remove dependence on qis from stochvolmodels

README.md

@@ -34,6 +34,19 @@ Close using
 git clone https://github.com/ArturSepp/StochVolModels.git
 ```
 
+Core dependencies:
+    python = ">=3.8",
+    numba = ">=0.56.4",
+    numpy = ">=1.22.4",
+    scipy = ">=1.10",
+    pandas = ">=2.2.0",
+    matplotlib = ">=3.2.2",
+    seaborn = ">=0.12.2"
+
+Optional dependencies:
+    qis ">=2.1.38" (for running code in my_papers and volatility_book)
+
+
 # Table of contents
 1. [Model Interface](#introduction)
     1. [Log-normal stochastic volatility model](#logsv)

stochvolmodels/data/fetch_option_chain.py → my_papers/fetch_option_chain.py

@@ -7,16 +7,14 @@
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
-import qis
 from qis import TimePeriod
 from typing import Dict, Tuple, Optional, Literal
 from numba.typed import List
 from enum import Enum
-
+import qis as qis
 # chain
 from option_chain_analytics import OptionsDataDFs, create_chain_from_from_options_dfs
 from option_chain_analytics.option_chain import SliceColumn, SlicesChain
-
 # analytics
 from stochvolmodels.data.option_chain import OptionChain
 

my_papers/logsv_model_wtih_quadratic_drift/article_figures.py

@@ -20,7 +20,7 @@
 # analytics
 from stochvolmodels import OptionChain, LogSvParams, LogSVPricer, VariableType, ExpansionOrder
 from stochvolmodels.pricers.logsv.vol_moments_ode import compute_analytic_qvar
-from stochvolmodels.data.fetch_option_chain import generate_vol_chain_np
+from my_papers.fetch_option_chain import generate_vol_chain_np
 import stochvolmodels.data.test_option_chain as chains
 from stochvolmodels.utils.funcs import set_seed, compute_histogram_data
 import stochvolmodels.utils.plots as plot

my_papers/logsv_model_wtih_quadratic_drift/model_fit_to_options_timeseries.py

@@ -11,10 +11,8 @@
 from enum import Enum
 
 # analytics
-from stochvolmodels.data.fetch_option_chain import (generate_vol_chain_np,
-                                                    load_option_chain,
-                                                    sample_option_chain_at_times,
-                                                    load_price_data)
+from my_papers.fetch_option_chain import (generate_vol_chain_np,
+                                          sample_option_chain_at_times)
 from stochvolmodels import (OptionChain, LogSvParams, LogSVPricer, ConstraintsType, LogsvModelCalibrationType)
 
 # chain data

my_papers/risk_premia/gmm_slides.py → my_papers/risk_premia_gmm/gmm_slides.py

@@ -20,7 +20,7 @@
 from option_chain_analytics import OptionsDataDFs, create_chain_from_from_options_dfs
 from option_chain_analytics.ts_loaders import ts_data_loader_wrapper
 
-from stochvolmodels.pricers.gmm_pricer import plot_gmm_pdfs
+from my_papers.risk_premia.plot_gmm import plot_gmm_pdfs
 
 FIGSIZE = (16, 4.5)
 

my_papers/risk_premia_gmm/plot_gmm.py

@@ -0,0 +1,60 @@
+"""
+illustrations of gmm vols
+"""
+import numpy as np
+import pandas as pd
+import qis as qis
+import seaborn as sns
+import matplotlib.pyplot as plt
+from typing import List
+from stochvolmodels import GmmParams, OptionChain, GmmPricer
+
+
+def plot_gmm_pdfs(params: GmmParams,
+                  option_chain0: OptionChain,
+                  nstdev: float = 10.0,
+                  titles: List[str] = None,
+                  axs: List[plt.Subplot] = None
+                  ) -> plt.Figure:
+    """
+    plot gmm pdf and model fit
+    """
+    stdev = nstdev * params.get_get_avg_vol() * np.sqrt(params.ttm)
+    x = np.linspace(-stdev, stdev, 3000)
+    state_pdfs, agg_pdf = params.compute_state_pdfs(x=x)
+
+    columns = []
+    for idx in range(len(params.gmm_weights)):
+        columns.append(
+            f"state-{idx + 1}: mean={params.gmm_mus[idx]:0.2f}, vol={params.gmm_vols[idx]:0.2f}, weight={params.gmm_weights[idx]:0.2f}")
+
+    state_pdfs = pd.DataFrame(state_pdfs, index=x, columns=columns)
+    agg_pdf = pd.Series(agg_pdf, index=x, name='Aggregate PDF')
+    df = pd.concat([agg_pdf, state_pdfs], axis=1)
+
+    kwargs = dict(fontsize=14, framealpha=0.80)
+
+    if axs is None:
+        with sns.axes_style("darkgrid"):
+            fig, axs = plt.subplots(1, 2, figsize=(16, 4.5))
+    else:
+        fig = None
+
+    qis.plot_line(df=df,
+                  linestyles=['--'] + ['-'] * len(params.gmm_weights),
+                  y_limits=(0.0, None),
+                  xvar_format='{:,.2f}',
+                  xlabel='log-price',
+                  first_color_fixed=True,
+                  ax=axs[0],
