def grid_search():
tickers = ['AAPL']
features = ['macd_9_18']
ta = TradingAlgorithm(tickers,
features,
type='svm',
data_lookback_period=15,
label_lower_threshold=-0.15,
label_upper_threshold=0.015,
label_period=10,
data_splits=[0.8, 0.2],
cnn_split=0,
verbose=True)
ta.grid_search(tickers[0], verbose=True)
def loadTradingAlgorithm(tradingAlgorithmId):
tradingAlgResult = db_wrapper.getTradingAlgorithm(tradingAlgorithmId)
tickers = tradingAlgResult[1].split(',')
features = tradingAlgResult[2].split(',')
length = int(tradingAlgResult[3])
upper_threshold = float(tradingAlgResult[4])
lower_threshold = float(tradingAlgResult[5])
period = int(tradingAlgResult[6])
modelCollectionIds = tradingAlgResult[7].split(',')
votingType = tradingAlgResult[8]
mcs = []
for modelCollectionId in modelCollectionIds:
mc = loadModelCollection(modelCollectionId)
mcs.append(mc)
model_mc = mcs[0]
features = model_mc.features
t = model_mc.type
ta = TradingAlgorithm(tickers=tickers,
features=features,
type=t,
data_lookback_period=length,
label_upper_threshold=upper_threshold,
label_lower_threshold=lower_threshold,
label_period=period,
voting_type=votingType,
models=mcs)
return ta
def test_conf_matrix_model_coll():
# one cm for each model
tickers = ['AAPL']
features = ['macd_9_18', 'closes']
ta = TradingAlgorithm(tickers,
features,
type='svm',
data_lookback_period=10,
label_lower_threshold=-0.15,
label_upper_threshold=0.015,
label_period=10,
data_splits=[0.8, 0.2],
cnn_split=0,
verbose=True,
model_params={
'gamma': 10,
"C": 1
})
ta.plot_model_cm(tickers[0])
def run_test_one():
tickers = ['AAPL']
features = ['macd_9_18', 'closes', 'prings', 'kst', 'kstTrix', 'rsi']
ta = TradingAlgorithm(tickers,
features,
type='svm',
data_lookback_period=10,
label_lower_threshold=-0.15,
label_upper_threshold=0.035,
label_period=10,
voting_type='Penrose',
model_params={
'gamma': 100,
"C": 1
},
data_splits=[0.8, 0.2],
cnn_split=0,
verbose=True,
test_mode=True)
print(ta.models[0].accuracy)
ta.generate_conf_matricies(datetime.datetime(2018, 1, 1),
datetime.datetime(2019, 1, 1))
def hyper_param_haul(features_list,ticker_list, model_params):
'''
Function for testing hyperparamters gamma, C for SVMs
Inputs:
ticker_list {list of lists} -- tickers to test for each feature set, each set of params
feautures_list {list of lists} -- features to each test for each set of params
model_params {list of dictionaries} -- combinations of gamma, C to test
-- writes results to csv for analysis, csv's in backend/hyperparam_tests--
'''
acc_lst =[]
cm_lst = []
file = open('hyperparam_tst.txt', 'w')
writer = csv.writer(file)
writer.writerow(["tickers", "features", "params", "acc"])
for ii in range(len(ticker_list)):
tickers = ticker_list[ii]
for jj in range(len(features_list)):
features = features_list[jj]
for kk in range(len(model_params)):
params = model_params[kk]
ta = TradingAlgorithm(tickers, features, type = 'svm', data_lookback_period = 10, label_lower_threshold = -0.15, label_upper_threshold = 0.035, label_period = 10, data_splits = [0.8, 0.2], cnn_split=0, verbose=True, model_params = params)
cm = ta.plot_model_cm(tickers[0])
cm_lst += cm
acc = ta.models[0].accuracy
acc_lst.append(acc)
writer.writerow([tickers, features, params, acc])
writer.writerow(['confusion matrices for features of earlier models trained on: ' + str(tickers[0])+ ' and ' + str(features)])
for ii in range(len(cm_lst)):
for jj in range(len(cm_lst[ii])):
file.write(str(cm_lst[ii][jj]))
file.write("\n")
file.close()
return max(acc_lst)
def build_confusion_matrix():
# one cm for whole alg
tickers = ['AAPL']
features = ['macd_9_18', 'closes', 'prings', 'kst', 'kstTrix', 'rsi']
ta = TradingAlgorithm(tickers,
features,
type='svm',
data_lookback_period=10,
label_lower_threshold=-0.15,
label_upper_threshold=0.035,
label_period=10,
voting_type='accuracy',
model_params={
'gamma': 100,
"C": 1
},
data_splits=[0.8, 0.2],
cnn_split=0,
verbose=True,
test_mode=True)
print(ta.models[0].accuracy)
## At this point, the model is trained for everything.
ta.generate_conf_matricies(datetime.datetime(2018, 1, 1),
datetime.datetime(2020, 3, 15))
def build_example_model():
tickers = ['AAPL', 'IBM']
features = [
'closes',
]
look_back = 10
lower_threshold = -0.15
upper_threshold = 0.03
label_period = 10
ta = TradingAlgorithm(tickers,
features,
type='svm',
data_lookback_period=look_back,
label_lower_threshold=lower_threshold,
label_upper_threshold=upper_threshold,
label_period=label_period)
print("Trading Alg Build")
createTradingAlgorithm(ta)