-
Notifications
You must be signed in to change notification settings - Fork 1
/
nifty_algo.py
97 lines (66 loc) · 2.37 KB
/
nifty_algo.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
import os, pandas
import pandas as pd
import streamlit as st
Consol_w = []
Break_w = []
colab_w = []
ma_w = []
tma_w = []
def is_consolidating(df, percentage=2.5):
recent_candlesticks = df[-15:]
#print(percentage)
max_close = recent_candlesticks['Close'].max()
min_close = recent_candlesticks['Close'].min()
threshold = 1 - (percentage
/ 100)
if min_close > (max_close * threshold):
return True
return False
def is_breaking_out(df, percentage=3):
last_close = df[-1:]['Close'].values[0]
if is_consolidating(df[:-1], percentage=percentage):
recent_closes = df[-16:-1]
if last_close > recent_closes['Close'].max():
return True
return False
def consolbreak_w():
for filename in os.listdir('datasets/long'):
df = pandas.read_csv('datasets/long/{}'.format(filename))
if is_consolidating(df):
consol1 = filename + " is consolidating"
Consol_w.append(consol1)
if is_breaking_out(df):
break1 = filename + " is Breaking Out"
Break_w.append(break1)
def vol_break_w():
global colab_w
colab_w = []
for filename in os.listdir('datasets/long'):
df = pandas.read_csv('datasets/long/{}'.format(filename))
recent_candlesticks = df[-30:-1]
global Volume
global last
Volume = int(recent_candlesticks['Volume'].mean())
last = df[-1:]['Volume'].values[0]
if last>Volume:
vol1 = filename + " is volume breaking Out"
colab_w.append(vol1)
def moving_avg_w():
global ma_w
ma_w = []
global tma_w
tma_w =[]
for filename in os.listdir('datasets/long'):
df = pandas.read_csv('datasets/long/{}'.format(filename))
df['100ma'] = df['Adj Close'].rolling(window=100, min_periods=0).mean()
df['200ma'] = df['Adj Close'].rolling(window=200, min_periods=0).mean()
last_close = df[-1:]['Adj Close'].values[0]
hma = df[-1:]['100ma'].values[0]
twoma = df[-1:]['200ma'].values[0]
per = (1/100)*last_close
if abs(last_close - hma) <= per:
x = filename + " is around 100 MA"
ma_w.append(x)
if abs(last_close - twoma) <= per:
y = filename + " is around 200 MA"
tma_w.append(y)