def news_category(subscription_key):
"""NewsCategory.
This will search category news for movie and TV entertainment with safe search then verify number of results and print out category, name, url, description, published time and name of provider of the first news result.
"""
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
try:
news_result = client.news.category(category="Entertainment_MovieAndTV",
market="en-us",
safe_search="strict")
print(
"Search category news for movie and TV entertainment with safe search"
)
if news_result.value:
first_news_result = news_result.value[0]
print("News result count: {}".format(len(news_result.value)))
print("First news category: {}".format(first_news_result.category))
print("First news name: {}".format(first_news_result.name))
print("First news url: {}".format(first_news_result.url))
print("First news description: {}".format(
first_news_result.description))
print("First published time: {}".format(
first_news_result.date_published))
print("First news provider: {}".format(
first_news_result.provider[0].name))
else:
print("Didn't see any news result data..")
except Exception as err:
print("Encountered exception. {}".format(err))
def news(self, search_term="Election India"):
#import pdb;pdb.set_trace()
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
news_result = client.news.search(query=search_term,
market="en-us",
count=10)
# import pdb; pdb.set_trace()
if news_result.value:
print("Total estimated matches value: {}".format(
news_result.total_estimated_matches))
print("News result count: {}".format(len(
news_result.value)).encode("utf-8"))
result = []
for first_news_result in news_result.value:
result.append("Title: {}".format(
first_news_result.name).encode("utf-8"))
result.append("URL: {}".format(
first_news_result.url).encode("utf-8"))
result.append("Description: {}".format(
first_news_result.description).encode("utf-8"))
# print("Published time: {}".format(first_news_result.date_published).encode("utf-8"))
# print("News provider: {}".format(first_news_result.provider[0].name).encode("utf-8"))
# print('\n\n\n\n')
return result
else:
print("Didn't see any news result data..")
def news_search_with_filtering(subscription_key):
"""NewsSearchWithFilters.
This will search most recent news for (Artificial Intelligence) with freshness and sortBy parameters then verify number of results and print out totalEstimatedMatches, name, url, description, published time and name of provider of the first news result.
"""
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
try:
news_result = client.news.search(query="Artificial Intelligence",
market="en-us",
freshness="Week",
sort_by="Date")
print(
"Search most recent news for query \"Artificial Intelligence\" with freshness and sortBy"
)
if news_result.value:
first_news_result = news_result.value[0]
print("News result count: {}".format(len(news_result.value)))
print("First news name: {}".format(first_news_result.name))
print("First news url: {}".format(first_news_result.url))
print("First news description: {}".format(
first_news_result.description))
print("First published time: {}".format(
first_news_result.date_published))
print("First news provider: {}".format(
first_news_result.provider[0].name))
else:
print("Didn't see any news result data..")
except Exception as err:
print("Encountered exception. {}".format(err))
def news_search(subscription_key):
"""NewsSearch.
This will search news for (Quantum Computing) with market and count parameters then verify number of results and print out totalEstimatedMatches, name, url, description, published time and name of provider of the first news result
"""
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
try:
news_result = client.news.search(query="Quantum Computing",
market="en-us",
count=10)
print(
"Search news for query \"Quantum Computing\" with market and count"
)
if news_result.value:
first_news_result = news_result.value[0]
print("Total estimated matches value: {}".format(
news_result.total_estimated_matches))
print("News result count: {}".format(len(news_result.value)))
print("First news name: {}".format(first_news_result.name))
print("First news url: {}".format(first_news_result.url))
print("First news description: {}".format(
first_news_result.description))
print("First published time: {}".format(
first_news_result.date_published))
print("First news provider: {}".format(
first_news_result.provider[0].name))
else:
print("Didn't see any news result data..")
except Exception as err:
print("Encountered exception. {}".format(err))
def get_news_articles(headline):
subscription_key = "9b539b9076e242f6a8318a980fdc6f88"
search_term = "Trump Has 95% Approval Rating"
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
news_result = client.news.search(query=search_term,
market="en-us",
count=3)
#if news_result.value:
# first_news_result = news_result.value[0]
# print("Total estimated matches value: {}".format(
# news_result.total_estimated_matches))
# print("News result count: {}".format(len(news_result.value)))
# print("First news name: {}".format(first_news_result.name))
# print("First news url: {}".format(first_news_result.url))
# print("First news description: {}".format(first_news_result.description))
# print("First published time: {}".format(first_news_result.date_published))
# print("First news provider: {}".format(first_news_result.provider[0].name))
#else:
# print("Didn't see any news result data..")
headlines = {}
if news_result.value:
for i in range(len(news_result.value)):
headlines['headline' + str(i)] = [
news_result.value[i].name, news_result.value[i].url
]
my_json = json.dumps(headlines)
print(my_json)
def news_trending(subscription_key):
"""NewsTrending.
