def trainModelGMM(X, lengths, states, num_gaus):
model = GMMHMM(n_components=states, n_mix=num_gaus,n_iter=1000,verbose=True).fit(X,lengths)
print('Mixture Models + HMM')
print(model.predict(X))
print(model.monitor_.converged)
print(model.monitor_)
print(model.score(X, lengths))
def hmm_train(features):
gmmhmm = GMMHMM(n_components=30, n_mix=8)
gmmhmm.startprob_ = np.array([
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0
])
l = np.identity(30) * 0.95
for i in range(l.shape[0] - 1):
l[i, i + 1] = 0.05
l[-1, -1] = 1
gmmhmm.transmat_ = l
gmmhmm.fit(features)
preds = gmmhmm.predict(features)
print(preds)
# Data Acquisition
X = np.column_stack([[0.1, 0.2, 0.3, 0.1], [0.3, 0.4, 0.7, 0.1],
[0.1, 0.2, 0.3, 0.5], [0.5, 0.6, 0.3, 0.5],
[0.1, 0.3, 0.3, 0.3]])
###############################################################################
# Run Gaussian HMM
print("fitting to HMM and decoding ...", end="")
# Make an HMM instance and execute fit
model = GMMHMM(n_components=4, n_mix=4, covariance_type="diag",
n_iter=1000).fit(X)
# Predict the optimal sequence of internal hidden state
hidden_states = model.predict(X)
print(hidden_states)
print("done")
###############################################################################
# Print trained parameters and plot
print("Transition matrix")
print(model.transmat_)
print()
print("Means and vars of each hidden state")
for i in range(model.n_components):
print("{0}th hidden state".format(i))
print("mean = ", model.means_[i])
print("var = ", np.diag(model.covars_[i]))
print()
def getTMHS(X):
model = GMMHMM(n_components=3, covariance_type="diag", n_iter=1000).fit(X)
hidden_states = model.predict(X)
transmat = model.transmat_
return hidden_states, transmat
class HiddenMarkovModel(BaseModel):
def __init__(self):
# Create some assets:
assetsList = [
Asset('WS30', 'traditional', 'historical'), # Index US
Asset('XAUUSD', 'traditional', 'historical'), # Commodity
Asset('GDAXIm', 'traditional', 'historical'), # Index EUR
Asset('EURUSD', 'traditional', 'historical'), # Major
Asset('GBPJPY', 'traditional', 'historical')
] # Minor
# Initialize the ResearchStudy class:
super().__init__('HiddenMarkovModel', assetsList)
# Make a random seed to reproduce results:
np.random.seed(33)
# Print to see if working:
#logger.warning(self.PORTFOLIO._portfolioDict['WS30'])
def _defineModelParameters(self):
# Define the model:
#self.model = GaussianHMM(n_components=2,
# covariance_type="full",
# n_iter=200,
# verbose=True)
self.model = GMMHMM(n_components=2,
covariance_type="full",
n_iter=20,
verbose=True)
def _monitorConvergence(self):
# Print:
logger.warning(f"Model Converged: {self.model.monitor_.converged}")
def _monitorHistory(self):
# Print:
logger.warning(f"Model History: {self.model.monitor_.history}")
def _fitTheModel(self, saveDirectory):
# Loop the portfolio dict:
for eachAssetName, eachAssetDataFrame in self.PORTFOLIO._portfolioDict.items(
):
# Re-initialize the parameters:
self._defineModelParameters()
# Fit the model:
# Get the returns into a 2D array > Actually, it is (X,) > We should conver to (X,1)
RETURNS_RESHAPED = np.column_stack([eachAssetDataFrame["Returns"]])
self.model.fit(RETURNS_RESHAPED)
logger.warning(
f"Model Score for asset <{eachAssetName}>: {self.model.score(RETURNS_RESHAPED)}"
)
# Check convergence and history:
self._monitorConvergence()
self._monitorHistory()
# Predict the hidden states based on the returns:
HIDDEN_STATES = self.model.predict(RETURNS_RESHAPED)
#logger.warning(HIDDEN_STATES)
# Save the model:
if saveDirectory:
self._saveModel(assetModelName=eachAssetName,
saveDirectory=saveDirectory)
# Create the new column in the dataframe:
eachAssetDataFrame['HiddenStates'] = HIDDEN_STATES
def _saveDataFrames(self, saveDirectory):
# Save each dataframe:
for eachAssetName, eachAssetDataFrame in self.PORTFOLIO._portfolioDict.items(
):
logger.warning(
f'[{self._saveDataFrames.__name__}] - Looping for asset <{eachAssetName}>...'
