def animate(ival):
if (20 + ival) > len(df):
print('no more data to plot')
ani.event_source.interval *= 3
if ani.event_source.interval > 12000:
exit()
return
data = df.iloc[0:(30 + ival)]
exp12 = data['Close'].ewm(span=12, adjust=False).mean()
exp26 = data['Close'].ewm(span=26, adjust=False).mean()
macd = exp12 - exp26
signal = macd.ewm(span=9, adjust=False).mean()
histogram = macd - signal
apds = [
mpf.make_addplot(exp12, color='lime', ax=ax_emav),
mpf.make_addplot(exp26, color='c', ax=ax_emav),
mpf.make_addplot(histogram,
type='bar',
width=0.7,
color='dimgray',
alpha=1,
ax=ax_hisg),
mpf.make_addplot(macd, color='fuchsia', ax=ax_macd),
mpf.make_addplot(signal, color='b', ax=ax_sign),
]
for ax in axes:
ax.clear()
mpf.plot(data, type='candle', addplot=apds, ax=ax_main, volume=ax_volu)
def test_addplot03(bolldata):
df = bolldata
fname = base + '03.png'
tname = os.path.join(tdir, fname)
rname = os.path.join(refd, fname)
tcdf = df[['LowerB', 'UpperB']] # DataFrame with two columns
low_signal = percentB_belowzero(df['PercentB'], df['Close'])
high_signal = percentB_aboveone(df['PercentB'], df['Close'])
apds = [
mpf.make_addplot(tcdf),
mpf.make_addplot(low_signal, scatter=True, markersize=200, marker='^'),
mpf.make_addplot(high_signal, scatter=True, markersize=200,
marker='v'),
mpf.make_addplot((df['PercentB']), panel='lower', color='g')
]
mpf.plot(df, addplot=apds, figscale=1.3, volume=True, savefig=tname)
tsize = os.path.getsize(tname)
print(glob.glob(tname), '[', tsize, 'bytes', ']')
rsize = os.path.getsize(rname)
print(glob.glob(rname), '[', rsize, 'bytes', ']')
result = compare_images(rname, tname, tol=IMGCOMP_TOLERANCE)
if result is not None:
print('result=', result)
assert result is None
def animate(ival):
global df, rs
nxt = rtapi.fetch_next()
if nxt is None:
print('no more data to plot')
exit()
df = df.append(nxt)
rs = df.resample(resample_period).agg(resample_map).dropna()
if len(rs) > 90:
rs = rs[-90:]
data = rs
exp12 = data['Close'].ewm(span=12, adjust=False).mean()
exp26 = data['Close'].ewm(span=26, adjust=False).mean()
macd = exp12 - exp26
signal = macd.ewm(span=9, adjust=False).mean()
histogram = macd - signal
apds = [
mpf.make_addplot(exp12, color='lime', ax=ax_emav),
mpf.make_addplot(exp26, color='c', ax=ax_emav),
mpf.make_addplot(histogram,
type='bar',
width=0.7,
color='dimgray',
alpha=1,
ax=ax_hisg),
mpf.make_addplot(macd, color='fuchsia', ax=ax_macd),
mpf.make_addplot(signal, color='b', ax=ax_sign),
]
for ax in axes:
ax.clear()
mpf.plot(data, type='candle', addplot=apds, ax=ax_main, volume=ax_volu)
def draw_MACD(table):
global panelCount
panelCount = panelCount + 1
macd, macdsignal, macdhist = talib.MACD(table['Close'])
PICS.append(mpf.make_addplot(macd,panel = panelCount,ylabel = 'MACD',color='blue'))
PICS.append(mpf.make_addplot(macdsignal,panel = panelCount,color='red'))
PICS.append(mpf.make_addplot(macdhist,type = 'bar',panel = panelCount))
def generate_macd_reports(ticker_df, ticker_symbol, reports_path):
output_dir = f'{reports_path}{ticker_symbol.lower()}/'
if not os.path.exists(os.path.dirname(output_dir)):
try:
os.makedirs(os.path.dirname(output_dir))
except OSError as exc: # Guard against race condition
if exc.errno != errno.EEXIST:
raise
macd_obj = ta.trend.MACD(close=ticker_df['Close'])
macd = macd_obj.macd()
macd_signal = macd_obj.macd_signal()
macd_plot = mpl.make_addplot(macd, panel='lower')
