From 24276c1ec5459f9047bee99da424e3b27db7498b Mon Sep 17 00:00:00 2001
From: Ammar Siddiqui <ammarsiddiqui@Ammars-MacBook-Pro.local>
Date: Sat, 20 May 2023 16:52:33 -0400
Subject: [PATCH 1/4] Added new ml module folder and time series file
---
quantpy/ml/__init__.py | 0
quantpy/ml/time_series.py | 15 +++++++++++++++
2 files changed, 15 insertions(+)
create mode 100644 quantpy/ml/__init__.py
create mode 100644 quantpy/ml/time_series.py
diff --git a/quantpy/ml/__init__.py b/quantpy/ml/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/quantpy/ml/time_series.py b/quantpy/ml/time_series.py
new file mode 100644
index 0000000..188c27c
--- /dev/null
+++ b/quantpy/ml/time_series.py
@@ -0,0 +1,15 @@
+'''Models.'''
+
+'''
+Ideas:
+ Regression/Time-series:
+ - ARIMA
+ - ARFIMA
+ - Seasonal Models (SARIMA, Winters)
+ - Spectral Analysis
+ -
+'''
+
+import pandas as pd
+from statsmodels.tsa.arima.model import ARIMA
+
From 50bd233b1a507b271df2b5d93c9a99ba03addffd Mon Sep 17 00:00:00 2001
From: Ammar Siddiqui <ammarsiddiqui@Ammars-MacBook-Pro.local>
Date: Mon, 22 May 2023 18:45:46 -0400
Subject: [PATCH 2/4] added new time series regression model
---
quantpy/ml/models.py | 45 +++++++++++++++++++++++++++++++++++++++
quantpy/ml/time_series.py | 11 ++++++++++
2 files changed, 56 insertions(+)
create mode 100644 quantpy/ml/models.py
diff --git a/quantpy/ml/models.py b/quantpy/ml/models.py
new file mode 100644
index 0000000..9eaace8
--- /dev/null
+++ b/quantpy/ml/models.py
@@ -0,0 +1,45 @@
+import numpy as np
+from pandas import DataFrame, Series
+from typing import Union, Optional, Tuple, List
+from sklearn.base import RegressorMixin
+from statsmodels.base.models import Model as StatsModel
+from statsmodels.tsa.arima.model import ARIMA
+
+class BenchmarkRegressor():
+ '''
+ Default Regression Model based on ARIMA (Auto-Regressive Integrated Moving
+ Averages). Aims to provide a basic compute-efficient time-series model to
+ make predictions. (Aim to beat this model's prediction accuracy)
+ '''
+ def __init__(
+ self,
+ data: Union[DataFrame, Series],
+ param: Tuple[int, int, int] = (5, 2, 0)
+ ) -> None:
+
+ self.model = ARIMA(data, order=param)
+ self.model_fit = self.model.fit(data)
+ self.residuals = DataFrame(self.model_fit.resid)
+
+
+ def describe_residuals(self) -> DataFrame:
+ return self.residuals.describe()
+
+
+ def plot_residuals(self) -> None:
+ self.residuals().plot()
+
+
+ def cross_validate(
+ self,
+ comparision_model: Optional[Union[
+ List,
+ RegressorMixin,
+ StatsModel
+ ]]
+ ) -> None:
+ ...
+
+
+ def predict(self) -> np.float64:
+ ...
diff --git a/quantpy/ml/time_series.py b/quantpy/ml/time_series.py
index 188c27c..bb7b310 100644
--- a/quantpy/ml/time_series.py
+++ b/quantpy/ml/time_series.py
@@ -8,8 +8,19 @@
- Seasonal Models (SARIMA, Winters)
- Spectral Analysis
-
+
+ - Brownian Motion
+ - Martingale + Random Walk
+ - Monte-Carlo
+ - Stochastic Calculus for Finance (Discrete and Continuous)
+ - Implied Volatility
+ - Black Scholes
+ - Merton
+ - Markov
+ -
'''
import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
+
From a751c37ac942953a0ff75dd6e5b4ffd159506658 Mon Sep 17 00:00:00 2001
From: Ammar Siddiqui <ammarsiddiqui@Ammars-MacBook-Pro.local>
Date: Thu, 25 May 2023 19:15:45 -0400
Subject: [PATCH 3/4] added garch model and exceptions
---
quantpy/ml/exceptions.py | 8 ++
quantpy/ml/models.py | 45 ----------
quantpy/ml/time_series.py | 167 ++++++++++++++++++++++++++++++++------
setup.py | 4 +
4 files changed, 156 insertions(+), 68 deletions(-)
create mode 100644 quantpy/ml/exceptions.py
delete mode 100644 quantpy/ml/models.py
diff --git a/quantpy/ml/exceptions.py b/quantpy/ml/exceptions.py
new file mode 100644
index 0000000..55e66eb
--- /dev/null
+++ b/quantpy/ml/exceptions.py
@@ -0,0 +1,8 @@
+class ModelNotFittedError(Exception):
+ ...