+                  **kwargs)
+    axs[0].get_lines()[0].set_linewidth(4.0)
+    axs[0].get_legend().get_lines()[0].set_linewidth(4.0)
+    qis.set_title(ax=axs[0], title='(A) State PDF and Aggregate Risk-Neutral PDF', **kwargs)
+
+    gmm_pricer = GmmPricer()
+    gmm_pricer.plot_model_ivols_vs_bid_ask(option_chain=option_chain0, params=params,
+                                           is_log_strike_xaxis=True,
+                                           axs=[axs[1]],
+                                           **kwargs)
+    return fig

stochvolmodels/examples/run_gmm_fit.py → my_papers/risk_premia_gmm/run_gmm_fit.py

@@ -7,8 +7,8 @@
 import qis as qis
 from stochvolmodels import (get_btc_test_chain_data,
                             get_spy_test_chain_data,
-                            OptionChain, GmmPricer,
-                            plot_gmm_pdfs)
+                            OptionChain, GmmPricer)
+from my_papers.risk_premia_gmm.plot_gmm import plot_gmm_pdfs
 
 # get test option chain data
 # option_chain = get_btc_test_chain_data()

my_papers/volatility_models/autocorr_fit.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-import qis
+import qis as qis
 from typing import Tuple
 from scipy.optimize import minimize
 from enum import Enum

my_papers/volatility_models/ss_distribution_fit.py

@@ -7,7 +7,7 @@
 from scipy.stats import norm
 from typing import Optional
 from enum import Enum
-import qis
+import qis as qis
 
 from my_papers.volatility_models.load_data import fetch_ohlc_vol
 from stochvolmodels import LogSvParams, HestonParams

my_papers/volatility_models/vol_beta.py

@@ -2,7 +2,7 @@
 import matplotlib.pyplot as plt
 import seaborn as sns
 from enum import Enum
-import qis
+import qis as qis
 
 from my_papers.volatility_models.load_data import fetch_ohlc_vol
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "stochvolmodels"
-version = "1.0.24"
+version = "1.0.26"
 description = "Implementation of stochastic volatility models for option pricing"
 license = "LICENSE.txt"
 authors = ["Artur Sepp <[email protected]>"]
@@ -37,13 +37,10 @@ python = ">=3.8"
 numba = ">=0.55"
 numpy = ">=1.22.4"
 scipy = ">=1.3"
-statsmodels = ">=0.13.0"
 pandas = ">=0.19"
 matplotlib = ">=3.5.2"
 seaborn = ">=0.11.2"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
-
-

setup.py

@@ -16,7 +16,7 @@ def read_file(file):
 
 setup(
     name='stochvolmodels',
-    version='1.0.24',
+    version='1.0.26',
     author='Artur Sepp, Parviz Rakhmonov',
     author_email='[email protected], [email protected]',
     url='https://github.com/ArturSepp/StochVolModels',

stochvolmodels/__init__.py

@@ -22,7 +22,8 @@
     to_flat_np_array,
     update_kwargs,
     ncdf,
-    npdf
+    npdf,
+    find_nearest
 )
 
 from stochvolmodels.pricers.analytic.bsm import (
@@ -111,8 +112,7 @@
 
 from stochvolmodels.pricers.gmm_pricer import (
     GmmParams,
-    GmmPricer,
-    plot_gmm_pdfs
+    GmmPricer
 )
 
 from stochvolmodels.pricers.tdist_pricer import (

stochvolmodels/data/option_chain.py

@@ -8,16 +8,15 @@
 from __future__ import annotations
 
 import numpy as np
+import pandas as pd
 from dataclasses import dataclass
 from typing import Tuple, Optional
-
-import pandas as pd
 from numba.typed import List
 
 import stochvolmodels.pricers.analytic.bsm as bsm
-from  stochvolmodels.utils.var_swap_pricer import compute_var_swap_strike
-from stochvolmodels.pricers.factor_hjm.rate_core import get_default_swap_term_structure, swap_rate
 import stochvolmodels.pricers.analytic.bachelier as bachel
+from stochvolmodels.utils.var_swap_pricer import compute_var_swap_strike
+from stochvolmodels.pricers.factor_hjm.rate_core import get_default_swap_term_structure, swap_rate
 
 
 @dataclass

stochvolmodels/examples/run_heston_sv_pricer.py

@@ -1,8 +1,6 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from enum import Enum
-
 import stochvolmodels as sv
 from stochvolmodels import HestonPricer, HestonParams, OptionChain, BTC_HESTON_PARAMS
 

stochvolmodels/examples/run_pricing_options_on_qvar.py

@@ -3,7 +3,6 @@
 """
 import numpy as np
 import matplotlib.pyplot as plt
-import qis as qis
 import stochvolmodels.data.test_option_chain as chains
 from numba.typed import List
 from stochvolmodels import (LogSVPricer, LogSvParams, compute_analytic_qvar, OptionChain,
@@ -37,15 +36,15 @@
                                            params=LOGSV_BTC_PARAMS,
                                            variable_type=VariableType.Q_VAR,