This will search news trending topics in Bing then verify number of results and print out name, text of query, webSearchUrl, newsSearchUrl and image Url of the first news result.
"""
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
try:
trending_topics = client.news.trending(market="en-us")
print("Search news trending topics in Bing")
if trending_topics.value:
first_topic = trending_topics.value[0]
print("News result count: {}".format(len(trending_topics.value)))
print("First topic name: {}".format(first_topic.name))
print("First topic query: {}".format(first_topic.query.text))
print("First topic image url: {}".format(first_topic.image.url))
print("First topic webSearchUrl: {}".format(
first_topic.web_search_url))
print("First topic newsSearchUrl: {}".format(
first_topic.news_search_url))
else:
print("Didn't see any topics result data..")
except Exception as err:
print("Encountered exception. {}".format(err))
def news(search_term=None):
try:
subscription_key = "a05456788ad8447d8039b0e1d75f012a"
if search_term == None:
save_speech('newsspeak')
speech = speech2text()
search_term = check(speech)
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
news_result = client.news.search(query=search_term,
market="en-us",
count=10,
freshness='Week')
closer = False
if news_result.value:
for k in news_result.value:
modular_speech(k.name)
while True:
if keyboard.is_pressed('a'):
break
if keyboard.is_pressed('s'):
modular_speech(k.description)
sleep(1)
break
if keyboard.is_pressed('d'):
closer = True
break
if keyboard.is_pressed('q'):
speak_label('goodbye')
quit()
if closer == True:
break
else:
pass
else:
save_speech("nonews")
except Exception:
pass
def test(path):
subscription_key = "9b539b9076e242f6a8318a980fdc6f88"
search_term = "Trump Has 95% Approval Rating"
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
news_result = client.news.search(query=search_term,
market="en-us",
count=3)
headlines = {}
if news_result.value:
for i in range(len(news_result.value)):
headlines['headline' + str(i)] = [
news_result.value[i].name, news_result.value[i].url
]
my_json = json.dumps(headlines)
return my_json
def news_candidate(self, search_term="Indian Election"):
#import pdb;pdb.set_trace()
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
news_result = client.news.search(query=search_term,
market="en-us",
count=3)
# import pdb; pdb.set_trace()
if news_result.value:
print("Total estimated matches value: {}".format(
news_result.total_estimated_matches))
print("News result count: {}".format(len(
news_result.value)).encode("utf-8"))
result = []
for first_news_result in news_result.value:
if len(first_news_result.name) > 100:
a = first_news_result.name[:100] + "..."
else:
a = first_news_result.name
# subresult.append("{}".format(first_news_result.name))
# subresult.append("{}".format(first_news_result.url))
result.append(a)
#result.append("{}".format(first_news_result.description))
# print("Published time: {}".format(first_news_result.date_published).encode("utf-8"))
# print("News provider: {}".format(first_news_result.provider[0].name).encode("utf-8"))
# print('\n\n\n\n')
# import pdb; pdb.set_trace()
if len(result) < 3:
while len(result) < 3:
result.append("No more news...")
# result=['No more news...','No more news...','No more news...']
return result[0], result[1], result[2]
else:
print("Didn't see any news result data..")
return ['No news found...']
