Solarize_Light2')
# 正常显示中文字体
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
# 设定布局
fig = plt.figure(figsize=(22, 9))
plt.subplots_adjust(left=0.04, right=0.96)
if (title is None):
fig.suptitle(u'阿财的 {:s}（{:s}）量化学习笔记绘制K线DEMO'.format(codename, code),
fontsize=16)
else:
fig.suptitle(title, fontsize=16)
ax1 = fig.add_subplot(111)
# 绘制K线
ohlc_data = ohlc_data.reset_index([1], drop=False)
mc_stock_cn = mpf.make_marketcolors(up='r', down='g')
s_stock_cn = mpf.make_mpf_style(marketcolors=mc_stock_cn)
mpf.plot(data=ohlc_data, ax=ax1, type='candle', style=s_stock_cn)
# 设定最标轴时间
datetime_index = ohlc_data.index.get_level_values(level=0).to_series()
DATETIME_LABEL = datetime_index.apply(
lambda x: x.strftime("%Y-%m-%d %H:%M")[2:16])
ax1.set_xticks(
range(0, len(DATETIME_LABEL), round(len(DATETIME_LABEL) / 12)))
ax1.set_xticklabels(DATETIME_LABEL[::round(len(DATETIME_LABEL) / 12)])
ax1.grid(True)
return ax1
def create(data, csv_rows, X):
n = csv_rows - 22
q = 0
w = 50
num = 1
while w < n:
s = mpf.make_mpf_style(base_mpf_style='yahoo',
gridcolor='white',
figcolor='white',
edgecolor='white',
facecolor='white')
mpf.plot(
data[q:w],
type='candle',
show_nontrading=False,
savefig=dict(
fname=
'scripts/Finance_Data/Chart_Snapshot/TEST/operation/{name}_{number}test.jpg'
.format(name=X, number=num)),
style=s,
axisoff=False)
f = open("darknet/data/test.txt", 'a')
f.write(
"\nscripts/Finance_Data/Chart_Snapshot/TEST/operation/{name}_{number}test.jpg"
.format(name=X, number=num))
q += 50
w += 50
num += 1
def draw_candle_chart(stock_id):
stock_id = str(stock_id) + ".TW" # Yahoo Finance 的 代號為台灣的代號 + .TW
data = yf.Ticker(stock_id) # 抓取資料
# 1mo = 1個月，max 可以把所有期間的資料都下載
ohlc = data.history(period="2mo")
ohlc = ohlc.loc[:, ["Open", "High", "Low", "Close",
"Volume"]] # 選擇製圖需要欄位(開高低收量)
# 調整圖表標示顏色
mc = fplt.make_marketcolors(
up='tab:red',
down='tab:green', # 上漲為紅，下跌為綠
wick={
'up': 'red',
'down': 'green'
}, # 影線上漲為紅，下跌為綠
volume='tab:green', # 交易量顏色
)
s = fplt.make_mpf_style(marketcolors=mc) # 定義圖表風格
fplt.plot(
ohlc, # 開高低收量的資料
type='candle', # 類型為蠟燭圖，也就是 K 線圖
style=s, # 套用圖表風格
title=stock_id, # 設定圖表標題
ylabel='Price ($)', # 設定 Y 軸標題
volume=True,
savefig='stock_Kbar.png', # 儲存檔案
)
def draw_chart(symbol, hist):
mc = fplt.make_marketcolors(up='tab:blue',
down='tab:red',
edge='black',
wick={
'up': 'blue',
'down': 'red'
},
volume='green',
alpha=1.0)
s = fplt.make_mpf_style(marketcolors=mc,
mavcolors=["yellow", "orange", 'brown'])
image_file = f"./live_graphs/{symbol}_{hist.index[-1].isoformat().replace(':', '-')}.png"
fplt.plot(hist.iloc[-46:],
type='candle',
style=s,
volume=True,
axisoff=True,
mav=(5, 8, 13),
figsize=(3.2, 3.2),
xlim=(12.5, 44.5),
savefig=image_file,
tight_layout=True,
returnfig=False)
return image_file
def gen_graph(stock, timespan, multiplier, start, end):
r = requests.get(f'https://api.polygon.io/v2/aggs/ticker/{stock.upper()}/range/{multiplier}/{timespan}/{start.strftime("%Y-%m-%d")}/{end.strftime("%Y-%m-%d")}?apiKey={VERY_SECRET_LAUNCH_CODES.HYDROGEN_LAUNCH_CODE()}')
# Copy pasted code to format dataframe for mplfinance
df = pd.DataFrame(r.json()['results'])
est = pytz.timezone('US/Eastern')
utc = pytz.utc
df.index = [datetime.utcfromtimestamp(ts / 1000.).replace(tzinfo=utc).astimezone(est) for ts in df['t']]
df.index.name = 'Date'
df.columns = ['Volume', 'Volume Weighted', 'Open', 'Close', 'High', 'Low', 'Time', 'Num Items']