                                            nb_path=nb_path)
-qis.set_suptitle(fig1, title='Implied variance skew by Log-Normal SV model')
+fig1.suptitle('Implied variance skew by Log-Normal SV model')
 
 # run Heston prices
 heston_pricer = HestonPricer()
 fig2 = heston_pricer.plot_model_ivols_vs_mc(option_chain=option_chain,
                                             params=BTC_HESTON_PARAMS,
                                             variable_type=VariableType.Q_VAR,
                                             nb_path=nb_path)
-qis.set_suptitle(fig2, title='Implied variance skew by Heston SV model')
+fig2.suptitle('Implied variance skew by Heston SV model')
 
 
 plt.show()

stochvolmodels/pricers/gmm_pricer.py

@@ -4,16 +4,12 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from dataclasses import dataclass
-
-import pandas as pd
-import qis as qis
-import seaborn as sns
 from scipy.optimize import minimize
 from numba import njit
 from numba.typed import List
 from typing import Tuple
 from enum import Enum
-
+# project
 import stochvolmodels.pricers.analytic.bsm as bsm
 from stochvolmodels.utils.funcs import to_flat_np_array, timer, npdf
 from stochvolmodels.pricers.model_pricer import ModelParams, ModelPricer
@@ -263,56 +259,6 @@ def gmm_vanilla_chain_pricer(gmm_weights: np.ndarray,
     return model_prices_ttms
 
 
-def plot_gmm_pdfs(params: GmmParams,
-                  option_chain0: OptionChain,
-                  nstdev: float = 10.0,
-                  titles: List[str] = None,
-                  axs: List[plt.Subplot] = None
-                  ) -> plt.Figure:
-    """
-    plot gmm pdf and model fit
-    """
-    stdev = nstdev * params.get_get_avg_vol() * np.sqrt(params.ttm)
-    x = np.linspace(-stdev, stdev, 3000)
-    state_pdfs, agg_pdf = params.compute_state_pdfs(x=x)
-
-    columns = []
-    for idx in range(len(params.gmm_weights)):
-        columns.append(
-            f"state-{idx + 1}: mean={params.gmm_mus[idx]:0.2f}, vol={params.gmm_vols[idx]:0.2f}, weight={params.gmm_weights[idx]:0.2f}")
-
-    state_pdfs = pd.DataFrame(state_pdfs, index=x, columns=columns)
-    agg_pdf = pd.Series(agg_pdf, index=x, name='Aggregate PDF')
-    df = pd.concat([agg_pdf, state_pdfs], axis=1)
-
-    kwargs = dict(fontsize=14, framealpha=0.80)
-
-    if axs is None:
-        with sns.axes_style("darkgrid"):
-            fig, axs = plt.subplots(1, 2, figsize=(16, 4.5))
-    else:
-        fig = None
-
-    qis.plot_line(df=df,
-                  linestyles=['--'] + ['-'] * len(params.gmm_weights),
-                  y_limits=(0.0, None),
-                  xvar_format='{:,.2f}',
-                  xlabel='log-price',
-                  first_color_fixed=True,
-                  ax=axs[0],
-                  **kwargs)
-    axs[0].get_lines()[0].set_linewidth(4.0)
-    axs[0].get_legend().get_lines()[0].set_linewidth(4.0)
-    qis.set_title(ax=axs[0], title='(A) State PDF and Aggregate Risk-Neutral PDF', **kwargs)
-
-    gmm_pricer = GmmPricer()
-    gmm_pricer.plot_model_ivols_vs_bid_ask(option_chain=option_chain0, params=params,
-                                           is_log_strike_xaxis=True,
-                                           axs=[axs[1]],
-                                           **kwargs)
-    return fig
-
-
 class UnitTests(Enum):
     CALIBRATOR = 1
 

stochvolmodels/pricers/heston_pricer.py

@@ -348,6 +348,7 @@ class UnitTests(Enum):
     MC_COMPARISION = 4
     MC_COMPARISION_QVAR = 5
 
+
 def run_unit_test(unit_test: UnitTests):
 
     import stochvolmodels.data.test_option_chain as chains

stochvolmodels/pricers/logsv/logsv_params.py

@@ -1,12 +1,13 @@
-from dataclasses import dataclass, asdict
-from typing import Optional, Dict, Any
-
+"""
+implementation of log sv params
+"""
 import numpy as np
 import pandas as pd
 from numpy import linalg as la
-from qis import find_nearest
+from dataclasses import dataclass, asdict
+from typing import Optional, Dict, Any
 
-from stochvolmodels import VariableType
+from stochvolmodels import VariableType, find_nearest
 from stochvolmodels.pricers.model_pricer import ModelParams
 
 

stochvolmodels/tests/bsm_mgf_pricer.py

@@ -9,7 +9,7 @@
 from typing import Tuple
 from enum import Enum
 
-from stochvolmodels.pricers.analytic import mgf_pricer as mgfp
+import stochvolmodels.utils.mgf_pricer as mgfp
 from stochvolmodels.pricers.analytic.bsm import infer_bsm_ivols_from_model_chain_prices
 from stochvolmodels.utils.config import VariableType