mirror of
https://github.com/wassname/optlib.git
synced 2026-07-05 17:30:23 +08:00
Update optlib. Add functionality, call API from instrument classes
This commit is contained in:
dbrojas
@@ -0,0 +1,2 @@
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from .instruments import Historical
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from .instruments import OptionChain
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+8
-3
@@ -1,7 +1,10 @@
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from subprocess import check_output
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import json
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from optlib.classes import Historical, OptionChain
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import logging
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logger = logging.getLogger(__name__)
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# ------------------------------
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# This class defines the URLs to the implemented endpoints.
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@@ -28,6 +31,8 @@ def _test_input(*args, **kwargs):
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def _get(endpoint, *args, **kwargs):
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url = "?".join([endpoint, "&".join(f"{k}={v}" for k, v in kwargs.items())])
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logger.debug("GET", url)
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if (resp := json.loads(check_output(["curl", "-gs", url]))).get("error"):
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raise API_InputError("{0}".format(resp["error"]))
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@@ -61,7 +66,7 @@ def get_chain(*args, **kwargs):
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_test_input(*args, **kwargs)
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endpoint = Endpoint.CHAIN
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return OptionChain.parse(_get(endpoint, *args, **kwargs))
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return _get(endpoint, *args, **kwargs)
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def get_historical(*args, **kwargs):
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"""Request historical price data from TDAmeritrade's API.
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@@ -83,7 +88,7 @@ def get_historical(*args, **kwargs):
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_test_input(*args, **kwargs)
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endpoint = Endpoint.HISTORY.format(kwargs["symbol"])
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return Historical.parse(_get(endpoint, *args, **kwargs))
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return _get(endpoint, *args, **kwargs)
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def get_fundamental(*args, **kwargs):
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"""Retrieve fundamental data.
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+54
-57
@@ -12,15 +12,12 @@ from __future__ import division
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# import necessary libaries
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import math
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import logging
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import numpy as np
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from scipy.stats import mvn, norm
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logging.basicConfig(
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format="[%(asctime)s %(levelname)s] %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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level=logging.INFO
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)
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import logging
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logger = logging.getLogger(__name__)
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# This class contains the limits on inputs for GBS models
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@@ -146,7 +143,7 @@ def _gbs_test_inputs(option_type, fs, x, t, r, b, v):
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# b = cost of carry, v = implied volatility
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# Outputs: value, delta, gamma, theta, vega, rho
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def _gbs(option_type, fs, x, t, r, b, v):
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logging.debug("Debugging Information: _gbs()")
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logger.debug("Debugging Information: _gbs()")
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# -----------
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# Test Inputs (throwing an exception on failure)
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_gbs_test_inputs(option_type, fs, x, t, r, b, v)
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@@ -159,7 +156,7 @@ def _gbs(option_type, fs, x, t, r, b, v):
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if option_type == "c":
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# it's a call
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logging.debug(" Call Option")
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logger.debug(" Call Option")
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value = fs * math.exp((b - r) * t) * norm.cdf(d1) - x * math.exp(-r * t) * norm.cdf(d2)
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delta = math.exp((b - r) * t) * norm.cdf(d1)
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gamma = math.exp((b - r) * t) * norm.pdf(d1) / (fs * v * t__sqrt)
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@@ -169,7 +166,7 @@ def _gbs(option_type, fs, x, t, r, b, v):
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rho = x * t * math.exp(-r * t) * norm.cdf(d2)
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else:
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# it's a put
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logging.debug(" Put Option")
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logger.debug(" Put Option")
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value = x * math.exp(-r * t) * norm.cdf(-d2) - (fs * math.exp((b - r) * t) * norm.cdf(-d1))
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delta = -math.exp((b - r) * t) * norm.cdf(-d1)
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gamma = math.exp((b - r) * t) * norm.pdf(d1) / (fs * v * t__sqrt)
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@@ -178,8 +175,8 @@ def _gbs(option_type, fs, x, t, r, b, v):
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vega = math.exp((b - r) * t) * fs * t__sqrt * norm.pdf(d1)
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rho = -x * t * math.exp(-r * t) * norm.cdf(-d2)
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logging.debug(" d1= {0}\n d2 = {1}".format(d1, d2))