macd_signal_plot = mpl.make_addplot(macd_signal, panel='lower')
mpl.plot(ticker_df,
type='candle',
volume=True,
style='yahoo',
addplot=[macd_plot, macd_signal_plot],
savefig=f'{output_dir}Daily_MACD.svg',
figscale=1.2,
closefig=True)
def control_test():
x = get_candles("NOG", 10000, 0)
x_ = to_dataframe(x)
rsi_ = rsi(x)
control, generated = get_control(x)
# NOG first three samples
session = [0] * len(x)
for i in range(14,68):
session[i] = 50
session[14] = 25
session[60] = 75
for i in range(250,312):
session[i] = 50
session[255] = 25
session[301] = 75
for i in range(373,404):
session[i] = 50
session[376] = 25
session[398] = 75
apds = [
make_addplot(rsi_, panel = 1),
make_addplot([70] * len(x), panel = 1),
make_addplot([30] * len(x), panel = 1),
make_addplot(session, panel = 2)
#make_addplot([ r["close"] for r in generated ], panel = 2),
#make_addplot(control, panel = 3),
#make_addplot([70] * len(x), panel = 3),
#make_addplot([30] * len(x), panel = 3)
]
plot(x_, type = "candle", addplot = apds)
def test_addplot08(bolldata):
df = bolldata
fname = base+'08.png'
tname = os.path.join(tdir,fname)
rname = os.path.join(refd,fname)
tcdf = df[['LowerB','UpperB']] # DataFrame with two columns
low_signal = percentB_belowzero(df['PercentB'], df['Close'])
high_signal = percentB_aboveone(df['PercentB'], df['Close'])
import math
new_low_signal = [x*20.*math.sin(x) for x in low_signal]
apds = [ mpf.make_addplot(tcdf,linestyle='dashdot'),
mpf.make_addplot(new_low_signal,scatter=True,markersize=200,marker='^'),
mpf.make_addplot(high_signal,scatter=True,markersize=200,marker='v'),
mpf.make_addplot((df['PercentB']),panel='lower',color='g',linestyle='dotted')
]
mpf.plot(df,addplot=apds,figscale=1.5,volume=True,
mav=(15,30,45),style='default',savefig=tname)
tsize = os.path.getsize(tname)
print(glob.glob(tname),'[',tsize,'bytes',']')
rsize = os.path.getsize(rname)
print(glob.glob(rname),'[',rsize,'bytes',']')
result = compare_images(rname,tname,tol=IMGCOMP_TOLERANCE)
if result is not None:
print('result=',result)
assert result is None
def draw_ADLs(table):
global panelCount
panelCount = panelCount + 1
table_fast = tools.smooth_Data(table,10)
table_slow = tools.smooth_Data(table,30)
PICS.append(mpf.make_addplot(table_fast,panel = panelCount,color='red'))
PICS.append(mpf.make_addplot(table_slow,panel = panelCount,color='blue',ylabel = "ADLs"))
def show_stock_chart2(df, title='', figpath=None):
# 日付で昇順ソース
df_sort = df.sort_index()
# MACDを追加
df_macd = add_macd(df_sort)
print('==========')
print(df_macd)
# MACDとシグナルのプロット作成
add_plot = [
mpf.make_addplot(df_macd['MACD'],
color='m',
panel=1,
secondary_y=False),
mpf.make_addplot(df_macd['Signal'],
color='c',
panel=1,
secondary_y=False),
mpf.make_addplot(df_macd['Hist'],
type='bar',
color='g',
panel=1,
secondary_y=True)
]
# ローソク足チャートを表示
mpf.plot(df_sort,
type='candle',
mav=(5, 25, 75),
volume=True,
addplot=add_plot,
volume_panel=2,
savefig=figpath)
def generate_on_balance_volume_reports(ticker_df, ticker_symbol, reports_path):
output_dir = f'{reports_path}{ticker_symbol.lower()}/'
if not os.path.exists(os.path.dirname(output_dir)):
try:
os.makedirs(os.path.dirname(output_dir))
except OSError as exc: # Guard against race condition
if exc.errno != errno.EEXIST:
raise
on_balance_volume = ta.volume.OnBalanceVolumeIndicator(
close=ticker_df['Close'],
volume=ticker_df['Volume']).on_balance_volume()