# Very dank custom mpl theme copyright your mom LLC
mc = mpf.make_marketcolors(up='#00a320', down='#ff3334', inherit=True)
s = mpf.make_mpf_style(base_mpf_style='nightclouds', marketcolors=mc, gridaxis='horizontal', gridcolor='#454545', gridstyle='solid', edgecolor='#454545', facecolor='#000000')
# This is probably the most autistic shit I have ever written in my life
delta = end - start
weekend = {5, 6}
splits = []
for i in range(delta.days + 1):
if (start + timedelta(days=i)).weekday() not in weekend:
splits.append((start + timedelta(days=i)).strftime("%Y-%m-%d" + " 09:30"))
splits.append((start + timedelta(days=i)).strftime("%Y-%m-%d" + " 16:00"))
# Saves graph
saving_params = dict(fname='plot.png', dpi=1200)
mpf.plot(df, type='candle', volume=True, style=s, savefig=saving_params, vlines=dict(vlines=splits, linewidths=0.5, linestyle='-.', colors="#454545"))
def plot_candle(df,start_index,end_index,bottom,output_path):
#df = pd.read_csv(r'D:\stock\history\000001.SZ.csv')
sub_df = df[['trade_date','open','high','low','close','vol']].copy()
# daily = sub_df.head(30)
sub_df.rename(columns={'trade_date':'Date', 'open':'Open', 'high':'High','low':'Low', 'close':'Close','vol':'Volume'}, inplace = True)
# daily['Date'] = pd.to_datetime(daily['Date'])
sub_df[['Date']] = sub_df[['Date']].astype(str)
# print(sub_df['Date'])
sub_df['Date'] = pd.to_datetime(sub_df['Date'])
start_date = sub_df['Date'][start_index]
end_date = sub_df['Date'][end_index]
sub_df.set_index("Date", inplace=True)
kwargs = dict(type='candle',volume=True,figratio=(11,8),figscale=0.85)
mc = mpf.make_marketcolors(up='r',down='g',volume='in')
s = mpf.make_mpf_style(marketcolors=mc)
# mpf.plot(daily,**kwargs,style=s,savefig='testsave.png')
saved_filename = '{}_{}.png'.format(str(start_date).split(' ')[0],df['ts_code'][0].split('.')[0])
outputfile = os.path.join(output_path,saved_filename)
# mpf.plot(sub_df, **kwargs, style=s,vlines=dict(vlines=[start_date,end_date],linewidths=(0.5,0.5),linestyle='-.'),\
# hlines=dict(hlines=[bottom],linewidths=(0.5),colors=['r'],linestyle='-.'),savefig=outputfile)
mpf.plot(sub_df, **kwargs, style=s,\
hlines=dict(hlines=[bottom],linewidths=(0.5),colors=['r'],linestyle='-.'),savefig=outputfile,title=saved_filename)
def chart():
df = dataframe()
df.index = pd.DatetimeIndex(df['timestamp'])
df = df.iloc[::-1]
s = mpf.make_mpf_style(base_mpf_style='charles', gridcolor='#555555', gridstyle="--", rc={'axes.edgecolor': 'white', 'font.size': 5})
fig, axlist = mpf.plot(df, type='candle', style=s, title= coin, ylabel = 'Price (€)', volume=True, warn_too_much_data=9999999, returnfig=True)
mpf.show(block=True)
def plot_graph(ticker, df):
mc = mpl.make_marketcolors(up='green', down='red', inherit=True)
s = mpl.make_mpf_style(base_mpl_style='ggplot',
marketcolors=mc, y_on_right=False, mavcolors=['#1f77b4', '#ff7f0e', '#2ca02c'], gridcolor='#b6b6b6',
gridstyle='--', figcolor='#eee9e9', edgecolor='#8b8585')
ap = [mpl.make_addplot(df['Bought'], type='scatter',markersize=200,marker='>', color='#29854f', panel=1),
mpl.make_addplot(df['Sold'], type='scatter', marker='<', markersize=200, color='#720c06', panel=1),
mpl.make_addplot(df['ATR'], type='line', panel=0, ylabel='ATR', color='#8774AB', secondary_y=False, ylim=(
min(df['ATR']), max(df['ATR'])
)),
mpl.make_addplot(df['Lower B'], type='line', panel=1, color='#3838ea', alpha=0.50),
