-
Notifications
You must be signed in to change notification settings - Fork 1
/
coffee_ml.py
166 lines (116 loc) · 5.38 KB
/
coffee_ml.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
import numpy as np
from ta import *
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split,cross_val_score
from sklearn import metrics
from sklearn.neighbors import *
from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor, ExtraTreesClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.feature_selection import *
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
from sklearn.svm import LinearSVC,SVC
import backtrader as bt
import pandas as pd
import numpy as np
from statsmodels.tsa.arima_model import ARIMA
from sklearn import metrics
import seaborn as sns
from vwap import *
# Displays all coloumns in df when outputting but some coloumns may go on the second line due to the console char width
# limit so you also expand the width so this doesn;t happen
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 200)
pd.set_option('float_format', '{:f}'.format)
df = pd.read_csv('COFFEE.CMDUSX_Candlestick_5_M_ASK_04.12.2017-03.05.2019-exact.csv', sep=',')
df2 = pd.read_csv('COCOA.CMDUSD_Candlestick_5_M_ASK_04.12.2017-03.05.2019-exact.csv', sep=',')
df = df.dropna()
df2 = df2.dropna()
# Features construction, volume perc change should be 1 or -1 so its normalized
df['High_wick_p'] = np.where(df['Close'] > df['Open'], (df['High'] - df['Close']) / (df['High'] - df['Low']), (df['High'] - df['Open']) / (df['High'] - df['Low']))
df['Low_wick_p'] = np.where(df['Close'] > df['Open'], (df['Open'] - df['Low']) / (df['High'] - df['Low']), (df['Close'] - df['Low']) / (df['High'] - df['Low']))
df['Body_p'] = 1 - df['High_wick_p'] - df['Low_wick_p']
df2['High_wick_p'] = np.where(df2['Close'] > df2['Open'], (df2['High'] - df2['Close']) / (df2['High'] - df2['Low']), (df2['High'] - df2['Open']) / (df2['High'] - df2['Low']))
df2['Low_wick_p'] = np.where(df2['Close'] > df2['Open'], (df2['Open'] - df2['Low']) / (df2['High'] - df2['Low']), (df2['Close'] - df2['Low']) / (df2['High'] - df2['Low']))
df2['Body_p'] = 1 - df2['High_wick_p'] - df2['Low_wick_p']
df['High_wick_p2'] = df2['High_wick_p']
df['Low_wick_p2'] = df2['Low_wick_p']
df['Body_p2'] = df2['Body_p']
df['Volume'] = df['Volume']/1000000
df['Volume'] = df['Volume'].round(4)
df['Vol_perc_change'] = df['Volume'].pct_change()
df['RSI_perc_change'] = rsi(df['Close'], n=14).pct_change()
df['ATR_perc_change'] = average_true_range(df['High'], df['Low'], df['Close'], n=5).pct_change()
df['SMA_10_perc_change'] = ema_indicator(df['Close'], n=10).pct_change()
df['SMA_5_perc_change'] = ema_indicator(df['Close'], n=5).pct_change()
df['Stock_Returns'] = df['Close'].pct_change()
df['Volume2'] = df2['Volume']/1000000
df['Volume2'] = df['Volume2'].round(4)
df['Vol_perc_change2'] = df['Volume2'].pct_change()
df['RSI_perc_change2'] = rsi(df2['Close'], n=14).pct_change().round(3)
df['ATR_perc_change2'] = average_true_range(df2['High'], df2['Low'], df2['Close'], n=5).pct_change()
df['SMA_10_perc_change2'] = ema_indicator(df2['Close'], n=10).pct_change()
df['SMA_5_perc_change2'] = ema_indicator(df2['Close'], n=5).pct_change()
df['Stock_Returns2'] = df2['Close'].pct_change()
df['Ratio'] = (df['Close'] / df2['Close']).pct_change()
df['Diff'] = (df['Close'] - df2['Close']).pct_change()
df['vwap1'] = vwap(df['Close'], df['High'], df['Low'], df['Volume'], period = 5)
df['vwap1'] = df['vwap1'].pct_change()
df['vwap2'] = vwap(df2['Close'], df2['High'], df2['Low'], df2['Volume'], period = 5)
df['vwap2'] = df['vwap2'].pct_change()
df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
X = df[['RSI_perc_change',
'ATR_perc_change',
'Stock_Returns',
'SMA_10_perc_change',
'SMA_5_perc_change',
'High_wick_p',
'Low_wick_p',
'Body_p',
'vwap1',
'RSI_perc_change2',
'ATR_perc_change2',
'Stock_Returns2',
'SMA_10_perc_change2',
'SMA_5_perc_change2',
'High_wick_p2',
'Low_wick_p2',
'Body_p2',
'vwap2',
'Ratio'
]]
y = np.where(df['Close'].shift(-1) > df['Close'], 1, -1)
feat = ExtraTreesClassifier()
model = feat.fit(X,y)
feat_importances = pd.Series(model.feature_importances_, index=X.columns)
feat_importances.nlargest(10).plot(kind='barh')
plt.show()
corr = df.corr()
sns.heatmap(corr,
xticklabels=corr.columns,
yticklabels=corr.columns)
standardscaler = StandardScaler(with_mean=False, with_std=False)
X = standardscaler.fit_transform(X)
print(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, shuffle=False)
#print(pd.isnull(X_train).sum() > 0)
log_regr = RandomForestClassifier(n_estimators=500, n_jobs=-1, min_samples_leaf=10)
log_regr = log_regr.fit(X_train,y_train)
accuracy = metrics.accuracy_score(y_test,log_regr.predict(X_test))
print (accuracy)
report = metrics.classification_report(y_test, log_regr.predict(X_test))
print (report)
length = len(X_train)
df['Predicted'] = log_regr.predict(X)
cum_stock_returns = np.cumsum(df[length:]['Stock_Returns'])
df['Strat_returns'] = df['Stock_Returns'] * df['Predicted'].shift(1)
cum_strat_returns = np.cumsum(df[length:]['Strat_returns'])
plt.figure(figsize=(10,5))
plt.plot(cum_stock_returns, color='r',label = 'Stock Returns')
plt.plot(cum_strat_returns, color='g', label = 'Strategy Returns')
plt.legend()
plt.show()
print(df.tail(40))