# obj=NewsSearch()
# print(obj.news("dawood ibrahim criminal records"))
def bingResult(keywords, curLink):
subscription_key = "18d92d49457f44869027dcd6145d4b11"
search_term = ""
for word in keywords:
search_term += word + " "
print("SEARCHTERM:", search_term)
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
news_result = client.news.search(query=search_term,
market="en-us",
count=9)
count = 7
i = 1
newsInfo = {"title": [], "provider": [], "description": [], "URL": []}
while i < min(count, len(news_result.value)):
if news_result.value[i].url not in curLink:
print("index:", i)
cur_news_result = news_result.value[i]
newsInfo["title"].append(cur_news_result.name)
newsInfo["provider"].append(cur_news_result.provider[0].name)
newsInfo["description"].append(cur_news_result.description)
newsInfo["URL"].append(cur_news_result.url)
"""
("num", i, "news url: {}".format(first_news_result.url)),
("num", i, "news description: {}".format(first_news_result.description)),
("num", i, "published time: {}".format(first_news_result.date_published)),
("num", i, "news provider: {}".format(first_news_result.provider[0].name))})
"""
else:
count += 1
i += 1
return newsInfo
def analysis(request):
if request.method == 'GET':
form = request.GET
ticker = form.get("Ticker", "0")
co = Conames.objects.get(Co_name=ticker).Co_name
index = Conames.objects.get(Co_name=ticker).pk
Y = Conames.objects.only('ticker').get(pk=index).ticker
X = Conames.objects.only('tick').get(pk=index).tick
tick = X.upper()
start = dt.datetime(2017, 1, 1)
end = date.today()
df = web.DataReader(Y, 'yahoo', start, end)
close = df['Close']
high = df['High']
low = df['Low']
volume = df['Volume']
p = close[-1]
p1 = close[-2]
diff = ((p - p1) / p1) * 100
d = "%.2f" % diff
if diff > 0:
diff_green = "+" + d + "%"
else:
diff_green = ""
if diff < 0:
diff_red = d + "%"
else:
diff_red = ""
price = "%.2f" % p
def rs():
r = ta.rsi(close, n=14, fillna=False)
rsi = (r[-1])
if 15.1 <= rsi <= 30:
status = "Buy"
elif rsi <= 15:
status = "Strong Buy"
elif 84.9 <= rsi >= 70:
status = "Sell"
elif rsi >= 85:
status = "Strong Sell"
else:
status = "Hold"
return status
def ao():
ao = ta.ao(high, low, s=5, len=34, fillna=False)
AO = ao[-1]
A = (ao[-10:])
if 0 >= AO >= -0.5:
def order(): # For ascending
for i in range(len(A) - 1):
if A[i] - A[i + 1] > 0:
return False
return True
if order():
status1 = "Buy"
else:
status1 = "Sell"
elif 0 <= AO <= 0.5:
def order(): # For descending
for i in range(len(A) - 1):
if A[i] - A[i + 1] < 0:
return False
return True
if order():
status1 = "Sell"
else:
status1 = "Buy"
elif AO <= -1:
status1 = "Buy"
elif AO >= 1:
status1 = "Sell"
else:
status1 = "Hold"
return status1
def mf():
mfi = ta.money_flow_index(high,
low,
close,
volume,
n=14,
fillna=False)
MFI = mean(mfi[-7:])
if MFI <= 20:
status2 = "Buy"
elif MFI <= 30:
status2 = "Buy"
elif MFI >= 80:
status2 = "Sell"
elif MFI >= 70:
status2 = "Sell"
else:
status2 = "Hold"
return status2
def so():
so = ta.stoch(high, low, close, n=14, fillna=False)
SO = (so[-1])
if SO <= 20:
status3 = "Buy"
elif SO <= 30:
status3 = "Buy"
elif SO >= 80:
status3 = "Sell"
elif SO >= 70:
status3 = "Sell"
else:
status3 = "Hold"
return status3
def s_o():
sos = ta.stoch_signal(high, low, close, n=14, d_n=3, fillna=False)
SOS = (sos[-1])
if SOS <= 20:
status4 = "Buy"
elif SOS <= 30:
status4 = "Buy"
elif SOS >= 80:
status4 = "Sell"
elif SOS >= 70:
status4 = "Sell"
else:
status4 = "Hold"
return status4
def tsi():
tsi = ta.tsi(close, r=25, s=13, fillna=False)
TSI = tsi[-1]
if TSI >= 20:
status5 = "Buy"
elif 5 <= TSI <= 20:
status5 = "Buy"
elif TSI <= 5:
status5 = "Hold"
elif -20 <= TSI <= -5:
status5 = "Sell"