mpl.make_addplot(df['Upper B'], type='line', panel=1, color='#3838ea', alpha=0.50),
mpl.make_addplot(df['70'], panel=2, type='line', secondary_y=False, ylim=(0, 100), color='r', alpha=0.25),
mpl.make_addplot(df['30'], panel=2, type='line', secondary_y=False, ylim=(0, 100), color='g', alpha=0.25),
mpl.make_addplot(df['RSI'], panel=2, type='line', ylabel='RSI', secondary_y=False, ylim=(0, 100)),
mpl.make_addplot(df['SMA_20'], panel=1, type='line', alpha=0.5, color='orange'),
mpl.make_addplot(df['macdline'], type='line', color='purple', panel=3, secondary_y=False),
mpl.make_addplot(df['signalline'], type ='line', color='orange', panel=3, secondary_y=False),
mpl.make_addplot(df['hist'], type='bar',panel=3, ylabel='MACD',color='#9c9796'),
mpl.make_addplot(df['0'],type='line',panel=3,color='k',secondary_y=False,
ylim=((min(df['signalline']-1), (max(df['signalline']+0.5))))),
mpl.make_addplot(df['Chandelier_Exit'],type='scatter',marker='_', panel=1)]
mpl.plot(df, title=ticker, type='candle', style=s, ylabel='Price'
, addplot=ap, panel_ratios=(0.7, 2, 0.7, 0.8), figratio=(2, 1),
figscale=1.1, datetime_format='%m-%Y', tight_layout=True, main_panel=1,
ylim=(min(df['Adj Close']-2), max(df['Adj Close']+2)),
fill_between=dict(y1=df['Lower B'].values, y2=df['Upper B'].values, color='#f2ad73', alpha=0.20))
plt.show()
def style_test():
now = datetime.datetime.today().strftime('%Y%m%d')
df_1 = get_stock_price_df(stock_code='002415.SZ',
start_date='20160101',
end_date=now)
my_color = mpf.make_marketcolors(up='red',
down='green',
edge='inherit',
wick='inherit',
volume='inherit')
my_style = mpf.make_mpf_style(marketcolors=my_color,
gridaxis='both',
gridstyle='-.',
y_on_right=True)
# fig = plt.figure()
# 建立划分1行1列的子图，取第1幅子图
# ax1 = fig.add_subplot(1, 1, 1)
mpf.plot(df_1,
type='candle',
volume=True,
figscale=1.5,
style=my_style,
title='****报价',
figratio=(5, 5),
ylabel='价格',
ylabel_lower='成交量',
savefig='my_image.png')
print('end')
def plot_kline(stock_data, volume=False, mav=(5, 10)):
rename_map = {
"交易日期": "Date",
"开盘价": "Open",
"收盘价": "Close",
"最高价": "High",
"最低价": "Low",
"成交量": "Volume",
}
stock_data = stock_data.rename(columns=rename_map)
stock_data = stock_data[list(rename_map.values())]
stock_data.set_index('Date', inplace=True)
print(stock_data)
"""
up: 设置上涨K线的颜色
down: 设置下跌K线的颜色
edge=inherit: K线图边缘和主题颜色保持一致
volume=in: 成交量bar的颜色继承K线颜色
wick=in: 上下引线颜色继承K线颜色
"""
mc = mpf.make_marketcolors(up='r',
down='g',
volume='in',
edge='inherit',
wick='in')
s = mpf.make_mpf_style(marketcolors=mc)
mpf.plot(stock_data,
type='candle',
style=s,
volume=volume,
mav=mav,
figratio=(9, 6),
figscale=2)
def plot_profitline(stock_data,
profit_ratio,
title='',
output=os.path.join(_test_path, 'profitline.jpg')):
stock_data['close'] = profit_ratio
stock_profitline = stock_data.set_index("date")
stock_profitline.index = pd.to_datetime(stock_profitline.index)
stock_profitline = stock_profitline.astype(float)
kwargs = dict(type='line',
volume=False,
ylabel='Return Rate',
figratio=(11, 8),
figscale=0.85)
style = mpf.make_mpf_style(base_mpf_style='yahoo', rc={'font.size': 8})
fig, axes = mpf.plot(stock_profitline,
**kwargs,
style=style,
scale_padding={
'left': 0.1,
'top': 1,
'right': 1,
'bottom': 1
},
returnfig=True)
axes[0].yaxis.set_major_formatter(
matplotlib.ticker.PercentFormatter(xmax=1.0))
axes[0].set_title(title)
fig.savefig(output, dpi=300)
plt.close(fig)
def __call__(self, show_buy_and_sell=False, analyze_positions=False, make_plot_param={}):