obv_sma = ta.trend.SMAIndicator(close=on_balance_volume,
n=20).sma_indicator()
obv_plot = mpl.make_addplot(on_balance_volume)
obv_sma_plot = mpl.make_addplot(obv_sma)
mpl.plot(ticker_df,
type='candle',
volume=True,
style='yahoo',
addplot=[obv_plot, obv_sma_plot],
savefig=f'{output_dir}On_Balance_Volume.svg',
figscale=1.2,
closefig=True)
def plotResults(data, addings, adding_types, addings_colors, sizes,
within_data, legend, names):
if len(addings) == len(adding_types) == len(addings_colors):
apdict = []
#if within_data is True, then the addings will be a list of column names to add to the plot
if within_data:
for i in range(len(addings)):
apdict.append(
mpf.make_addplot(data[addings[i]],
type=adding_types[i],
color=addings_colors[i],
markersize=sizes[i]))
#if within_data is False, then the addings will be a list of dataframes to add to the plot
else:
for i in range(len(addings)):
apdict.append(
mpf.make_addplot(addings[i],
type=adding_types[i],
color=addings_colors[i],
markersize=sizes[i]))
if legend:
fig, ax = plt.subplots(1)
fig, ax = mpf.plot(data,
type='candle',
block=False,
addplot=apdict,
returnfig=True)
ax.legend(names)
fig.show()
#return ax
else:
mpf.plot(data, type='candle', block=False, addplot=apdict)
def plot_stock_sig_back(pddatas, datal, sigs):
"datas标准数据，sigs(trade_sig,up_buy,up_sell,down_buy,down_sell)"
# https://blog.csdn.net/weixin_42524945/article/details/112187912
# https://blog.csdn.net/xingbuxing_py/article/details/109379080
# 1. 添加额外线
add_plot = mpf.make_addplot(datal[['High', 'MidValue', 'Low']])
mpf.plot(pddatas, type='candle', addplot=add_plot)
# 2. 添加额外点
a_list = datal.High.tolist()
b_list = datal.Low.tolist()
c_list = datal.Low.tolist()
add_plot = [
mpf.make_addplot(datal['MidValue']),
mpf.make_addplot(a_list,
scatter=True,
markersize=100,
marker='v',
color='g'),
mpf.make_addplot(b_list,
scatter=True,
markersize=100,
marker='^',
color='r'),
mpf.make_addplot(c_list,
scatter=True,
markersize=100,
marker='o',
color='y'),
]
mpf.plot(pddatas, type='candle', addplot=add_plot)
def animate(self, ival):
if (20 + ival) > len(self.df):
print('no more data to plot')
return
data = self.df.iloc[0:(30 + ival)]
self.exp12 = data['Close'].ewm(span=12, adjust=False).mean()
self.exp26 = data['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', ax=self.ax_emav),
mpf.make_addplot(self.exp26, color='c', ax=self.ax_emav),
mpf.make_addplot(self.histogram,
type='bar',
width=0.7,
color='dimgray',
alpha=1,
ax=self.ax_hisg),
mpf.make_addplot(self.macd, color='fuchsia', ax=self.ax_macd),
mpf.make_addplot(self.signal, color='b', ax=self.ax_sign),
]
for ax in self.axes:
ax.clear()
mpf.plot(data,
type='candle',
addplot=self.apds,
ax=self.ax_main,
volume=self.ax_volu)
def plot_j2(pdata, sma9, sma20, lowerbb, upperbb, numofdays, ticker,
defining_ma):
sma9dict = mpf.make_addplot(sma9['data'][-numofdays:],
color='#c87cff',
width=1)
sma20dict = mpf.make_addplot(sma20['data'][-numofdays:],
color='#f28c06',
width=1)
lowerbbdict = mpf.make_addplot(lowerbb['data'][-numofdays:],
color='#b90c0c',
width=1)
upperbbdict = mpf.make_addplot(upperbb['data'][-numofdays:],
color='#b90c0c',
width=1)
mpf.plot(
pdata[-numofdays:],
type='candle',
style='charles',
addplot=[sma9dict, sma20dict, lowerbbdict, upperbbdict],
# figscale=.9,
tight_layout=False,
ylabel='',
savefig=
f'''/Users/mike/Desktop/ib_insync-MovingAverageChartGen/9-200SMA_{today_date}/{ticker}_{defining_ma}_{today_date}.pdf'''