elif TSI >= -20:
status5 = "Sell"
return status5
def u_o():
uo = ta.uo(high,
low,
close,
s=7,
m=14,
len=28,
ws=4.0,
wm=2.0,
wl=1.0,
fillna=False)
UO = uo[-1]
if UO <= 10:
status6 = "Buy"
elif 10.1 <= UO <= 30:
status6 = "Buy"
elif 70 <= UO <= 90:
status6 = "Sell"
elif UO >= 90:
status6 = "Sell"
else:
status6 = "Hold"
return status6
def w_r():
wr = ta.wr(high, low, close, lbp=14, fillna=False)
WR = wr[-1]
if 0 >= WR >= 20:
status7 = "Sell"
elif -80 <= WR:
status7 = "Buy"
else:
status7 = "Hold"
return status7
def cm():
cmf = ta.chaikin_money_flow(high,
low,
close,
volume,
n=20,
fillna=False)
CMF = cmf[-1]
if CMF > 1.5:
vol_status_cmf = "Buy"
elif 0 <= CMF <= 1.5:
vol_status_cmf = "Buy"
elif CMF == 0:
vol_status_cmf = "Hold"
elif -1.5 <= CMF <= 0:
vol_status_cmf = "Sell"
else:
vol_status_cmf = "Sell"
return vol_status_cmf
def em():
emv = ta.ease_of_movement(high,
low,
close,
volume,
n=20,
fillna=False)
EMV = emv[-1]
if EMV >= 1.5:
vol_status_emv = "Buy"
elif -1.5 <= EMV <= 1.5:
vol_status_emv = "Hold"
else:
vol_status_emv = "Sell"
return vol_status_emv
def f_i():
fi = ta.force_index(close, volume, n=2, fillna=False)
FI = fi[-1]
if FI >= 0:
vol_status_fi = "Buy"
elif FI <= 0:
vol_status_fi = "Sell"
else:
vol_status_fi = "Hold"
return vol_status_fi
def nv():
nvi = ta.negative_volume_index(close, volume, fillna=False)
ema255 = ta.ema_indicator(close, n=255, fillna=False)
NVI = nvi[-1]
E = ema255[-1]
if NVI > E:
vol_status_nvi = "Buy"
elif NVI < E:
vol_status_nvi = "Sell"
else:
vol_status_nvi = "Hold"
return vol_status_nvi
def ob():
obv = ta.on_balance_volume(close, volume, fillna=False)
OBV = obv[-10:]
def order(): # For ascending
for i in range(len(OBV) - 1):
if OBV[i] - OBV[i + 1] > 0:
return False
return True
if order():
vol_status_obv = "Buy"
else:
vol_status_obv = "Buy"
return vol_status_obv
def adi():
add = ta.acc_dist_index(high, low, close, volume, fillna=False)
a = add[-1]
ad = add[-7:]
if a <= 1000:
def order(): # For ascending
for i in range(len(ad) - 1):
if ad[i] - ad[i + 1] > 0:
return False
return True
if order():
vol_status_add = "Buy"
else:
vol_status_add = "Sell"
else:
vol_status_add = "No signal"
return vol_status_add
def at():
atr = ta.average_true_range(high, low, close, n=14, fillna=False)
if atr[-1] >= 1.5 + mean(atr[-10:]):
vot_status_atr = "Buy"
elif atr[-1] <= mean(atr[-10:] - 1.5):
vot_status_atr = "Sell"
else:
vot_status_atr = "Hold"
return vot_status_atr
def bb():
bbhb = ta.bollinger_hband(close, n=20, ndev=2, fillna=False)
bbhb_ind = ta.bollinger_hband_indicator(close,
n=20,
ndev=2,
fillna=False)
bblb = ta.bollinger_lband(close, n=20, ndev=2, fillna=False)
bblb_ind = ta.bollinger_lband_indicator(close,
n=20,
ndev=2,
fillna=False)
bbmavg = ta.bollinger_mavg(close, n=20, fillna=False)
sub = bbhb[-1] - close[-1]
sub2 = close[-1] - bblb[-1]
if sub > sub2:
vot_status_bb = "Buy"
elif sub < sub2:
vot_status_bb = "Sell"
else:
vot_status_bb = "Hold"
return vot_status_bb
def dch():
dch = ta.donchian_channel_hband(close, n=20, fillna=False)
dchi = ta.donchian_channel_hband_indicator(close,
n=20,
fillna=False)
dcl = ta.donchian_channel_lband(close, n=20, fillna=False)
dcli = ta.donchian_channel_lband_indicator(close,
n=20,
fillna=False)
if close[-1] == dch[-1]:
vot_status_dc = "Strong Sell"
elif dch[-1] > close[-1] > dch[-1] - 2:
vot_status_dc = "Sell"
elif dcl[-1] == close[-1]:
vot_status_dc = "Strong Buy"
elif dcl[-1] < close[-1] <= dcl[-1] + 2:
vot_status_dc = "Buy"
else:
vot_status_dc = "Hold"
return vot_status_dc
def adx():
adx = ta.adx(high, low, close, n=14, fillna=False)
adxn = ta.adx_neg(high, low, close, n=14, fillna=False)
adxp = ta.adx_pos(high, low, close, n=14, fillna=False)
