def generate_signal_prices(dfs,
feature_names_filename,
model_params_filename,
period=1e7,
sample_th=10,
returns_th=5):
file = open(model_params_filename, 'r')
model_params = json.load(file)
file.close()
features_config = load_feature_config(feature_names_filename)
timestamps = [subsample(df, sample_th) for df in dfs]
model = Model(**model_params)
returns = [calc_return(df, period) for df in dfs]
features = [generate_features(df, features_config) for df in dfs]
returns = [calc_return(df, period) for df in dfs]
returns = [get_non_duplicated(x) for x in returns]
features = [get_non_duplicated(feature) for feature in features]
sub_features = [
features[i].loc[timestamps[i]] for i in range(len(timestamps))
]
sub_returns = [
returns[i].loc[timestamps[i]] for i in range(len(timestamps))
]
signal_prices_list = []
for i in range(len(features)):
test_features = features[i]
X_test = get_non_duplicated(test_features)
cond = (X_test.isnull().sum(axis=1) == 0)
X_test = X_test.loc[cond]
df = get_non_duplicated(dfs[i])
df = df.loc[cond]
mid_prices = 0.5 * (df['ap0'] + df['bp0'])
X_train = []
y_train = []
for j in range(len(features)):
if i == j:
continue
train_features = sub_features[j]
train_returns = sub_returns[j]
cond = (~train_returns.isnull()) & (train_features.isnull().sum(
axis=1) == 0)
train_features = train_features.loc[cond]
train_returns = train_returns.loc[cond]
X_train.append(train_features)
y_train.append(train_returns)
X_train = pd.concat(X_train)
y_train = pd.concat(y_train)
X_train, y_train = subsample_returns(X_train, y_train, returns_th)
model.fit(X_train.values, y_train.values)
pred = model.predict(X_test)
pred = pd.Series(pred, index=X_test.index)
signal_prices = mid_prices + pred
signal_prices_list.append(signal_prices)
return signal_prices_list
def post(self):
model = Model()
text = request.json['text']
predict = model.predict(text)
return jsonify(predict)
