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})
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()
})
"language": "en",
"text": data
},
{
"id": "2",
"language": "en",
"text": data1
},
{
"id": "3",
"language": "en",
"text": data2
},
]
response = client.sentiment(documents=documents)
res = []
for document in response.documents:
res.append(float(format(document.score)))
res_mean = (sum(res) / 4) * 100
tech_signals = [
rs(),
ao(),
mf(),
so(),
s_o(),
tsi(),
u_o(),
w_r(),