)
chartFilePath = f'''/Users/mike/Desktop/ib_insync-MovingAverageChartGen/9-200SMA_{today_date}/{ticker}_{defining_ma}_{today_date}.pdf'''
chartFileTitle = f'''{ticker}_{defining_ma}_{today_date}.pdf'''
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 draw_KD(table,stockInfo):#table = 表 stockInfo = 股票資訊結構
table['k'],table['d'] = talib.STOCH(table['High'],table['Low'],table['Close'])
table['k'].fillna(value=0,inplace = True)
table['d'].fillna(value=0,inplace = True)
global panelCount
panelCount = panelCount + 1
PICS.append(mpf.make_addplot(table['k'],panel = panelCount,ylabel = "KD",color='red'))
PICS.append(mpf.make_addplot(table['d'],panel = panelCount,color='blue'))
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 ChartOrder_MA(KBar,TR):
# 將K線轉為DataFrame
Kbar_df=KbarToDf(KBar)
# 定義指標副圖
addp=[]
addp.append(mpf.make_addplot(Kbar_df['MA_long'],color='red'))
addp.append(mpf.make_addplot(Kbar_df['MA_short'],color='yellow'))
# 繪製指標、下單圖
ChartOrder(KBar,TR,addp)
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)
def plot_signal(df, signal, position=False, lines=False):
name = (df.reset_index())['symbol'].iloc[0]
buys = df[df[signal] > 0].index
sells = df[df[signal] < 0].index
fechas = []
colores = []
Trades = []
for trade in buys:
fechas.append(trade)
colores.append('g')
for trade in sells:
fechas.append(trade)
colores.append('r')
df_trades = pd.DataFrame({'colors': colores}, index=fechas)
df = df.join(df_trades)
df['buy_price'] = np.where(df['colors'] == 'g', df['Close'], math.nan)
df['sell_price'] = np.where(df['colors'] == 'r', df['Close'], math.nan)
if position:
lines = True
if lines:
marker = 0.1
if position:
width = 0.3
alpha = 0.1
else:
width = 0.5
alpha = 0.7
else:
width = 0.01
alpha = 0.01
marker = 70
if df['buy_price'].notnull().sum().sum() > 0:
trades_buy = mpf.make_addplot(df['buy_price'],
type='scatter',
markersize=marker,
marker='^',
color='g')
Trades.append(trades_buy)
if df['sell_price'].notnull().sum().sum() > 0:
trades_sell = mpf.make_addplot(df['sell_price'],
type='scatter',
markersize=marker,
marker='v',
color='r')
Trades.append(trades_sell)
mpf.plot(df,
type='candle',
style='charles',
figratio=(16, 8),
addplot=Trades,
vlines=dict(vlines=fechas,
colors=colores,
linewidths=width,
alpha=alpha),
title=name + ' daily chart' + ' signal:' + signal,
ylabel='price')
def send_monthly_data():
''' Send the long term data as a nice chart :s '''
econ_data = pd.read_sql(f'''SELECT * FROM forecast''', ECON_CON)
econ_data.datetime = pd.to_datetime(econ_data.datetime)
econ_data = econ_data.set_index('datetime', drop=True).replace('', np.nan)
# I need to get the sum of the most recent values from each event. I can
# filter for where long_term.notna(), but my rolling window is unknown
# because not all currencies have the same number of long term events.
data = {}
for ccy in econ_data.ccy.unique():
temp = econ_data[
(econ_data.ccy == ccy)
&
(econ_data.long_term.notna())
]
# Get the window sized based on number of unique values found
roll_window = len(temp.ccy_event.unique())
final_data = temp.long_term.rolling(roll_window).sum() / roll_window
# The data needs to get aligned to whatever timeframe of ohlc it will be plotted on
# Since there is sometimes multiple releases on a given day, group by day
final_data.index = final_data.index.date
data[ccy] = final_data.groupby([final_data.index]).sum()