self.cal_technical_index()
if show_buy_and_sell:
res_list = []
data_dict = {}
item_list = ["date", "open", "close", "low", "high", "volume", "trade", "MACD", "MACDsignal", "MACDhist",
"slowk", "slowd", "slowj"]
for index, row in self.data.iterrows():
for item in item_list:
data_dict[item] = row[item]
res_list.append(data_dict.copy())
return json.dumps(res_list, indent=2, ensure_ascii=False)
self.strategy_exec()
if analyze_positions:
self.eval_index()
is_make_plot = make_plot_param.get("is_make_plot", False)
if is_make_plot:
self.data['Date'] = pd.to_datetime(self.data["date"])
self.data.set_index('Date', inplace=True)
my_color = mpf.make_marketcolors(up="red", down="green", edge="inherit", volume="inherit")
my_style = mpf.make_mpf_style(marketcolors=my_color)
add_plot = [mpf.make_addplot(self.data[['wma10']])]
mpf.plot(self.data, type="line", ylabel="price", style=my_style, volume=True, ylabel_lower="volume",
addplot=add_plot)
def plot_benchmark(self, stock_code):
import pandas as pd
import math
from dev_global.env import TIME_FMT
df = self.mysql.select_values(
stock_code,
'trade_date,open_price,close_price,highest_price,lowest_price,volume')
# data cleaning
df.columns = ['Date', 'Open', 'Close', 'High', 'Low', 'Volume']
df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)
# data constructing
LEN = 70
df = df[-LEN:]
import mplfinance as mpf
usr_color = mpf.make_marketcolors(up='red', down='green')
usr_style = mpf.make_mpf_style(marketcolors=usr_color)
plt, _ = mpf.plot(
df,returnfig=True, type='candle', mav=(5,10,20),
volume=True, title=stock_code, style=usr_style,
datetime_format='%Y-%m-%d'
)
#img_path = f"/home/friederich/Documents/dev/neutrino/applications/template/{stock_code}.png"
img_path = f"/var/www/neutrino/static/images/{stock_code}.png"
plt.savefig(img_path, format='png')
def get_style(background_colour, line_colour, text_colour):
market_colours = mpf.make_marketcolors(up="g",
down="r",
edge=background_colour,
wick=line_colour)
style_dict = {
"xtick.color": line_colour,
"ytick.color": line_colour,
"xtick.labelcolor": text_colour,
"ytick.labelcolor": text_colour,
"axes.spines.top": False,
"axes.spines.right": False,
"axes.labelcolor": text_colour,
"axes.labelsize": 18
}
style = mpf.make_mpf_style(marketcolors=market_colours,
facecolor=background_colour,
edgecolor=line_colour,
figcolor=background_colour,
gridcolor=line_colour,
gridstyle="--",
rc=style_dict)
return style
def run(pickle_file=PICKLE_FILE):
mc = mpf.make_marketcolors(up='cyan', down='fuchsia', inherit=True)
style = mpf.make_mpf_style(base_mpf_style='nightclouds',
gridstyle='',
marketcolors=mc)
fig = mpf.figure(style=style, figsize=(19, 20))
plt.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.95, 0.95)
df1 = utils.try_to_get_df(pickle_file)
ntickets = len(df1.groupby('Papel').first().index.to_list())
positions = []
j1 = 0
for i in range(ntickets):
if ntickets % 2 != 0 and i == ntickets - 1:
positions.append((j1 + 1, j1 + 68))
j1 += 80
elif i % 2 == 0:
positions.append((j1 + 1, j1 + 68))
positions.append((j1 + 5, j1 + 72))
j1 += 80
dimension = [j1 / 8, 8]
global axes
axes = []
for pos in positions:
ax = fig.add_subplot(dimension[0], dimension[1], pos)
axes.append(ax)
a = ScrollableWindow(fig, analysis, pickle_file)
def plot_candlestick(self, df, addplot_list=None):
print('[Plot]: plot_candlestick()')