if adxp[-1] > adxn[-1]:
trn_adx_status = " Buy"
elif adxp[-1] < adxn[-1]:
trn_adx_status = " Sell"
else:
trn_adx_status = " Hold"
return trn_adx_status
def ai():
aid = ta.aroon_down(close, n=25, fillna=False)
aiu = ta.aroon_up(close, n=25, fillna=False)
if aiu[-1] > aid[-1]:
trn_ai_status = "Buy"
elif aiu[-1] < aid[-1]:
trn_ai_status = "Sell"
else:
trn_ai_status = "Hold"
return trn_ai_status
def c():
cci = ta.cci(high, low, close, n=20, c=0.015, fillna=False)
cc = cci[-1]
if 0 <= cc <= 50:
trn_cci_status = "Buy"
elif 50.1 <= cc <= 100:
trn_cci_status = "Hold"
elif 100.1 <= cc:
trn_cci_status = "Sell"
elif -50 <= cc <= 0:
trn_cci_status = "Sell"
elif -100 <= cc <= -50.1:
trn_cci_status = "Hold"
else:
trn_cci_status = "Buy"
return trn_cci_status
def dpo():
d = ta.dpo(close, n=20, fillna=False)
do = d[-1]
if do >= 0:
trn_dpo_status = "Buy"
elif do <= 0:
trn_dpo_status = "Sell"
else:
trn_dpo_status = "Hold"
return trn_dpo_status
def ema():
em = ta.ema_indicator(close, n=12, fillna=False)
e = em[-7:]
if em[-1] < close[-1]:
def order(): # For ascending
for i in range(len(e) - 1):
if e[i] - e[i + 1] > 0:
return False
return True
if order():
trn_ema_status = "Sell"
else:
trn_ema_status = "Buy"
return trn_ema_status
elif em[-1] > close[-1]:
def order(): # For ascending
for i in range(len(e) - 1):
if e[i] - e[i + 1] > 0:
return False
return True
if order():
trn_ema_status = "Buy"
else:
trn_ema_status = "Sell"
return trn_ema_status
def ich():
ica = ta.ichimoku_a(high,
low,
n1=9,
n2=26,
visual=False,
fillna=False)
icb = ta.ichimoku_b(high,
low,
n2=26,
n3=52,
visual=False,
fillna=False)
if ica[-1] > icb[-1]:
trn_ich_status = "Buy"
elif ica[-1] < icb[-1]:
trn_ich_status = "Sell"
else:
trn_ich_status = "Hold"
return trn_ich_status
def kst():
kst = ta.kst(close,
r1=10,
r2=15,
r3=20,
r4=30,
n1=10,
n2=10,
n3=10,
n4=15,
fillna=False)
kst_sig = ta.kst_sig(close,
r1=10,
r2=15,
r3=20,
r4=30,
n1=10,
n2=10,
n3=10,
n4=15,
nsig=9,
fillna=False)
if kst[-1] < kst_sig[-1]:
trn_kst_status = "Sell"
elif kst[-1] > kst_sig[-1]:
trn_kst_status = "Buy"
else:
trn_kst_status = "Hold"
return trn_kst_status
def macd():
macd = ta.macd(close, n_fast=12, n_slow=26, fillna=False)
macd_sig = ta.macd_signal(close,
n_fast=12,
n_slow=26,
n_sign=9,
fillna=False)
if macd[-1] > macd_sig[-1]:
trn_macd_status = "Buy"
elif macd[-1] < macd_sig[-1]:
trn_macd_status = "Sell"
else:
trn_macd_status = "Hold"
return trn_macd_status
subscription_key = "d225b3f12aab446aa34af931359edbe0"
search_term = ticker
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
news_result = client.news.search(query=search_term, market="en-US")
if news_result.value:
first_news_result = news_result.value[0]
data = format(first_news_result.description)
sec_news_result = news_result.value[1]
data1 = format(sec_news_result.description)
third_news_result = news_result.value[2]
data2 = format(third_news_result.description)
fourth_news_result = news_result.value[3]
data3 = format(fourth_news_result.description)
# print("news name: {}".format(first_news_result.name))
#
# print("news description: {}".format(first_news_result.description))
else:
HttpResponse("NIL")
# --------------------------------------------
TEXT_ANALYTICS_SUBSCRIPTION_KEY = '4d1d3697dc8548b59163e2592b22beb7'
subscription_key = TEXT_ANALYTICS_SUBSCRIPTION_KEY
TEXT_ANALYTICS_ENDPOINT = 'https://analytics4sentiment.cognitiveservices.azure.com/'
endpoint = TEXT_ANALYTICS_ENDPOINT
credentials = CognitiveServicesCredentials(subscription_key)
text_analytics_client = TextAnalyticsClient(endpoint=endpoint,
credentials=credentials)
client = text_analytics_client
documents = [
{
"id": "1",