# data[ccy].index = pd.to_datetime(data[ccy].index)
# data[ccy] = data[ccy].resample('1W')
# my daily candles from IC Markets have the hours at 16:00 so add that lest you want funkydata
# These long term data items were really only applicable to the currencies
# with lots of unique events (E,U,G).
timeframe = 'D1'
symbols = ['EURUSD', 'GBPUSD', 'EURGBP', 'USDCHF', 'NZDUSD', 'USDCAD', 'USDJPY']
for symbol in symbols:
df = mt5_ohlc_request(symbol, timeframe, num_candles=270)
df.index = df.index.date
df['long_term1'] = data[symbol[:3]]
df['long_term2'] = data[symbol[3:]]
df.long_term1 = df.long_term1.fillna(method='ffill')
df.long_term2 = df.long_term2.fillna(method='ffill')
df.index = pd.to_datetime(df.index)
plots = []
plots.append(mpf.make_addplot(df.long_term1, color='r', panel=1, width=2, secondary_y=True))
plots.append(mpf.make_addplot(df.long_term2, color='b', panel=1, width=1, secondary_y=True))
mpf.plot(df, type='candle',
tight_layout=True,
addplot=plots,
show_nontrading=False,
volume=True,
title=f'{symbol} {timeframe}',
# style='classic', ## black n white
savefig=f'{symbol}_longterm.png'
)
def showCompare(self, bars: [], code: str):
from earnmi.data.MarketImpl import MarketImpl
market = MarketImpl()
market.addNotice(code)
today: datetime = bars[-1].datetime
market.setToday(today + timedelta(days=1))
start: datetime = bars[0].datetime
baseBars = market.getHistory().getKbarFrom(
code, datetime(start.year, start.month, start.day))
#baseBar = mainBar
### 初始化columns
columns = ['Open', 'High', 'Low', 'Close', "Volume", "Close2"]
data = []
index = []
bars1 = baseBars
bars2 = bars
size1 = len(bars1)
size2 = len(bars2)
rate = bars1[0].close_price / bars2[0].close_price
bar_pre_2 = bars2[0]
i2 = 0
for i1 in range(size1):
bar1 = bars1[i1]
index.append(bar1.datetime)
list = [
bar1.open_price, bar1.high_price, bar1.low_price,
bar1.close_price, bar1.volume
]
if i2 < size2:
bar2 = bars2[i2]
if utils.is_same_day(bar1.datetime, bar2.datetime):
list.append(bar2.close_price * rate)
bar_pre_2 = bar2
i2 = i2 + 1
else:
##bar2缺少今天数据。
list.append(bar_pre_2.close_price * rate)
##保证i2要大于大
while (i2 < size2):
bar2 = bars2[i2]
days = (bar2.datetime - bar1.datetime).days
if days > 0:
break
i2 = i2 + 1
else:
list.append(bar_pre_2.close_price * rate)
data.append(list)
trades = pd.DataFrame(data, index=index, columns=columns)
apds = []
apds.append(mpf.make_addplot(trades['Close'], color='gray'))
apds.append(mpf.make_addplot(trades['Close2'], color='r'))
mpf.plot(trades, type='line', figscale=1.3, addplot=apds)
pass
def ChartOrder_BBANDS(KBar,TR):
# 將K線轉為DataFrame
Kbar_df=KbarToDf(KBar)
# 將副圖繪製出來
addp=[]
addp.append(mpf.make_addplot(Kbar_df['Upper'],color='yellow'))
addp.append(mpf.make_addplot(Kbar_df['Middle'],color='grey'))
addp.append(mpf.make_addplot(Kbar_df['Lower'],color='yellow'))
# 開始繪圖
ChartOrder(KBar,TR,addp,True)
def ChartOrder_RSI_2(KBar,TR):
# 將K線轉為DataFrame
Kbar_df=KbarToDf(KBar)
# 將副圖繪製出來
addp=[]
addp.append(mpf.make_addplot(Kbar_df['RSI'],panel='lower',color='black',secondary_y=False))
addp.append(mpf.make_addplot(Kbar_df['Ceil'],panel='lower',color='red',secondary_y=False))
addp.append(mpf.make_addplot(Kbar_df['Floor'],panel='lower',color='red',secondary_y=False))
# 開始繪圖
ChartOrder(KBar,TR,addp,False)
def ChartOrder_RSI_1(KBar,TR):
# 將K線轉為DataFrame
Kbar_df=KbarToDf(KBar)
# 將副圖繪製出來
addp=[]
addp.append(mpf.make_addplot(Kbar_df['RSI_long'],panel='lower',color='red',secondary_y=False))
addp.append(mpf.make_addplot(Kbar_df['RSI_short'],panel='lower',color='green',secondary_y=False))
addp.append(mpf.make_addplot(Kbar_df['Middle'],panel='lower',color='black',secondary_y=False))
# 開始繪圖
ChartOrder(KBar,TR,addp,False)
def create_addplots(df, mpl):
"""
"""
adps, hlines = [], {
'hlines': [],
'colors': [],
'linestyle': '--',
'linewidths': 0.75
}
# Add technical feature data (indicator values, etc).
for col in list(df):
if (col != "Open" and col != "High" and col != "Low" and col != "Close"
and col != "Volume"):
adps.append(mpl.make_addplot(df[col]))
# Add entry marker
color = 'limegreen' if trade['direction'] == "LONG" else 'crimson'
adps.append(
mpl.make_addplot(entry_marker,
type='scatter',
markersize=200,
marker='.',
color=color))