# 设置基本参数
# type:绘制图形的类型，有candle, renko, ohlc, line等
# 此处选择candle,即K线图
# mav(moving average):均线类型,此处设置7,30,60日线
# volume:布尔类型，设置是否显示成交量，默认False
# title:设置标题
# y_label_lower:设置成交量图一栏的标题
# figratio:设置图形纵横比
# figscale:设置图形尺寸(数值越大图像质量越高)
kwargs = dict(
type='candle',
volume=True,
# title='\nA_stock %s candle_line' % (symbol),
ylabel='OHLC Candles',
ylabel_lower='Shares\nTraded Volume',
figratio=(15, 10),
figscale=5)
# 设置marketcolors
# up:设置K线线柱颜色，up意为收盘价大于等于开盘价
# down:与up相反，这样设置与国内K线颜色标准相符
# edge:K线线柱边缘颜色(i代表继承自up和down的颜色)，下同。详见官方文档)
# wick:灯芯(上下影线)颜色
# volume:成交量直方图的颜色
# inherit:是否继承，选填
mc = mpf.make_marketcolors(up='red',
down='green',
edge='i',
wick='i',
volume='in',
inherit=True)
# 设置图形风格
# gridaxis:设置网格线位置
# gridstyle:设置网格线线型
# y_on_right:设置y轴位置是否在右
s = mpf.make_mpf_style(gridaxis='both',
gridstyle='-.',
y_on_right=False,
marketcolors=mc)
# 设置均线颜色，配色表可见下图
# 建议设置较深的颜色且与红色、绿色形成对比
# 此处设置七条均线的颜色，也可应用默认设置
mpl.rcParams['axes.prop_cycle'] = cycler(color=[
'dodgerblue', 'deeppink', 'navy', 'teal', 'maroon', 'darkorange',
'indigo'
])
# 设置线宽
mpl.rcParams['lines.linewidth'] = .5
if addplot_list is not None:
addplot_list = self._make_addplot(addplot_list)
mpf.plot(df, **kwargs, style=s, addplot=addplot_list)
else:
kwargs['mav'] = (7, 30, 60)
mpf.plot(df, **kwargs, style=s)
def image_preprocessing(pattern_data, pattern_csv):
#pattern_data for shown pattern
#pattern csv for get the data of shown_pattern
uuid_list = []
whole_data = pattern_data['bull'] + pattern_data['bear']
path = path = os.path.join(os.getcwd(), 'ml_model/image_db/')
for data in whole_data:
#get the timedata
date = data[1]
nei_data = pattern_csv.iloc[pattern_csv.index.get_loc(date) -
2:pattern_csv.index.get_loc(date) + 3]
#uuid for image name
name = uuid.uuid4()
uuid_list.append((date, name))
#시각과 image 이름을 기억한다.
#mplplot setting
mc = mpf.make_marketcolors(up='r', down='b')
s = mpf.make_mpf_style(marketcolors=mc)
savefig = dict(fname=f'{path}/{name}.png',\
dpi=50,pad_inches=0,bbox_inches='tight')
#plot
mpf.plot(nei_data,
axisoff=True,
type='candle',
style=s,
savefig=savefig)
return uuid_list
def plot_ka(ka, file_image, mav=(5, 20, 120, 250), max_k_count=1000, dpi=50):
"""绘制 ka，保存到 file_image"""
df = pd.DataFrame(ka.kline)
df.rename(
{
"open": "Open",
"close": "Close",
"high": "High",
"low": "Low",
"vol": "Volume"
},
axis=1,
inplace=True)
df.index = pd.to_datetime(df['dt'])
df = df.tail(max_k_count)
kwargs = dict(type='candle', mav=mav, volume=True)
bi_xd = [[(x['dt'], x['bi']) for _, x in df.iterrows() if x['bi'] > 0],
[(x['dt'], x['xd']) for _, x in df.iterrows() if x['xd'] > 0]]
mc = mpf.make_marketcolors(up='red',
down='green',
edge='i',
wick='i',
volume='in',
inherit=True)
s = mpf.make_mpf_style(gridaxis='both',
gridstyle='-.',
y_on_right=False,
marketcolors=mc)
mpl.rcParams['font.sans-serif'] = ['KaiTi']
mpl.rcParams['font.serif'] = ['KaiTi']
mpl.rcParams['font.size'] = 48
mpl.rcParams['axes.unicode_minus'] = False
mpl.rcParams['lines.linewidth'] = 1.0
title = '%s@%s（%s - %s）' % (ka.symbol, ka.name, df.index[0].__str__(),
df.index[-1].__str__())
fig, axes = mpf.plot(df,
columns=['Open', 'High', 'Low', 'Close', 'Volume'],
style=s,
title=title,
ylabel='K线',
ylabel_lower='成交量',
**kwargs,
alines=dict(alines=bi_xd,
colors=['r', 'g'],
linewidths=8,
alpha=0.35),
returnfig=True)
w = len(df) * 0.15
fig.set_size_inches(w, 30)
ax = plt.gca()
ax.set_xbound(-1, len(df) + 1)