)
eachAssetDataFrame.to_csv(saveDirectory +
f'/{eachAssetName}_DF.csv')
def _saveModel(self, assetModelName, saveDirectory):
# Save the model:
with open(saveDirectory + f'/HMM_{assetModelName}.pickle',
'wb') as pickle_file:
pickle.dump(self.model, pickle_file)
def _loadModel(self, assetModelName, loadDirectory):
# Load the model:
with open(loadDirectory + f'/HMM_{assetModelName}.pickle',
'rb') as pickle_file:
self.model = pickle.load(pickle_file)
def _plotModelOutput(self, saveDirectory='', showIt=False):
# Plot:
for eachAssetName, eachAssetDataFrame in self.PORTFOLIO._portfolioDict.items(
):
logger.warning(
f'[{self._plotModelOutput.__name__}] - Looping for asset <{eachAssetName}>...'
)
# We will just get part of the dataframe for the plot:
eachAssetDataFrame_Little = eachAssetDataFrame[:200].copy()
eachAssetDataFrame_Little['date'] = range(
1,
len(eachAssetDataFrame_Little) + 1)
# Create the figure:
f1, ax = plt.subplots(3, figsize=(10, 5))
# Create the colormap:
colormap = cm.get_cmap('rainbow')
# Create the plots:
ax[0].scatter(eachAssetDataFrame_Little.date,
eachAssetDataFrame_Little.close,
c=eachAssetDataFrame_Little.HiddenStates,
cmap=colormap,
label='Hidden States',
s=80)
ax[0].set_xlabel('Hidden States',
horizontalalignment='center',
verticalalignment='center',
fontsize=12,
labelpad=20)
ax[0].set_ylabel('Observations',
horizontalalignment='center',
verticalalignment='center',
fontsize=12,
labelpad=20)
ax[0].legend(loc='best')
ax[1].plot(eachAssetDataFrame_Little.date,
eachAssetDataFrame_Little.close,
label='Close Price')
ax[1].set_xlabel('Observations',
horizontalalignment='center',
verticalalignment='center',
fontsize=12,
labelpad=20)
ax[1].set_ylabel('Close Price',
horizontalalignment='center',
verticalalignment='center',
fontsize=12,
labelpad=20)
ax[1].legend(loc='best')
ax[2].plot(eachAssetDataFrame_Little.date,
eachAssetDataFrame_Little.Returns,
label='Returns')
ax[2].set_xlabel('Observations',
horizontalalignment='center',
verticalalignment='center',
fontsize=12,
labelpad=20)
ax[2].set_ylabel('Returns',
horizontalalignment='center',
verticalalignment='center',
fontsize=12,
labelpad=20)
ax[2].legend(loc='best')
plt.grid(linestyle='dotted')
plt.subplots_adjust(left=0.09,
bottom=0.20,
right=0.94,
top=0.90,
wspace=0.2,
hspace=0)
f1.canvas.set_window_title(
f'Hidden Markov Model + more data plot for asset <{eachAssetName}>'
)
#f1.tight_layout()
# In PNG:
plt.savefig(saveDirectory + f'/HMM_{eachAssetName}.png')
# Show it:
if showIt:
plt.show()
