我如何制作 1 个由不同数据框组成的图表，我也需要帮助来添加正确的按钮？
how do i make 1 plotly chart consisting of different dataframes, I need help to add the right buttons as well?
以下是起始代码、数据帧和 TA 指标。使用 plotly 制作我所有的图表。数据帧是 'df' 和 'd15'。我还有其他人，但会保持简单的帮助。
import yfinance as yf
import plotly.graph_objs as go
#Importing my data
df = yf.download(tickers='EURUSD=X', period='1d', interval='5m')
d15 = yf.download(tickers='EURUSD=X', period='3d',interval='15m')
def Supertrend(df, atr_period, multiplier):
high = df['High']
low = df['Low']
close = df['Close']
# calculate ATR
price_diffs = [high - low,
high - close.shift(),
close.shift() - low]
true_range = pd.concat(price_diffs, axis=1)
true_range = true_range.abs().max(axis=1)
# default ATR calculation in supertrend indicator
atr = true_range.ewm(alpha=1/atr_period,min_periods=atr_period).mean()
# df['atr'] = df['tr'].rolling(atr_period).mean()
# HL2 is simply the average of high and low prices
hl2 = (high + low) / 2
# upperband and lowerband calculation
# notice that final bands are set to be equal to the respective bands
final_upperband = upperband = hl2 + (multiplier * atr)
final_lowerband = lowerband = hl2 - (multiplier * atr)
# initialize Supertrend column to True
supertrend = [True] * len(df)
for i in range(1, len(df.index)):
curr, prev = i, i-1
# if current close price crosses above upperband
if close[curr] > final_upperband[prev]:
supertrend[curr] = True
# if current close price crosses below lowerband
elif close[curr] < final_lowerband[prev]:
supertrend[curr] = False
# else, the trend continues
else:
supertrend[curr] = supertrend[prev]
# adjustment to the final bands
if supertrend[curr] == True and final_lowerband[curr] < final_lowerband[prev]:
final_lowerband[curr] = final_lowerband[prev]
if supertrend[curr] == False and final_upperband[curr] > final_upperband[prev]:
final_upperband[curr] = final_upperband[prev]
# to remove bands according to the trend direction
if supertrend[curr] == True:
final_upperband[curr] = np.nan
else:
final_lowerband[curr] = np.nan
return pd.DataFrame({
'Supertrend': supertrend,
'Final Lowerband': final_lowerband,
'Final Upperband': final_upperband
}, index=df.index)
atr_period = 10
atr_multiplier = 6.0
df = yf.download(tickers='EURUSD=X', period='1d', interval='5m')
supertrend = Supertrend(df, atr_period, atr_multiplier)
df = df.join(supertrend)
#15 Minute Indicator
def Supertrend(df, atr_period, multiplier):
high = df['High']
low = df['Low']
close = df['Close']
# calculate ATR
price_diffs = [high - low,
high - close.shift(),
close.shift() - low]
true_range = pd.concat(price_diffs, axis=1)
true_range = true_range.abs().max(axis=1)
# default ATR calculation in supertrend indicator
atr = true_range.ewm(alpha=1/atr_period,min_periods=atr_period).mean()
# df['atr'] = df['tr'].rolling(atr_period).mean()
# HL2 is simply the average of high and low prices
hl2 = (high + low) / 2
# upperband and lowerband calculation
# notice that final bands are set to be equal to the respective bands
final_upperband = upperband = hl2 + (multiplier * atr)
final_lowerband = lowerband = hl2 - (multiplier * atr)
# initialize Supertrend column to True
supertrend = [True] * len(df)
for i in range(1, len(df.index)):
curr, prev = i, i-1
# if current close price crosses above upperband
if close[curr] > final_upperband[prev]:
supertrend[curr] = True
# if current close price crosses below lowerband
elif close[curr] < final_lowerband[prev]:
supertrend[curr] = False
# else, the trend continues
else:
supertrend[curr] = supertrend[prev]
# adjustment to the final bands
if supertrend[curr] == True and final_lowerband[curr] < final_lowerband[prev]:
final_lowerband[curr] = final_lowerband[prev]
if supertrend[curr] == False and final_upperband[curr] > final_upperband[prev]:
final_upperband[curr] = final_upperband[prev]
# to remove bands according to the trend direction
if supertrend[curr] == True:
final_upperband[curr] = np.nan
else:
final_lowerband[curr] = np.nan
return pd.DataFrame({
'Supertrend': supertrend,
'Final Lowerband': final_lowerband,
'Final Upperband': final_upperband
}, index=df.index)
atr_period = 10
atr_multiplier = 6.0
df = yf.download(tickers='EURUSD=X', period='1d', interval='5m')
supertrend = Supertrend(df, atr_period, atr_multiplier)
df = df.join(supertrend)
下一部分是情节，我认为这是我需要帮助的地方。我需要添加 2 个按钮并将这 2 个图表相互添加？
按钮 1：5m（显示 05m TF 图）
按钮 2：15m（显示 15m TF 图）
#5 Minute TF plot
fig = go.Figure()
fig.add_trace(go.Candlestick(x=df.index,
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'],
increasing_line_color= '#04b29b',
decreasing_line_color= '#ff2d5d',
increasing_fillcolor = '#04b29b',
decreasing_fillcolor = '#ff2d5d',
name='EURUSD'
))
fig.add_trace(go.Scatter(x=df.index,
y=df['Final Lowerband'],
mode='lines',
line=dict(color='#04b29b'),
name='Bullish'
))
fig.add_trace(go.Scatter(x=df.index,
y=df['Final Upperband'],
mode='lines',
line=dict(color='#ff2d5d'),
name='Bearish'
))
fig.update_layout(xaxis_rangeslider_visible=False,
plot_bgcolor = 'black', showlegend = False,
margin = dict(l=10, r=10,t=10,b=10),
paper_bgcolor='black',
xaxis=dict(showgrid=False, zerolinecolor = 'white',
color='white'),
yaxis=dict(showticklabels=False, showgrid=False))
fig.update_xaxes(
rangebreaks=[
dict(bounds=["sat", "mon"]), #hide weekends
dict(values=["2015-12-25", "2016-01-01"]) # hide Christmas
and New Year's
]
)
fig.show()
这是 15 分钟 tf
15 Minute TF Plot
fig15 = go.Figure()
fig15.add_trace(go.Candlestick(x=d15.index,
open=d15['Open'],
high=d15['High'],
low=d15['Low'],
close=d15['Close'],
increasing_line_color= '#04b29b',
decreasing_line_color= '#ff2d5d',
increasing_fillcolor = '#04b29b',
decreasing_fillcolor = '#ff2d5d',
name='EURUSD'
))
fig15.add_trace(go.Scatter(x=d15.index,
y=d15['Final Lowerband'],
mode='lines',
line=dict(color='#04b29b'),
name='Bullish'
))
fig15.add_trace(go.Scatter(x=d15.index,
y=d15['Final Upperband'],
mode='lines',
line=dict(color='#ff2d5d'),
name='Bearish'
))
fig15.update_layout(xaxis_rangeslider_visible=False,
plot_bgcolor = 'black', showlegend = False,
margin = dict(l=10, r=10,t=10,b=10),
paper_bgcolor='black',
xaxis=dict(showgrid=False, zerolinecolor = 'white',
color='white'),
yaxis=dict(showticklabels=False, showgrid=False))
fig15.update_xaxes(
rangebreaks=[
dict(bounds=["sat", "mon"]), #hide weekends
dict(values=["2015-12-25", "2016-01-01"]) # hide Christmas
and New Year's
]
)
fig15.show()
如果你想快速得到答案，如果你把更简化的代码放在上面，你会得到更快的答案。或者，如果您使用示例数据和图形代码创建可重现的情况，您将更有可能获得答案。要回答如何使每个图形成为按钮的主要问题，您可以使用官方参考中的股价数据，绘制三只不同的股票，并为每个设置 show/hide 控件以显示 例如，如果您只有 AAPL，将其他设置为 False 以隐藏它们。那只是唯一的设置。
import plotly.graph_objects as go
import plotly.express as px
df = px.data.stocks()
fig = go.Figure()
fig.add_trace(go.Scatter(x=df['date'], y=df['AAPL'], name='AAPL'))
fig.add_trace(go.Scatter(x=df['date'], y=df['GOOG'], name='GOOG'))
fig.add_trace(go.Scatter(x=df['date'], y=df['AMZN'], name='AMZN'))
fig.update_layout(
updatemenus=[
dict(
type="buttons",
direction="right",
active=0,
x=0.25,
y=1.2,
buttons=list([
dict(label="All",
method="update",
args=[{"visible": [True, True, True]},
{"title": "All"}
]),
dict(label="AAPL",
method="update",
args=[{"visible": [True, False, False]},
{"title": "AAPL"}
]),
dict(label="GOOG",
method="update",
args=[{"visible": [False, True, False]},
{"title": "GOOG"}
]),
dict(label="AMZN",
method="update",