fig.savefig(fname=file_image, dpi=dpi, bbox_inches='tight')
plt.close()
def show_adv(self, ftitle="Stock", **kwargs):
"""
顯示進階K線圖，K線搭配一主圖指標與兩個副圖指標
使用mplfinance實現之(panel method)
已定義的作圖參數：
ti_main = "SMA" / "VWMA"
ti_sub1 = "KD" / "MACD" / "STD" / "VIX" / "CVOL"
ti_sub2 = "KD" / "MACD" / "STD" / "VIX" / "CVOL"
"""
self._setrun(**kwargs) # 進行參數驗證與指標計算
dest = Dpath + '\\' + ftitle + ".png"
mc = mpf.make_marketcolors(up='r', down='g', inherit=True)
ms = mpf.make_mpf_style(base_mpf_style = "yahoo", \
marketcolors = mc, y_on_right = True)
# 不支援中文字型，圖表文字需為英文
mfargs = dict(type = "candle", volume = True, \
columns = ("O", "H", "L", "C", "V"), \
show_nontrading = False, returnfig = True, \
figsize = (self.fx, self.fy), \
title = ftitle, style = ms, ylabel = "Price", \
ylabel_lower = "Volume in shares", \
panel_ratios = (1, 1))
aps = []
if "ti_main" in kwargs:
tidf = self.val_main.copy()
isma = False
if kwargs["ti_main"] == "SMA" or kwargs["ti_main"] == "VWMA":
isma = True # 主圖技術指標為均線類型
for i in range(0, tidf.shape[1]):
aps.append(mpf.make_addplot(tidf.iloc[:, i], \
color = MA_Colors[i]))
if isma: # 補均線扣抵記號，配色同均線
aps.append(mpf.make_addplot(self.sig_main.iloc[:, i], \
type = "scatter", \
markersize = 200, \
marker = '^', \
color = MA_Colors[i]))
pid = 1 # Panel ID = 0 for candlestick, 1 for volume
if "ti_sub1" in kwargs: # pid = 2
pid += 1
ap2 = self._setsubti(self.val_sub1, kwargs["ti_sub1"], pid)
for item in ap2:
aps.append(item)
if "ti_sub2" in kwargs: # pid = 3 (or 2 if ti_sub1 is not assigned)
pid += 1
ap3 = self._setsubti(self.val_sub2, kwargs["ti_sub2"], pid)
for item in ap3:
aps.append(item)
if len(aps) > 0:
if pid == 2:
mfargs["panel_ratios"] = (2, 1, 1)
elif pid == 3:
mfargs["panel_ratios"] = (3, 1, 1, 1)
fig, axes = mpf.plot(self.df, addplot=aps, **mfargs)
else:
fig, axes = mpf.plot(self.df, **mfargs)
mpf.show()
fig.savefig(dest)
return None
async def candle(message: types.Message, regexp_command):
chat_id = message.chat.id
try:
coin = regexp_command.group(1)
trades = get_ohcl_trades(coin, 180)
trades = trades[-60:]
df = pd.DataFrame(
trades, columns='time open high low close volume amount'.split())
df['time'] = pd.DatetimeIndex(df['time'] * 10**9)
df.set_index('time', inplace=True)
df['MA20'] = df['close'].rolling(window=20).mean()
df['20dSTD'] = df['close'].rolling(window=20).std()
df['Upper'] = df['MA20'] + (df['20dSTD'] * 2)
df['Lower'] = df['MA20'] - (df['20dSTD'] * 2)
rsi_df = get_rsi_df(df)
df = df.tail(30)
rsi_df = rsi_df.tail(30)
apd = [
mpf.make_addplot(df['Lower'], color='#EC407A', width=0.9),
mpf.make_addplot(df['Upper'], color='#42A5F5', width=0.9),
mpf.make_addplot(df['MA20'], color='#FFEB3B', width=0.9)
]
if rsi_df is not None:
apd.append(
mpf.make_addplot(rsi_df,
color='#FFFFFF',
panel=1,
ylabel='RSI',
ylim=[0, 100]))
kwargs = dict(type='candle',
ylabel=coin.upper() + ' Price in $',
volume=True,
figratio=(3, 2),
figscale=1.5,
addplot=apd)
mpf.plot(df, **kwargs, style='nightclouds')
mc = mpf.make_marketcolors(up='#69F0AE', down='#FF5252', inherit=True)
s = mpf.make_mpf_style(base_mpf_style='nightclouds',
facecolor='#121212',
edgecolor="#131313",
gridcolor="#232323",
marketcolors=mc)
mpf.plot(df,
**kwargs,
style=s,
scale_width_adjustment=dict(volume=0.55, candle=0.8),
savefig=coin + '-mplfiance.png')
await bot.send_photo(chat_id=chat_id,
photo=InputFile(coin + '-mplfiance.png'))
except Exception as e:
logging.error("ERROR Making chart:" + str(e))