# Plotting Stop and TP levels cause incorrect scaling when stop/TP are
# far away from entry. Fix later. Not urgent or required
# # Add stop and TP levels.
# o_ids = [i for i in trade['orders'].keys()]
# for o_id in o_ids:
# if trade['orders'][o_id]['metatype'] == "STOP":
# hlines['hlines'].append(trade['orders'][o_id]['price'])
# hlines['colors'].append('crimson')
# elif trade['orders'][o_id]['metatype'] == "TAKE_PROFIT":
# hlines['hlines'].append(trade['orders'][o_id]['price'])
# hlines['colors'].append('limegreen')
# elif trade['orders'][o_id]['metatype'] == "FINAL_TAKE_PROFIT":
# hlines['hlines'].append(trade['orders'][o_id]['price'])
# hlines['colors'].append('limegreen')
# # Add an invisible hline to re-scale, in case stop/TP is far away.
# difference = max([abs(trade['entry_price'] - i) for i in hlines['hlines']])
# if max(hlines['hlines']) > difference:
# hlines['hlines'].append(trade['entry_price'] - difference)
# hlines['colors'].append('white')
# elif max(hlines['hlines']) < differe7nce:
# hlines['hlines'].append(trade['entry_price'] + difference)
# hlines['colors'].append('white')
return adps, hlines
def ChartKBar_MA(KBar,longPeriod=20,shortPeriod=5):
# 計算移動平均線(長短線)
KBar['MA_long']=SMA(KBar,timeperiod=longPeriod)
KBar['MA_short']=SMA(KBar,timeperiod=shortPeriod)
# 將K線轉為DataFrame
Kbar_df=KbarToDf(KBar)
# 將副圖繪製出來
addp=[]
addp.append(mpf.make_addplot(Kbar_df['MA_long'],color='red'))
addp.append(mpf.make_addplot(Kbar_df['MA_short'],color='yellow'))
# 開始繪圖
ChartKBar(KBar,addp,True)
def ChartKBar_BBANDS(KBar,BBANDSPeriod):
# 計算布琳通道
KBar['Upper'],KBar['Middle'],KBar['Lower']=BBANDS(KBar,timeperiod=BBANDSPeriod)
# 將K線轉為DataFrame
Kbar_df=KbarToDf(KBar)
# 將副圖繪製出來
addp=[]
addp.append(mpf.make_addplot(Kbar_df['Upper'],color='yellow'))
addp.append(mpf.make_addplot(Kbar_df['Middle'],color='grey'))
addp.append(mpf.make_addplot(Kbar_df['Lower'],color='yellow'))
# 開始繪圖
ChartKBar(KBar,addp,True)
def create_summary(self):
"""Creates a csv file and image showing all positions taken."""
# csv file
summary = pd.DataFrame.from_dict(self.equity)
summary = summary.set_index('datestamp')
summary['Buy Orders'] = np.nan
summary['Sell Orders'] = np.nan
for trade in self.all_positions:
open_time = dt.datetime.strptime(trade['Open Time'],
'%d-%m-%Y %H:%M:%S')
close_time = dt.datetime.strptime(trade['Close Time'],
'%d-%m-%Y %H:%M:%S')
if trade['Direction'] == 'BUY':
summary.loc[open_time, 'Buy Orders'] = trade['Open Price']
summary.loc[close_time, 'Sell Orders'] = trade['Close Price']
else:
summary.loc[open_time, 'Sell Orders'] = trade['Open Price']
summary.loc[close_time, 'Buy Orders'] = trade['Close Price']
curr_dir = os.path.dirname(os.path.realpath(__file__))
save_path = os.path.join(curr_dir, 'summary')
summary.to_csv(f'{save_path}/summary.csv')
# image
data = self.data.symbol_data
plots = [
mpf.make_addplot(summary['Buy Orders'],
type='scatter',
color='green',
marker='^',
markersize=40),
mpf.make_addplot(summary['Sell Orders'],
type='scatter',
color='red',
marker='v',
markersize=40),
mpf.make_addplot(summary['balance'], panel=1, color='fuchsia')
]
mpf.plot(data,
type='candlestick',
figratio=(18, 10),
panel_ratios=(8, 2),
addplot=plots,
style='sas',
savefig=f'{save_path}/summary.png',
tight_layout=True)