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logging.debug(" delta = {0}\n gamma = {1}\n theta = {2}\n vega = {3}\n rho={4}".format(delta, gamma,
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logger.debug(" d1= {0}\n d2 = {1}".format(d1, d2))
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logger.debug(" delta = {0}\n gamma = {1}\n theta = {2}\n vega = {3}\n rho={4}".format(delta, gamma,
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theta, vega,
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rho))
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@@ -196,17 +193,17 @@ def _gbs(option_type, fs, x, t, r, b, v):
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def _american_option(option_type, fs, x, t, r, b, v):
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# -----------
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# Test Inputs (throwing an exception on failure)
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logging.debug("Debugging Information: _american_option()")
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logger.debug("Debugging Information: _american_option()")
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_gbs_test_inputs(option_type, fs, x, t, r, b, v)
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# -----------
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if option_type == "c":
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# Call Option
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logging.debug(" Call Option")
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logger.debug(" Call Option")
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return _bjerksund_stensland_2002(fs, x, t, r, b, v)
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else:
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# Put Option
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logging.debug(" Put Option")
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logger.debug(" Put Option")
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# Using the put-call transformation: P(X, FS, T, r, b, V) = C(FS, X, T, -b, r-b, V)
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# WARNING - When reconciling this code back to the B&S paper, the order of variables is different
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@@ -237,13 +234,13 @@ def _bjerksund_stensland_1993(fs, x, t, r, b, v):
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rho = my_output[5]
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# debugging for calculations
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logging.debug("-----")
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logging.debug("Debug Information: _Bjerksund_Stensland_1993())")
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logger.debug("-----")
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logger.debug("Debug Information: _Bjerksund_Stensland_1993())")
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# if b >= r, it is never optimal to exercise before maturity
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# so we can return the GBS value
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if b >= r:
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logging.debug(" b >= r, early exercise never optimal, returning GBS value")
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logger.debug(" b >= r, early exercise never optimal, returning GBS value")
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return e_value, delta, gamma, theta, vega, rho
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# Intermediate Calculations
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@@ -259,21 +256,21 @@ def _bjerksund_stensland_1993(fs, x, t, r, b, v):
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alpha = (i - x) * (i ** (-beta))
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# debugging for calculations
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logging.debug(" b = {0}".format(b))
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logging.debug(" v2 = {0}".format(v2))
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logging.debug(" beta = {0}".format(beta))
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logging.debug(" b_infinity = {0}".format(b_infinity))
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logging.debug(" b_zero = {0}".format(b_zero))
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logging.debug(" h1 = {0}".format(h1))
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logging.debug(" i = {0}".format(i))
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logging.debug(" alpha = {0}".format(alpha))
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logger.debug(" b = {0}".format(b))
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logger.debug(" v2 = {0}".format(v2))
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logger.debug(" beta = {0}".format(beta))
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logger.debug(" b_infinity = {0}".format(b_infinity))
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logger.debug(" b_zero = {0}".format(b_zero))
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logger.debug(" h1 = {0}".format(h1))
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logger.debug(" i = {0}".format(i))
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logger.debug(" alpha = {0}".format(alpha))
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# Check for immediate exercise
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if fs >= i:
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logging.debug(" Immediate Exercise")
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logger.debug(" Immediate Exercise")
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value = fs - x
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else:
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logging.debug(" American Exercise")
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logger.debug(" American Exercise")
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value = (alpha * (fs ** beta)
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- alpha * _phi(fs, t, beta, i, i, r, b, v)
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+ _phi(fs, t, 1, i, i, r, b, v)
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@@ -303,13 +300,13 @@ def _bjerksund_stensland_2002(fs, x, t, r, b, v):
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rho = my_output[5]
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# debugging for calculations
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logging.debug("-----")
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logging.debug("Debug Information: _Bjerksund_Stensland_2002())")
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logger.debug("-----")
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logger.debug("Debug Information: _Bjerksund_Stensland_2002())")