await bot.send_message(chat_id=chat_id,
text="Failed to create chart",
parse_mode="HTML")
def build_graph(data):
imgdata = BytesIO()
style = mpf.make_mpf_style(base_mpf_style='yahoo', rc={'font.size': 6})
mpf.plot(data, type='candle', style=style, figscale=0.5, savefig=imgdata)
imgdata.seek(0)
string = base64.b64encode(imgdata.read())
uri = urllib.parse.quote(string)
return uri
def plot(df: pd.DataFrame,
title: Optional[str] = None,
mav: Optional[Tuple[int]] = None,
alines: Optional[dict] = None):
color = mpf.make_marketcolors(up='red', down='cyan', inherit=True)
style = mpf.make_mpf_style(marketcolors=color)
# # 设置外观效果
# plt.rc('font', family='Microsoft YaHei') # 用中文字体，防止中文显示不出来
# plt.rc('figure', fc='k') # 绘图对象背景图
# plt.rc('text', c='#800000') # 文本颜色
# plt.rc('axes', axisbelow=True, xmargin=0, fc='k', ec='#800000', lw=1.5, labelcolor='#800000',
# unicode_minus=False) # 坐标轴属性(置底，左边无空隙，背景色，边框色，线宽，文本颜色，中文负号修正)
# plt.rc('xtick', c='#d43221') # x轴刻度文字颜色
# plt.rc('ytick', c='#d43221') # y轴刻度文字颜色
# plt.rc('grid', c='#800000', alpha=0.9, ls=':', lw=0.8) # 网格属性(颜色，透明值，线条样式，线宽)
# plt.rc('lines', lw=0.8) # 全局线宽
#
# # 创建绘图对象和4个坐标轴
# figure = plt.figure(figsize=(16, 8))
# left, width = 0.01, 0.98
# ax1 = figure.add_axes([left, 0.6, width, 0.35]) # left, bottom, width, height
# ax2 = figure.add_axes([left, 0.45, width, 0.15], sharex=ax1) # 共享ax1轴
# ax3 = figure.add_axes([left, 0.25, width, 0.2], sharex=ax1) # 共享ax1轴
# ax4 = figure.add_axes([left, 0.05, width, 0.2], sharex=ax1) # 共享ax1轴
# plt.setp(ax1.get_xticklabels(), visible=False) # 使x轴刻度文本不可见，因为共享，不需要显示
# plt.setp(ax2.get_xticklabels(), visible=False) # 使x轴刻度文本不可见，因为共享，不需要显示
# plt.setp(ax3.get_xticklabels(), visible=False) # 使x轴刻度文本不可见，因为共享，不需要显示
#
# # 绘制蜡烛图
# mpf.c(ax, data['open'], data['close'], data['high'], data['low'],
# width=0.5, colorup='r', colordown='green',
# alpha=0.6)
kwargs: dict = {
'type':
'candle',
'style':
style,
'volume':
True,
'figratio': (3, 1),
'figscale':
20,
'savefig':
dict(fname=
f'D:\\Test_{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.png',
bbox_inches='tight')
}
if title:
kwargs['title'] = title
if mav:
kwargs['mav'] = mav
if alines:
kwargs['alines'] = alines
mpf.plot(df, **kwargs)
mpf.show()
def mpf_Candlestick():
#INPUT
excel_file = "YOUR PATH/Data/Stock_YTS.xlsx"
#Stock INPUT
file = open('YOUR PATH/stocks/stocks.txt', 'r')
#Produce data visuals for desired stocks
for stock in file:
# Parse Stock Symbols
stock = stock.strip()
# Creating sql engine to use SQL/reading Excel sheets
engine = create_engine('sqlite://', echo=False)
df = pd.read_excel(excel_file, sheet_name=stock)
# Creating Tables to extract data
df.to_sql('Monthly', engine, if_exists='replace', index=False)
#Query1 - Filtering monthly stock data using SQL method
q1 = engine.execute("Select * FROM Monthly \
WHERE DATE LIKE '%2020%' \
OR DATE LIKE '%2019%' \
ORDER BY DATE ASC \
")
monthly_Stock = pd.DataFrame(q1, columns=df.columns)
''' Method 2 [OPTIONAL]:
#filter = df[(df.DATE >= '2020-01-31')]
#filter = filter.sort_values(by=['DATE']) '''
# Converting Date to datetime datatype and setting as index
monthly_Stock.DATE = pd.to_datetime(monthly_Stock.DATE)
monthly_Stock = monthly_Stock.set_index('DATE')
# Data Visual - Candlestick chart with custom parameters
mc = mpf.make_marketcolors(up='purple',
down='Black',
edge='black',
wick={
'up': 'blue',
'down': 'red'
},
