Basic EMA and RSI Stratergy

[Sfuarf]

Hi there,

I have started having a read through the documentation and I would like to implement my own stratergy using EMA and RSI values. I would like to do similar to the following:

fast_ema = period=tbc
slow_ema = period=tbc

Logic to be:

Entry = fast_ema crosses_above(slow_ema) and RSI_value < Threshold

OR

Entry = fast_ema >= slow_ema and RSI_value < Threshold

I wanted to use the ta-lib intergration for getting the EMA values, but I can not find how I can use the equivilent "MA_crossed_above()" functions for these custom indicators.

Any help would be greatly appreciated!

Cheers,

[Krzysiek191]

You need to check what are the output names for the indicators you want to use.
print(fast_ema.output_names)
Then you pick the output you want to use and add the attribute of your desire. I use pandas_ta so in my case it would be:
fast_ema.ema_crossed_above
In which 'ema' is the output...

[Sfuarf]

Brilliant, thank you @Krzysiek191! I think I have managed to get it working.

[cryptoliciousdev]

hi, what would the syntax exactly be for specifically using pandas_ta EMA ?
fast_ma, slow_ma = vbt.MA.run_combs(price, window=windows, r=2, short_names=['fast', 'slow'])

what should it be exactly for EMA from pandas_ta? thanks in advance

this didnt work:
price = vbt.YFData.download(symbols , start=start, end=end, missing_index='drop').get('Close')
windows = np.arange(10, 45)
fast_ema, slow_ema = vbt.IndicatorFactory.from_pandas_ta('EMA').run_combs(price, window=windows, r=2, short_names=['fast', 'slow'])
gave ValueError: not enough values to unpack (expected 2, got 0)

the online manual showed this syntax on the indicators page:
vbt.pandas_ta('SMA').run(price, [2, 3]).sma

tried like
vbt.pandas_ta('EMA').run_combs(price, window=windows, r=2, short_names=['fast', 'slow'])
and
vbt.pandas_ta('EMA').run_combs(price, window=windows, r=2, short_names=['fast', 'slow']).ema
and
fast_ema, slow_ema = vbt.IndicatorFactory.from_pandas_ta('EMA').run_combs(price, window=windows, r=2, short_names=['fast', 'slow']).ema
diff errors like tuple object has no attribute 'ema' or

tried a few other ways, no luck

thanks

[murdochfoo]

hi i am having the same issue. Could you pls advise the final code that got it working. I tried "print(fast_ma.output_names)"

but got "list' object has no attribute 'output_names"

AttributeError Traceback (most recent call last)
Input In [18], in <cell line: 1>()
----> 1 print(fast_ma.output_names)

AttributeError: 'list' object has no attribute 'output_names'

[AntonioRitucci]

hi, what would the syntax exactly be for specifically using pandas_ta EMA ? fast_ma, slow_ma = vbt.MA.run_combs(price, window=windows, r=2, short_names=['fast', 'slow'])

what should it be exactly for EMA from pandas_ta? thanks in advance

this didnt work: price = vbt.YFData.download(symbols , start=start, end=end, missing_index='drop').get('Close') windows = np.arange(10, 45) fast_ema, slow_ema = vbt.IndicatorFactory.from_pandas_ta('EMA').run_combs(price, window=windows, r=2, short_names=['fast', 'slow']) gave ValueError: not enough values to unpack (expected 2, got 0)

the online manual showed this syntax on the indicators page: vbt.pandas_ta('SMA').run(price, [2, 3]).sma

tried like vbt.pandas_ta('EMA').run_combs(price, window=windows, r=2, short_names=['fast', 'slow']) and vbt.pandas_ta('EMA').run_combs(price, window=windows, r=2, short_names=['fast', 'slow']).ema and fast_ema, slow_ema = vbt.IndicatorFactory.from_pandas_ta('EMA').run_combs(price, window=windows, r=2, short_names=['fast', 'slow']).ema diff errors like tuple object has no attribute 'ema' or

tried a few other ways, no luck

thanks

this is the working example adpted with pandas_ta EMA: `import vectorbt as vbt
import numpy as np

symbols = ["BTC-USD", "ETH-USD", "LTC-USD"]
price = vbt.YFData.download(symbols, missing_index='drop').get('Close')

windows = np.arange(2, 101)
EMA = vbt.IndicatorFactory.from_pandas_ta('EMA')
fast_ma, slow_ma = EMA.run_combs(price, length=windows, r=2, short_names=['fast', 'slow'])
entries = fast_ma.ema_crossed_above(slow_ma)
exits = fast_ma.ema_crossed_below(slow_ma)

pf_kwargs = dict(size=np.inf, fees=0.001, freq='1D')
pf = vbt.Portfolio.from_signals(price, entries, exits, **pf_kwargs)

fig = pf.total_return().vbt.heatmap(
x_level='fast_length', y_level='slow_length', slider_level='symbol', symmetric=True,
trace_kwargs=dict(colorbar=dict(title='Total return', tickformat='%')))
fig.show()` note the changes are basically window->length as the pandas_ta uses "length" argument and not "window" argument, this mus be applied both in run_combs function and in heatmap x_level and y_level