class Stats(object):
"""
docstring
"""
def __init__(self):
"""
docstring
"""
self.db = Database()
self.df = pd.DataFrame()
def get_data(self):
self.db.set_time_from("-120d")
self.df = self.db.load_data(TableName.DAY, symbols="jks")
return self.df
def get_volume_realtions(self):
df = self.get_data()
print(df.corr())
# print(df)
sns.pairplot(df, kind="scatter")
plt.show()
def linear_relations(self):
df = self.get_data()
print(linregress(df['close'], df['volume']))
plt.scatter(df['close'], df['volume'])
plt.show()
class PredDT(object):
"""
docstring
"""
def __init__(self):
"""
docstring
"""
self.db = Database()
self.df = pd.DataFrame()
self.bt = BackTest()
def dt_prediction(self):
self.db.set_time_from("730d")
self.df = self.db.load_data(TableName.DAY,symbols='SPY')
# print(self.df)
self.add_sma(30)
self.add_sma(100)
print(self.df)
pass
def add_sma(self, days):
self.df["sma"+str(days)] = self.df['close'].rolling(window=days).mean()
def plot_data(self):
self.df.plot(kind="line", use_index=True,
y=["close","sma30","sma100"], legend=False, color=["b","orange","y"], linewidth=2, alpha=0.7)
plt.show()
def heatmap(self):
self.db.set_time_from("600d")
self.df = self.db.load_data(TableName.DAY, symbols='CHWY')
# calculate the correlation matrix
# self.df = ta.add_all_ta_features(self.df,open="open",close="close",high="high",low="low", volume="volume")
stocks = sdf.retype(self.df)
stocks = FinI.add_indicators(stocks)
stocks.get("macd")
stocks.get("kdjk")
stocks.get("boll")
stocks.get("sma")
print(stocks)
# stocks = FinI.add_day_types(stocks)
stocks.drop(columns=["open", "high", "low",
"sym", "sector", ], inplace=True)
print(stocks[['volume',"yearweek"]])
corr = stocks.corr()
corr = corr.round(1)
# print(corr)
# plot the heatmap
sns.heatmap(corr, annot=True, cmap="coolwarm",
xticklabels=corr.columns,
yticklabels=corr.columns)
plt.show()
def prediction(self):
df = self.df[['close']]
future_days = 25
# create the future date set X and convert it ti numpy array and remove last 'x' rows/days
df['Prediction'] = df[['close']].shift(-future_days)
X=np.array(df.drop(['Prediction'],1))[:-future_days]
# create the target data cell and get all of the target data
y=np.array(df['Prediction'])[:-future_days]
# print(y)
# lets split the data into 75% training and 25 %tresting
x_train,x_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)
# create decision tree regresor
tree = DecisionTreeRegressor().fit(x_train, y_train)
# create regresssion model
lr = LinearRegression().fit(x_train, y_train)
# get the last x rows of the future data set
x_future = df.drop(['Prediction'],1)[:-future_days]
x_future = x_future.tail(future_days)
x_future = np.array(x_future)
tree_prediction = tree.predict(x_future)
print(tree_prediction)
lr_prediction = lr.predict(x_future)
print()
print(lr_prediction)
# visualise predicted data
predictions = tree_prediction
valid = df[X.shape[0]:]
valid['Predictions'] = predictions
plt.figure(figsize=(16,8))
plt.title('days')
plt.xlabel('days')
plt.gca().fmt_xdata = mpl.dates.DateFormatter('%Y-%m-%d')
plt.grid(axis="both")
plt.ylabel("prediction")
plt.plot(df['close'])
plt.plot(valid[['close','Predictions']])
plt.legend(['Orig','Val', 'Pred'])
plt.show()