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# If b >= r, it is never optimal to exercise before maturity
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# so we can return the GBS value
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if b >= r:
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logging.debug(" Returning GBS value")
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logger.debug(" Returning GBS value")
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return e_value, delta, gamma, theta, vega, rho
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# -----------
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@@ -335,16 +332,16 @@ def _bjerksund_stensland_2002(fs, x, t, r, b, v):
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alpha2 = (i2 - x) * (i2 ** (-beta))
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# debugging for calculations
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logging.debug(" t1 = {0}".format(t1))
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logging.debug(" beta = {0}".format(beta))
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logging.debug(" b_infinity = {0}".format(b_infinity))
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logging.debug(" b_zero = {0}".format(b_zero))
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logging.debug(" h1 = {0}".format(h1))
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logging.debug(" h2 = {0}".format(h2))
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logging.debug(" i1 = {0}".format(i1))
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logging.debug(" i2 = {0}".format(i2))
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logging.debug(" alpha1 = {0}".format(alpha1))
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logging.debug(" alpha2 = {0}".format(alpha2))
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logger.debug(" t1 = {0}".format(t1))
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logger.debug(" beta = {0}".format(beta))
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logger.debug(" b_infinity = {0}".format(b_infinity))
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logger.debug(" b_zero = {0}".format(b_zero))
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logger.debug(" h1 = {0}".format(h1))
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logger.debug(" h2 = {0}".format(h2))
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logger.debug(" i1 = {0}".format(i1))
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logger.debug(" i2 = {0}".format(i2))
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logger.debug(" alpha1 = {0}".format(alpha1))
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logger.debug(" alpha2 = {0}".format(alpha2))
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# check for immediate exercise
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if fs >= i2:
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@@ -418,13 +415,13 @@ def _phi(fs, t, gamma, h, i, r, b, v):
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phi = math.exp(lambda1 * t) * (fs ** gamma) * (norm.cdf(d1) - ((i / fs) ** kappa) * norm.cdf(d2))
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logging.debug("-----")
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logging.debug("Debug info for: _phi()")
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logging.debug(" d1={0}".format(d1))
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logging.debug(" d2={0}".format(d2))
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logging.debug(" lambda={0}".format(lambda1))
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logging.debug(" kappa={0}".format(kappa))
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logging.debug(" phi={0}".format(phi))
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logger.debug("-----")
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logger.debug("Debug info for: _phi()")
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logger.debug(" d1={0}".format(d1))
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logger.debug(" d2={0}".format(d2))
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logger.debug(" lambda={0}".format(lambda1))
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logger.debug(" kappa={0}".format(kappa))
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logger.debug(" phi={0}".format(phi))
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return phi
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@@ -519,9 +516,9 @@ def _newton_implied_vol(val_fn, option_type, x, fs, t, b, r, cp, precision=.0000
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value, delta, gamma, theta, vega, rho = val_fn(option_type, fs, x, t, r, b, v)
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min_diff = abs(cp - value)
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logging.debug("-----")
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logging.debug("Debug info for: _Newton_ImpliedVol()")
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logging.debug(" Vinitial={0}".format(v))
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logger.debug("-----")
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logger.debug("Debug info for: _Newton_ImpliedVol()")
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logger.debug(" Vinitial={0}".format(v))
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# Newton-Raphson Search
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countr = 0
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@@ -529,7 +526,7 @@ def _newton_implied_vol(val_fn, option_type, x, fs, t, b, r, cp, precision=.0000
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v = v - (value - cp) / vega
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if (v > _GBS_Limits.MAX_V) or (v < _GBS_Limits.MIN_V):
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logging.debug(" Volatility out of bounds")
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logger.debug(" Volatility out of bounds")
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break
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value, delta, gamma, theta, vega, rho = val_fn(option_type, fs, x, t, r, b, v)
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@@ -537,7 +534,7 @@ def _newton_implied_vol(val_fn, option_type, x, fs, t, b, r, cp, precision=.0000
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# keep track of how many loops
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countr += 1
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logging.debug(" IVOL STEP {0}. v={1}".format(countr, v))
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logger.debug(" IVOL STEP {0}. v={1}".format(countr, v))
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# check if function converged and return a value
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@@ -552,8 +549,8 @@ def _newton_implied_vol(val_fn, option_type, x, fs, t, b, r, cp, precision=.0000