"language": "en",
"text": data
},
{
"id": "2",
"language": "en",
"text": data1
},
{
"id": "3",
"language": "en",
"text": data2
},
{
"id": "4 ",
"language": "en",
"text": data3
},
]
# print(documents)
response = client.sentiment(documents=documents)
res = []
for document in response.documents:
res.append(float(format(document.score)))
res_mean = (sum(res) / 4) * 100
if res_mean > 60:
final_sentiment_buy = "%.1f" % res_mean + "% Positive"
else:
final_sentiment_buy = ""
if res_mean < 40:
final_sentiment_sell = "%.1f" % res_mean + "% Positive"
else:
final_sentiment_sell = ""
if 40 <= res_mean <= 60:
final_sentiment_hold = "%.1f" % res_mean + "% Positive"
else:
final_sentiment_hold = ""
def final_signal():
global path, data, data1, data2, data3
tech_signals = [
rs(),
ao(),
mf(),
so(),
s_o(),
tsi(),
u_o(),
w_r(),
cm(),
em(),
f_i(),
nv(),
ob(),
adi(),
at(),
bb(),
dch(),
adx(),
ai(),
c(),
dpo(),
ema(),
ich(),
kst(),
macd(),
]
buy = tech_signals.count("Buy")
hold = tech_signals.count("Hold")
tech_score = ((buy * 4) + (hold * 2)) / 2
news_score = res_mean / 2
final_score = tech_score + news_score
if final_score >= 80:
path = 'bsh_user/Strong [email protected]'
signal = 'Strong Buy'
elif 55 <= final_score < 80:
path = 'bsh_user/[email protected]'
signal = 'Buy'
elif 45 <= final_score < 55:
path = 'bsh_user/[email protected]'
signal = 'Hold'
elif 10 < final_score < 45:
path = 'bsh_user/[email protected]'
signal = 'Sell'
elif final_score <= 10:
path = 'bsh_user/Strong [email protected]'
signal = 'Strong Sell'
return path
def signal():
global signal
tech_signals = [
rs(),
ao(),
mf(),
so(),
s_o(),
tsi(),
u_o(),
w_r(),
cm(),
em(),
f_i(),
nv(),
ob(),
adi(),
at(),
bb(),
dch(),
adx(),
ai(),
c(),
dpo(),
ema(),
ich(),
kst(),
macd(),
]
buy = tech_signals.count("Buy")
hold = tech_signals.count("Hold")
tech_score = ((buy * 4) + (hold * 2)) / 2
news_score = res_mean / 2
final_score = tech_score + news_score
if final_score >= 80:
signal = 'Strong Buy'
elif 55 <= final_score < 80:
signal = 'Buy'
elif 45 <= final_score < 55:
signal = 'Hold'
elif 10 < final_score < 45:
signal = 'Sell'
elif final_score <= 10:
signal = 'Strong Sell'
return signal
dfn = web.DataReader("^NSEI", 'yahoo', start, end)
closen = dfn['Close']
pricen1 = closen[-1]
pricen2 = closen[-2]
pdiffn = ((pricen1 - pricen2) / pricen2) * 100
diffn = "%.2f" % pdiffn
price_nifty = "%.2f" % pricen1
if pdiffn > 0:
diffn_green = "+" + diffn + "%"
else:
diffn_green = ""
if pdiffn < 0:
diffn_red = diffn + "%"
else:
diffn_red = ""
dfs = web.DataReader("^BSESN", 'yahoo', start, end)
closes = dfs['Close']
prices1 = closes[-1]
prices2 = closes[-2]
pdiffs = ((prices1 - prices2) / prices2) * 100
diffs = "%.2f" % pdiffs
price_sensex = "%.2f" % prices1
if pdiffs >= 0:
diffs_green = "+" + diffs + "%"
else:
diffs_green = ""
if pdiffs <= 0:
diffs_red = diffs + "%"
else:
diffs_red = ""
dfnb = web.DataReader("VAKRANGEE.NS", 'yahoo', start, end)
closenb = dfnb['Close']
pricenb1 = closenb[-1]
pricenb2 = closenb[-2]
pdiffnb = ((pricenb1 - pricenb2) / pricenb2) * 100
diffnb = "%.2f" % pdiffnb
if pdiffnb > 0:
diffnb_green = "+" + diffnb + "%"
else:
diffnb_green = ""
if pdiffnb < 0:
diffnb_red = diffnb + "%"
else:
diffnb_red = ""
price_nb = "%.2f" % pricenb1
return render(
request, 'bsh_user/analysis.html', {
'x': tick,
'ticker': ticker,
"diff_green": diff_green,
'diff_red': diff_red,
'price': price,
'price_nifty': price_nifty,
'diffn_green': diffn_green,
'diffn_red': diffn_red,
'price_sensex': price_sensex,
'diffs_red': diffs_red,
'diffs_green': diffs_green,
'price_nb': price_nb,
'diffnb_green': diffnb_green,
'diffnb_red': diffnb_red,
'name': "Vakrangee Ltd.",
"news_buy": final_sentiment_buy,