+
+class ModelTypeNotSupportedError(Exception):
+ ...
+
+class DataTypeNotSupportedError(Exception):
+ ...
\ No newline at end of file
diff --git a/quantpy/ml/models.py b/quantpy/ml/models.py
deleted file mode 100644
index 9eaace8..0000000
--- a/quantpy/ml/models.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import numpy as np
-from pandas import DataFrame, Series
-from typing import Union, Optional, Tuple, List
-from sklearn.base import RegressorMixin
-from statsmodels.base.models import Model as StatsModel
-from statsmodels.tsa.arima.model import ARIMA
-
-class BenchmarkRegressor():
- '''
- Default Regression Model based on ARIMA (Auto-Regressive Integrated Moving
- Averages). Aims to provide a basic compute-efficient time-series model to
- make predictions. (Aim to beat this model's prediction accuracy)
- '''
- def __init__(
- self,
- data: Union[DataFrame, Series],
- param: Tuple[int, int, int] = (5, 2, 0)
- ) -> None:
-
- self.model = ARIMA(data, order=param)
- self.model_fit = self.model.fit(data)
- self.residuals = DataFrame(self.model_fit.resid)
-
-
- def describe_residuals(self) -> DataFrame:
- return self.residuals.describe()
-
-
- def plot_residuals(self) -> None:
- self.residuals().plot()
-
-
- def cross_validate(
- self,
- comparision_model: Optional[Union[
- List,
- RegressorMixin,
- StatsModel
- ]]
- ) -> None:
- ...
-
-
- def predict(self) -> np.float64:
- ...
diff --git a/quantpy/ml/time_series.py b/quantpy/ml/time_series.py
index bb7b310..7936d35 100644
--- a/quantpy/ml/time_series.py
+++ b/quantpy/ml/time_series.py
@@ -1,26 +1,147 @@
-'''Models.'''
-
-'''
-Ideas:
- Regression/Time-series:
- - ARIMA
- - ARFIMA
- - Seasonal Models (SARIMA, Winters)
- - Spectral Analysis
- -
-
- - Brownian Motion
- - Martingale + Random Walk
- - Monte-Carlo
- - Stochastic Calculus for Finance (Discrete and Continuous)
- - Implied Volatility
- - Black Scholes
- - Merton
- - Markov
- -
-'''
-
-import pandas as pd
+import numpy as np
+from pandas import DataFrame, Series
+from typing import Union, Optional, Tuple, List
+from sklearn.base import RegressorMixin
+from statsmodels.base.models import Model as StatsModel
from statsmodels.tsa.arima.model import ARIMA
+from arch import arch_model
+
+from exceptions import (
+ ModelNotFittedError,
+ ModelTypeNotSupportedError,
+ DataTypeNotSupportedError
+)
+
+class BenchmarkRegressor():
+ '''
+ Default Regression Model based on ARIMA (Auto-Regressive Integrated Moving
+ Averages). Aims to provide a basic compute-efficient time-series model to
+ make predictions. (Aim to beat this model's prediction accuracy)
+ '''
+ def __init__(
+ self,
+ data: Union[DataFrame, Series],
+ param: Tuple[int, int, int] = (5, 2, 0),
+ fit: bool = True,
+ seasonal: bool = False,
+ bias: Optional[float] = None
+ ) -> None:
+ self.data = data
+ self.param = param
+ self.model = ARIMA(data, order=param)
+ self.bias = bias if bias else 0
+ if fit:
+ self.model_fit = self.model.fit(data + bias)
+ self.residuals = DataFrame(self.model_fit.resid)
+
+
+ def get_model(self) -> StatsModel:
+ return self.model
+
+
+ def get_summary(self) -> None:
+ self.model.summary()
+
+
+ def get_data(self) -> DataFrame:
+ return self.data
+
+
+ def get_params(self) -> Tuple:
+ return self.params
+
+
+ def fit(self):
+ self.model_fit = self.model.fit(self.data + self.bias)
+ self.residuals = DataFrame(self.model_fit.resid)
+
+
+ def describe_residuals(self) -> DataFrame:
+ if not self.residuals:
+ raise ModelNotFittedError()
+ return self.residuals.describe()
+
+
+ def plot_residuals(self) -> None:
+ if not self.residuals:
+ raise ModelNotFittedError()
+ self.residuals.plot()
+
+
+ def cross_validate(
+ self,
+ comparision_model: Optional[Union[
+ List,
+ RegressorMixin,
+ StatsModel
+ ]]
+ ) -> None:
+ '''Validate based on data or compare to other models'''
+ ...