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# ----------
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# Find the Implied Volatility using a Bisection search
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def _bisection_implied_vol(val_fn, option_type, fs, x, t, r, b, cp, precision=.00001, max_steps=100):
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logging.debug("-----")
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logging.debug("Debug info for: _bisection_implied_vol()")
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logger.debug("-----")
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logger.debug("Debug info for: _bisection_implied_vol()")
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# Estimate Upper and Lower bounds on volatility
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# Assume American Implied vol is within +/- 50% of the GBS Implied Vol
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@@ -576,8 +573,8 @@ def _bisection_implied_vol(val_fn, option_type, fs, x, t, r, b, cp, precision=.0
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current_step = 0
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diff = abs(cp - cp_mid)
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logging.debug(" American IVOL starting conditions: CP={0} cp_mid={1}".format(cp, cp_mid))
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logging.debug(" IVOL {0}. V[{1},{2},{3}]".format(current_step, v_low, v_mid, v_high))
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logger.debug(" American IVOL starting conditions: CP={0} cp_mid={1}".format(cp, cp_mid))
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logger.debug(" IVOL {0}. V[{1},{2},{3}]".format(current_step, v_low, v_mid, v_high))
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# Keep bisection volatility until correct price is found
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while (diff > precision) and (current_step < max_steps):
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@@ -599,7 +596,7 @@ def _bisection_implied_vol(val_fn, option_type, fs, x, t, r, b, cp, precision=.0
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cp_mid = val_fn(option_type, fs, x, t, r, b, v_mid)[0]
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diff = abs(cp - cp_mid)
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logging.debug(" IVOL {0}. V[{1},{2},{3}]".format(current_step, v_low, v_mid, v_high))
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logger.debug(" IVOL {0}. V[{1},{2},{3}]".format(current_step, v_low, v_mid, v_high))
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# return output
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if abs(cp - cp_mid) < precision:
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@@ -1,7 +1,11 @@
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from optlib.api import get_chain, get_historical
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from datetime import datetime
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import pandas as pd
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import json
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class Historical:
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def __init__(self, data):
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@@ -17,6 +21,10 @@ class Historical:
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def parse(cls, resp):
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return Historical(resp)
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@classmethod
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def get(cls, *args, **kwargs):
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return cls.parse(get_historical(*args, **kwargs))
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def to_dataframe(self):
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return pd.DataFrame([c for c in self.candles])
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@@ -85,6 +93,10 @@ class OptionChain:
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putExpDateMap=resp.get("putExpDateMap", dict())
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)
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@classmethod
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def get(cls, *args, **kwargs):
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return cls.parse(get_chain(*args, **kwargs))
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@classmethod
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def from_json(cls, filepath):
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@@ -97,11 +109,15 @@ class OptionChain:
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def options(self):
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return list(self)
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@property
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def expiration_dates(self):
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return list({opt.expirationDate for opt in self.options})
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def to_dataframe(self):
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return pd.DataFrame([opt.to_dict() for opt in self.options])
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def __iter__(self):
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for expiryDate, strikes in self.expDateMap:
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for strikePrice, data in strikes.items():
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for _, strikes in self.expDateMap:
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for _, data in strikes.items():
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for r in data:
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yield Option(r)
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@@ -2,11 +2,11 @@ from setuptools import setup
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setup(
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name="optlib",
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version="0.4.0",
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version="0.5.0",
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description="A library for financial options pricing written in Python.",
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url="http://github.com/bartolomed/optlib",
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author="Davis Edwards & Daniel Rojas",
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packages=["optlib"],
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install_requires=["numpy", "scipy"],
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install_requires=["numpy", "scipy", "pandas"],
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zip_safe=False
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)
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