volume='green')
s = mpf.make_mpf_style(marketcolors=mc,
gridstyle='--',
gridaxis='both',
mavcolors=['red'])
mpf.plot(monthly_Stock,
type='candle',
mav=(2),
figratio=(18, 10),
figscale=1.0,
volume=True,
tight_layout=True,
title='[' + stock + ']' + 'Monthly Time Series',
style=s,
xrotation=20,
savefig='YOUR PATH/Data_Visuals/candles/' + stock + '.png')
def __init__(self, parent=None, width=5, height=5, dpi=100):
idf = pd.read_csv('datas/SPY_20110701_20120630_Bollinger.csv',
index_col=0,
parse_dates=True)
idf.shape
idf.head(3)
idf.tail(3)
self.df = idf.loc['2011-07-01':'2011-12-30', :]
self.df = self.df.iloc[0:30]
self.exp12 = self.df['Close'].ewm(span=12, adjust=False).mean()
self.exp26 = self.df['Close'].ewm(span=26, adjust=False).mean()
self.macd = self.exp12 - self.exp26
self.signal = self.macd.ewm(span=9, adjust=False).mean()
self.histogram = self.macd - self.signal
self.apds = [
mpf.make_addplot(self.exp12, color='lime'),
mpf.make_addplot(self.exp26, color='c'),
mpf.make_addplot(self.histogram,
type='bar',
width=0.7,
panel=1,
color='dimgray',
alpha=1,
secondary_y=False),
mpf.make_addplot(self.macd,
panel=1,
color='fuchsia',
secondary_y=True),
mpf.make_addplot(self.signal, panel=1, color='b',
secondary_y=True),
]
s = mpf.make_mpf_style(base_mpf_style='starsandstripes',
rc={'figure.facecolor': 'lightgray'})
self.fig, self.axes = mpf.plot(self.df,
type='candle',
addplot=self.apds,
figscale=1.5,
figratio=(7, 5),
title='\n\nMACD',
style=s,
volume=True,
volume_panel=2,
panel_ratios=(6, 3, 2),
returnfig=True)
FigureCanvas.__init__(self, self.fig)
self.setParent(parent)
self.ax_main = self.axes[0]
self.ax_emav = self.ax_main
self.ax_hisg = self.axes[2]
self.ax_macd = self.axes[3]
self.ax_sign = self.ax_macd
self.ax_volu = self.axes[4]
self.df = idf.loc['2011-07-01':'2011-12-30', :]
self.ani = animation.FuncAnimation(self.fig,
self.animate,
interval=250)
def view_vwap(
s_ticker: str,
df_stock: pd.DataFrame,
offset: int = 0,
s_interval: str = "",
export: str = "",
):
"""Plots EMA technical indicator
Parameters
----------
s_ticker : str
Ticker
df_stock : pd.DataFrame
Dataframe of prices
offset : int
Offset variable
s_interval : str
Interval of data
export : str
Format to export data
"""
df_stock.index = df_stock.index.tz_localize(None)
df_stock["Day"] = [idx.date() for idx in df_stock.index]
day_df = df_stock[df_stock.Day == df_stock.Day[-1]]
df_vwap = overlap_model.vwap(day_df, offset)
mc = mpf.make_marketcolors(
up="green", down="red", edge="black", wick="black", volume="in", ohlc="i"
)
s = mpf.make_mpf_style(marketcolors=mc, gridstyle=":", y_on_right=True)
addplot_result = mpf.make_addplot(df_vwap, color="k")
mpf.plot(
day_df,
style=s,
type="candle",
addplot=addplot_result,
volume=True,
title=f"\n{s_ticker} {s_interval} VWAP",
)
if gtff.USE_ION:
plt.ion()
plt.show()
console.print("")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
"VWAP",
df_vwap,
)
def plot_image(df_):
my_color = mpf.make_marketcolors(up="red", down="green", edge="inherit", volume="inherit")
my_style = mpf.make_mpf_style(marketcolors=my_color)
add_plot = [mpf.make_addplot(df_[['ma_short', 'ma_long']]),
mpf.make_addplot(df_['signal_long'], scatter=True, marker="^", color="r"),
mpf.make_addplot(df_['signal_short'], scatter=True, marker="v", color="g")]
mpf.plot(df_, type="line", ylabel="price(usdt)", style=my_style, volume=True, ylabel_lower="vol",
addplot=add_plot)
