def generate_trendy_pdseries(
Nlength,
Tlength,
Xamplitude,
Volscale,
sines=False,
date_start=pd.datetime(1980, 1, 1),
):
"""
Generates a trend of length N amplitude X, plus gaussian noise mean zero std. dev (vol scale * amplitude)
With an arbitrary datetime index
If sines=True then generates as a sine wave, otherwise straight line
:param Nlength: total number of returns to generate
:type Nlength: int
:param Tlength: Length of each trend
:type Tlength: int
:param Xamplitude: Amplitude of each trend
:type Xamplitude: float
:param Volscale: Ratio of volatility to amplitude
:type Volscale: float
:param sines: Generate a sine wave (if True), or a saw tooth (False). Default False.
:type sines: bool
:param date_start: Start date for arbitrary series
:type date_start: datetime
:returns: a pd.Series, length Nlength
>>> generate_trendy_pdseries(10, 5, 5, 0.0)
1980-01-01 0.0
1980-01-02 1.0
1980-01-03 2.0
1980-01-04 3.0
1980-01-07 4.0
1980-01-08 5.0
1980-01-09 4.0
1980-01-10 3.0
1980-01-11 2.0
1980-01-14 1.0
Freq: B, dtype: float64
>>> generate_trendy_pdseries(10, 5, 5, 0.0, True)
1980-01-01 0.000000e+00
1980-01-02 1.469463e+00
1980-01-03 2.377641e+00
1980-01-04 2.377641e+00
1980-01-07 1.469463e+00
1980-01-08 2.220446e-16
1980-01-09 -1.469463e+00
1980-01-10 -2.377641e+00
1980-01-11 -2.377641e+00
1980-01-14 -1.469463e+00
Freq: B, dtype: float64
"""
stdev = Volscale * Xamplitude
noise_returns_as_list = generate_noise(Nlength, stdev)
# Can use a different process here if desired
if sines:
process_as_list = generate_siney_trends(Nlength, Tlength, Xamplitude)
else:
process_as_list = generate_trends(Nlength, Tlength, Xamplitude)
pd_process = create_arbitrary_pdseries(process_as_list,
date_start=date_start)
noise_returns = create_arbitrary_pdseries(noise_returns_as_list,
date_start=date_start)
process_returns = pd_process.diff()
combined_returns = noise_returns + process_returns
combined_returns[0] = 0
combined_price = combined_returns.cumsum()
return combined_price
return pdseries
def generate_trendy_pdseries(Nlength,
Tlength,
Xamplitude,
Volscale,
sines=False,
date_start=pd.datetime(1980, 1, 1)):
"""
Generates a trend of length N amplitude X, plus gaussian noise mean zero std. dev (vol scale * amplitude)
With an arbitrary datetime index
If sines=True then generates as a sine wave, otherwise straight line
:param Nlength: total number of returns to generate
:type Nlength: int
:param Tlength: Length of each trend
:type Tlength: int
:param Xamplitude: Amplitude of each trend
:type Xamplitude: float
:param Volscale: Ratio of volatility to amplitude
:type Volscale: float
:param sines: Generate a sine wave (if True), or a saw tooth (False). Default False.
:type sines: bool
:param date_start: Start date for arbitrary series
:type date_start: datetime
:returns: a pd.Series, length Nlength
>>> generate_trendy_pdseries(10, 5, 5, 0.0)
1980-01-01 0.0
1980-01-02 1.0
1980-01-03 2.0
1980-01-04 3.0
1980-01-07 4.0
1980-01-08 5.0
1980-01-09 4.0
1980-01-10 3.0
1980-01-11 2.0
1980-01-14 1.0
Freq: B, dtype: float64
>>> generate_trendy_pdseries(10, 5, 5, 0.0, True)
1980-01-01 0.000000e+00
1980-01-02 1.469463e+00
1980-01-03 2.377641e+00
1980-01-04 2.377641e+00
1980-01-07 1.469463e+00
1980-01-08 2.220446e-16
1980-01-09 -1.469463e+00
1980-01-10 -2.377641e+00
1980-01-11 -2.377641e+00
1980-01-14 -1.469463e+00
Freq: B, dtype: float64
"""
stdev = Volscale * Xamplitude
noise_returns_as_list = generate_noise(Nlength, stdev)
## Can use a different process here if desired
if sines:
process_as_list = generate_siney_trends(Nlength, Tlength, Xamplitude)
else:
process_as_list = generate_trends(Nlength, Tlength, Xamplitude)
pd_process = create_arbitrary_pdseries(
process_as_list, date_start=date_start)
noise_returns = create_arbitrary_pdseries(
noise_returns_as_list, date_start=date_start)
process_returns = pd_process.diff()
combined_returns = noise_returns + process_returns
combined_returns[0] = 0
combined_price = combined_returns.cumsum()
return combined_price
return pdseries