"news_sell": final_sentiment_sell,
"news_hold": final_sentiment_hold,
'mom_rsi': rs(),
'mom_ao': ao(),
'mom_mf': mf(),
'mom_so': so(),
'mom_sos': s_o(),
'mom_tsi': tsi(),
'mom_uo': u_o(),
'mom_wr': w_r(),
'vol_cm': cm(),
'vol_em': em(),
'vol_fi': f_i(),
'vol_nv': nv(),
'vol_ob': ob(),
'vol_adi': adi(),
'vot_atr': at(),
'vot_bb': bb(),
'vot_dch': dch(),
'trn_adx': adx(),
'trn_ai': ai(),
'trn_cci': c(),
'trn_dpo': dpo(),
'trn_ema': ema(),
'trn_ich': ich(),
'trn_kst': kst(),
'trn_macd': macd(),
'score': final_signal(),
'signal': signal()
})
def homepage(request):
global x, rows, signal
start = dt.datetime(2017, 1, 1)
end = date.today()
dfn = web.DataReader("^NSEI", 'yahoo', start, end)
closen = dfn['Close']
pricen1 = closen[-1]
pricen2 = closen[-2]
pdiffn = ((pricen1 - pricen2) / pricen2) * 100
diffn = "%.2f" % pdiffn
price_nifty = "%.2f" % pricen1
if pdiffn > 0:
diffn_green = "+" + diffn + "%"
else:
diffn_green = ""
if pdiffn < 0:
diffn_red = diffn + "%"
else:
diffn_red = ""
dfs = web.DataReader("^BSESN", 'yahoo', start, end)
closes = dfs['Close']
prices1 = closes[-1]
prices2 = closes[-2]
pdiffs = ((prices1 - prices2) / prices2) * 100
diffs = "%.2f" % pdiffs
price_sensex = "%.2f" % prices1
if pdiffs >= 0:
diffs_green = "+" + diffs + "%"
else:
diffs_green = ""
if pdiffs <= 0:
diffs_red = diffs + "%"
else:
diffs_red = ""
dfnb = web.DataReader("^NSEBANK", 'yahoo', start, end)
closenb = dfnb['Close']
pricenb1 = closenb[-1]
pricenb2 = closenb[-2]
pdiffnb = ((pricenb1 - pricenb2) / pricenb2) * 100
diffnb = "%.2f" % pdiffnb
if pdiffnb > 0:
diffnb_green = "+" + diffnb + "%"
else:
diffnb_green = ""
if pdiffnb < 0:
diffnb_red = diffnb + "%"
else:
diffnb_red = ""
price_nb = "%.2f" % pricenb1
dfbm = web.DataReader("BSE-MIDCAP.BO", 'yahoo', start, end)
closebm = dfbm['Close']
pricebm1 = closebm[-1]
pricebm2 = closebm[-2]
pdiffbm = ((pricebm1 - pricebm2) / pricebm2) * 100
diffbm = "%.2f" % pdiffbm
if pdiffbm > 0:
diffbm_green = "+" + diffbm + "%"
else:
diffbm_green = ""
if pdiffbm < 0:
diffbm_red = diffbm + "%"
else:
diffbm_red = ""
price_bm = "%.2f" % pricebm1
tickers = ['LT.BO', 'ITC.NS', 'RELIANCE.NS', 'WIPRO.BO', 'TCS.NS']
stocks = ["Larsen & Toubro", "ITC Limited", "Reliance Industries", "Wipro", "TATA Consultancy Services"]
closeps = []
x = []
price_diff_green = []
price_diff_red = []
hpbar = []
signal = []
for ticker in tickers:
start = dt.datetime(2019, 1, 1)
end = date.today()
df = web.DataReader(ticker, 'yahoo', start, end)
close = df['Close']
price_diff = ((close[-1] - close[-2]) / close[-2]) * 100
if price_diff > 0:
price_diff_green.append("+" + "%.2f" % price_diff + "%")
else:
price_diff_green.append("")
if price_diff < 0:
price_diff_red.append("%.2f" % price_diff + "%")
else:
price_diff_red.append("")
closeps.append("%.2f" % close[-1])
x.append(Homepage_db.objects.only('hco_logo').get(htic_name=ticker).hco_logo)
subscription_key = "d225b3f12aab446aa34af931359edbe0"
search_term = ticker
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
news_result = client.news.search(query=search_term, market="en-us", count=10)
if news_result.value:
first_news_result = news_result.value[0]
data = format(first_news_result.description)
# print("news name: {}".format(first_news_result.name))
#
# print("news description: {}".format(first_news_result.description))
else:
HttpResponse("NIL")
# --------------------------------------------
TEXT_ANALYTICS_SUBSCRIPTION_KEY = '4d1d3697dc8548b59163e2592b22beb7'
subscription_key = TEXT_ANALYTICS_SUBSCRIPTION_KEY
TEXT_ANALYTICS_ENDPOINT = 'https://analytics4sentiment.cognitiveservices.azure.com/'
endpoint = TEXT_ANALYTICS_ENDPOINT