args=[{"visible": [False, False, True]},
{"title": "AMZN"}
]),
]),
)
])
fig.show()
以下是起始代码、数据帧和 TA 指标。使用 plotly 制作我所有的图表。数据帧是 'df' 和 'd15'。我还有其他人，但会保持简单的帮助。
import yfinance as yf
import plotly.graph_objs as go
#Importing my data
df = yf.download(tickers='EURUSD=X', period='1d', interval='5m')
d15 = yf.download(tickers='EURUSD=X', period='3d',interval='15m')
def Supertrend(df, atr_period, multiplier):
high = df['High']
low = df['Low']
close = df['Close']
# calculate ATR
price_diffs = [high - low,
high - close.shift(),
close.shift() - low]
true_range = pd.concat(price_diffs, axis=1)
true_range = true_range.abs().max(axis=1)
# default ATR calculation in supertrend indicator
atr = true_range.ewm(alpha=1/atr_period,min_periods=atr_period).mean()
# df['atr'] = df['tr'].rolling(atr_period).mean()
# HL2 is simply the average of high and low prices
hl2 = (high + low) / 2
# upperband and lowerband calculation
# notice that final bands are set to be equal to the respective bands
final_upperband = upperband = hl2 + (multiplier * atr)
final_lowerband = lowerband = hl2 - (multiplier * atr)
# initialize Supertrend column to True
supertrend = [True] * len(df)
for i in range(1, len(df.index)):
curr, prev = i, i-1
# if current close price crosses above upperband
if close[curr] > final_upperband[prev]:
supertrend[curr] = True
# if current close price crosses below lowerband
elif close[curr] < final_lowerband[prev]:
supertrend[curr] = False
# else, the trend continues
else:
supertrend[curr] = supertrend[prev]
# adjustment to the final bands
if supertrend[curr] == True and final_lowerband[curr] < final_lowerband[prev]:
final_lowerband[curr] = final_lowerband[prev]
if supertrend[curr] == False and final_upperband[curr] > final_upperband[prev]:
final_upperband[curr] = final_upperband[prev]
# to remove bands according to the trend direction
if supertrend[curr] == True:
final_upperband[curr] = np.nan
else:
final_lowerband[curr] = np.nan
return pd.DataFrame({
'Supertrend': supertrend,
'Final Lowerband': final_lowerband,
'Final Upperband': final_upperband
}, index=df.index)
atr_period = 10
atr_multiplier = 6.0
df = yf.download(tickers='EURUSD=X', period='1d', interval='5m')
supertrend = Supertrend(df, atr_period, atr_multiplier)
df = df.join(supertrend)
#15 Minute Indicator
def Supertrend(df, atr_period, multiplier):
high = df['High']
low = df['Low']
close = df['Close']
# calculate ATR
price_diffs = [high - low,
high - close.shift(),
close.shift() - low]
true_range = pd.concat(price_diffs, axis=1)
true_range = true_range.abs().max(axis=1)
# default ATR calculation in supertrend indicator
atr = true_range.ewm(alpha=1/atr_period,min_periods=atr_period).mean()
# df['atr'] = df['tr'].rolling(atr_period).mean()
# HL2 is simply the average of high and low prices
hl2 = (high + low) / 2
# upperband and lowerband calculation
# notice that final bands are set to be equal to the respective bands
final_upperband = upperband = hl2 + (multiplier * atr)
final_lowerband = lowerband = hl2 - (multiplier * atr)
# initialize Supertrend column to True
supertrend = [True] * len(df)
for i in range(1, len(df.index)):
curr, prev = i, i-1
# if current close price crosses above upperband
if close[curr] > final_upperband[prev]:
supertrend[curr] = True
# if current close price crosses below lowerband
elif close[curr] < final_lowerband[prev]:
supertrend[curr] = False
# else, the trend continues
else:
supertrend[curr] = supertrend[prev]
# adjustment to the final bands
if supertrend[curr] == True and final_lowerband[curr] < final_lowerband[prev]:
final_lowerband[curr] = final_lowerband[prev]
if supertrend[curr] == False and final_upperband[curr] > final_upperband[prev]:
final_upperband[curr] = final_upperband[prev]
# to remove bands according to the trend direction
if supertrend[curr] == True:
final_upperband[curr] = np.nan
else:
final_lowerband[curr] = np.nan
return pd.DataFrame({
'Supertrend': supertrend,
'Final Lowerband': final_lowerband,
'Final Upperband': final_upperband
}, index=df.index)
atr_period = 10
atr_multiplier = 6.0
df = yf.download(tickers='EURUSD=X', period='1d', interval='5m')
supertrend = Supertrend(df, atr_period, atr_multiplier)
df = df.join(supertrend)
下一部分是情节，我认为这是我需要帮助的地方。我需要添加 2 个按钮并将这 2 个图表相互添加？
按钮 1：5m（显示 05m TF 图）
按钮 2：15m（显示 15m TF 图）
#5 Minute TF plot
fig = go.Figure()
fig.add_trace(go.Candlestick(x=df.index,
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'],
increasing_line_color= '#04b29b',
decreasing_line_color= '#ff2d5d',
increasing_fillcolor = '#04b29b',
decreasing_fillcolor = '#ff2d5d',
name='EURUSD'
))
fig.add_trace(go.Scatter(x=df.index,
y=df['Final Lowerband'],
mode='lines',
line=dict(color='#04b29b'),
name='Bullish'
))
fig.add_trace(go.Scatter(x=df.index,
y=df['Final Upperband'],
mode='lines',
line=dict(color='#ff2d5d'),
name='Bearish'
))
fig.update_layout(xaxis_rangeslider_visible=False,
plot_bgcolor = 'black', showlegend = False,
margin = dict(l=10, r=10,t=10,b=10),
paper_bgcolor='black',
xaxis=dict(showgrid=False, zerolinecolor = 'white',
color='white'),
yaxis=dict(showticklabels=False, showgrid=False))
fig.update_xaxes(
rangebreaks=[
dict(bounds=["sat", "mon"]), #hide weekends
dict(values=["2015-12-25", "2016-01-01"]) # hide Christmas
and New Year's
]
)
fig.show()
这是 15 分钟 tf
15 Minute TF Plot
fig15 = go.Figure()
fig15.add_trace(go.Candlestick(x=d15.index,
open=d15['Open'],
high=d15['High'],
low=d15['Low'],
close=d15['Close'],
increasing_line_color= '#04b29b',
decreasing_line_color= '#ff2d5d',
increasing_fillcolor = '#04b29b',
decreasing_fillcolor = '#ff2d5d',
name='EURUSD'
))
fig15.add_trace(go.Scatter(x=d15.index,
y=d15['Final Lowerband'],
mode='lines',
line=dict(color='#04b29b'),
name='Bullish'
))
fig15.add_trace(go.Scatter(x=d15.index,
y=d15['Final Upperband'],
mode='lines',
line=dict(color='#ff2d5d'),
name='Bearish'
))
fig15.update_layout(xaxis_rangeslider_visible=False,
plot_bgcolor = 'black', showlegend = False,
margin = dict(l=10, r=10,t=10,b=10),
paper_bgcolor='black',
xaxis=dict(showgrid=False, zerolinecolor = 'white',
color='white'),
yaxis=dict(showticklabels=False, showgrid=False))
fig15.update_xaxes(
rangebreaks=[
dict(bounds=["sat", "mon"]), #hide weekends
dict(values=["2015-12-25", "2016-01-01"]) # hide Christmas
and New Year's
]
)
fig15.show()
如果你想快速得到答案，如果你把更简化的代码放在上面，你会得到更快的答案。或者，如果您使用示例数据和图形代码创建可重现的情况，您将更有可能获得答案。要回答如何使每个图形成为按钮的主要问题，您可以使用官方参考中的股价数据，绘制三只不同的股票，并为每个设置 show/hide 控件以显示 例如，如果您只有 AAPL，将其他设置为 False 以隐藏它们。那只是唯一的设置。
import plotly.graph_objects as go
import plotly.express as px
df = px.data.stocks()
fig = go.Figure()
fig.add_trace(go.Scatter(x=df['date'], y=df['AAPL'], name='AAPL'))
fig.add_trace(go.Scatter(x=df['date'], y=df['GOOG'], name='GOOG'))
fig.add_trace(go.Scatter(x=df['date'], y=df['AMZN'], name='AMZN'))
fig.update_layout(
updatemenus=[
dict(
type="buttons",
direction="right",
active=0,
x=0.25,
y=1.2,
buttons=list([
dict(label="All",
method="update",
args=[{"visible": [True, True, True]},
{"title": "All"}
]),
dict(label="AAPL",
method="update",
args=[{"visible": [True, False, False]},
{"title": "AAPL"}
]),
dict(label="GOOG",
method="update",
args=[{"visible": [False, True, False]},
{"title": "GOOG"}
]),
dict(label="AMZN",
method="update",
args=[{"visible": [False, False, True]},
{"title": "AMZN"}
]),
]),
)
])
fig.show()