+
+
+ def predict(self, horizon: Union[int, Tuple] = None) -> np.float64:
+ if not self.model_fit:
+ raise ModelNotFittedError()
+ if type(horizon) == int:
+ return self.model_fit.forecast(steps=horizon)[0]
+ elif type(horizon) == tuple:
+ return self.model_fit.predict(start=horizon[0], end=horizon[1])
+ else:
+ return self.model_fit.forecast()[0]
+
+
+class GARCH():
+ '''
+ Default GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity)
+ model to find forecasts based on changes of variances in a time-series.
+ Useful for Volatility.
+ '''
+ def __init__(
+ self,
+ data: Union[DataFrame, Series],
+ p: int = 1,
+ q: int = 1,
+ fit: bool = True
+ ) -> None:
+ self.data = data
+ self.p, self.q = p, q
+ self.model = arch_model(data, mean='Zero', vol='GARCH', p=p, q=q)
+ if fit:
+ self.model_fit = self.model.fit()
+
+
+ def get_model(self) -> arch_model:
+ return self.model
+
+
+ def get_data(self) -> DataFrame:
+ return self.data
+
+
+ def get_params(self) -> Tuple:
+ return self.p, self.q
+
+
+ def fit(self):
+ self.model_fit = self.model.fit()
+
+
+ def cross_validate(
+ self,
+ comparision_model: Optional[Union[
+ List,
+ RegressorMixin,
+ StatsModel
+ ]]
+ ) -> None:
+ '''Validate based on data or compare to other models'''
+ ...
+
+
+ def predict(self, horizon: Union[DataFrame, Series]) -> Series:
+ if not self.model_fit:
+ raise ModelNotFittedError()
+ return self.model_fit.forecast(horizon=horizon)
diff --git a/setup.py b/setup.py
index b6b0967..bc9a2b1 100644
--- a/setup.py
+++ b/setup.py
@@ -11,7 +11,11 @@
description='A framework for quantitative finance In python',
long_description=open('README.md').read(),
install_requires=[
+ "numpy >= 1.23.5",
"pandas >= 0.10.0",
"matplotlib >= 1.1.0",
+ "statsmodels >= 0.14.0",
+ "arch >= 6.0.1",
+ "sklearn >= 1.1.0",
],
)
From 5319361eb48dabb5953f7d98201017b7a13a2a63 Mon Sep 17 00:00:00 2001
From: Ammar Siddiqui <ammarsiddiqui@Ammars-MacBook-Pro.local>
Date: Thu, 25 May 2023 19:22:37 -0400
Subject: [PATCH 4/4] updated sklearn version
---
quantpy/ml/exceptions.py | 2 +-
setup.py | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/quantpy/ml/exceptions.py b/quantpy/ml/exceptions.py
index 55e66eb..d4d9a08 100644
--- a/quantpy/ml/exceptions.py
+++ b/quantpy/ml/exceptions.py
@@ -5,4 +5,4 @@ class ModelTypeNotSupportedError(Exception):
...
class DataTypeNotSupportedError(Exception):
- ...
\ No newline at end of file
+ ...
diff --git a/setup.py b/setup.py
index bc9a2b1..59851f7 100644
--- a/setup.py
+++ b/setup.py
@@ -16,6 +16,6 @@
"matplotlib >= 1.1.0",
"statsmodels >= 0.14.0",
"arch >= 6.0.1",
- "sklearn >= 1.1.0",
+ "scikit-learn >= 1.1.0",
],
)