credentials = CognitiveServicesCredentials(subscription_key)
text_analytics_client = TextAnalyticsClient(
endpoint=endpoint, credentials=credentials)
client = text_analytics_client
documents = [
{"id": "1", "language": "en", "text": data},
]
response = client.sentiment(documents=documents)
res = []
for document in response.documents:
res.append(float(format(document.score)))
res_mean = (sum(res) * 100)
if res_mean > 80:
hpbar.append("StrongBuy_hpbar.svg")
signal.append("Strong Buy")
elif 80 > res_mean > 60:
hpbar.append("Buy_hpbar.svg")
signal.append("Buy")
elif 60 > res_mean > 35:
hpbar.append("Hold_hpbar.svg")
signal.append("Hold")
elif 20 > res_mean > 35:
hpbar.append("Sell_hpbar.svg")
signal.append("Sell")
else:
hpbar.append("StrongSell_hpbar.svg")
signal.append("Strong Sell")
rows = zip(x, closeps, price_diff_green, price_diff_red, hpbar, signal, stocks)
return render(request, 'homepage/homepage.html',
{"rows": rows, 'price_nifty': price_nifty, 'diffn_green': diffn_green, 'diffn_red': diffn_red,
'price_sensex': price_sensex,
'diffs_red': diffs_red, 'diffs_green': diffs_green, 'price_nb': price_nb,
'diffnb_green': diffnb_green,
'diffnb_red': diffnb_red, 'price_bm': price_bm, 'diffbm_green': diffbm_green,
'diffbm_red': diffbm_red})
from newspaper import Article
import pandas as pd
import datetime
import glob
import nltk
nltk.download('gutenberg')
from pprint import pprint
from nltk.tokenize.punkt import PunktSentenceTokenizer, PunktTrainer
from nltk.corpus import gutenberg
subscription_key = "8a4ebe4976ee4e24935b3a1ae0a56cfb"
search_term = "Canada Elections"
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
def Create_news_df(news_result):
news_df = pd.DataFrame(columns=[
"Headline", "URL", "Category", "Description", "Date_published",
"Provider"
])
for i in range(len(news_result.value)):
if news_result.value:
news = news_result.value[i]
new_dict = dict()
new_dict = {
"Headline": news.name,
"URL": news.url,
"Category": news.category,
import threading
import time
from collections import deque
from datetime import datetime
from azure.cognitiveservices.search.newssearch import NewsSearchAPI
from django.conf import settings
from msrest.authentication import CognitiveServicesCredentials
log = logging.getLogger(__name__)
# Process-safe queue for queries to be requested to Bing API.
query_queue = multiprocessing.Manager().Queue()
# Bing API client which takes care of the authentication via the API KEY
client = NewsSearchAPI(CognitiveServicesCredentials(settings.AZURE_API_KEY))
def start():
"""
Spawns the infinite loop dispatcher process that will consume the query queue
Calls also the initial queue filler to have some data to play with
"""
proc = multiprocessing.Process(target=dispatcher)
proc.start()
time.sleep(1)
if not proc.is_alive():
log.error("Problem starting fetcher process")
n_fast=12,
n_slow=26,
n_sign=9,
fillna=False)
if macd[-1] > macd_sig[-1]:
trn_macd_status = "Buy"
elif macd[-1] < macd_sig[-1]:
trn_macd_status = "Sell"
else:
trn_macd_status = "Hold"
return trn_macd_status
subscription_key = "d225b3f12aab446aa34af931359edbe0"
search_term = tickers
date = 2018, 1, 1
client = NewsSearchAPI(CognitiveServicesCredentials(subscription_key))
news_result = client.news.search(query=search_term)
if news_result.value:
first_news_result = news_result.value[2]
data = format(first_news_result.description)
sec_news_result = news_result.value[3]
data1 = format(sec_news_result.description)
third_news_result = news_result.value[4]
data2 = format(third_news_result.description)
# print("news name: {}".format(first_news_result.name))
#
# print("news description: {}".format(first_news_result.description))
