def calculate_boillinger_bands(length, period, date_reference, coins=None):
if not coins:
coins = all_tickers
for c in coins:
closes = db.session.query(Ticker).\
filter(and_(Ticker.date <= date_reference, Ticker.coin==c, Ticker.period==period)).\
order_by(Ticker.date.desc()).limit(length).all()
close = [v.close for v in closes][::-1]
df = pd.DataFrame(close, columns=['close'])
upper, middle, lower = tb.BBANDS(df.close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
try:
upper_band_value = TechnicalIndicator(date_reference, period, c,
'BBUpper', upper.iloc[-1])
db.session.add(upper_band_value)
lower_band_value = TechnicalIndicator(date_reference, period, c,
'BBLower', lower.iloc[-1])
db.session.add(lower_band_value)
middle_band_value = TechnicalIndicator(date_reference, period, c,
'BBMiddle', middle.iloc[-1])
db.session.add(middle_band_value)
db.session.commit()
except Exception:
db.session.rollback()
print('Already has value BBands for: ' + str(date_reference) +
' ' + str(length) + ' ' + str(c))
def calculate_atr(period, date_reference, length, coins=None):
if not coins:
coins = all_tickers
for c in coins:
closes = db.session.query(Ticker). \
filter(and_(Ticker.date <= date_reference, Ticker.coin == c, Ticker.period == period)). \
order_by(Ticker.date.desc()).all()
high = [v.high for v in closes][::-1]
low = [v.low for v in closes][::-1]
close = [v.close for v in closes][::-1]
df = pd.DataFrame(columns=['close', 'high', 'low'])
df['close'] = close
df['high'] = high
df['low'] = low
atr = tb.ATR(df.high, df.low, df.close, timeperiod=length)
try:
atr_entity = TechnicalIndicator(date_reference, period, c, 'ATR',
atr.iloc[-1])
db.session.add(atr_entity)
db.session.commit()
except Exception:
db.session.rollback()
print('Already has value ATR ' + str(date_reference))
def calculate_macd(period,
date_reference,
length_short,
length_long,
length_macd,
coins=None):
if not coins:
coins = all_tickers
for c in coins:
closes = db.session.query(Ticker). \
filter(and_(Ticker.date <= date_reference, Ticker.coin == c, Ticker.period == period)). \
order_by(Ticker.date.desc()).all()
close = [v.close for v in closes][::-1]
df = pd.DataFrame(close, columns=['close'])
macd, macdsignal, macdhist = tb.MACD(df.close,
fastperiod=length_short,
slowperiod=length_long,
signalperiod=length_macd)
try:
macd_line_entity = TechnicalIndicator(date_reference, period, c,
'MACD', macd.iloc[-1])
db.session.add(macd_line_entity)
signal_line_entity = TechnicalIndicator(date_reference, period, c,
'SIGNAL',
macdsignal.iloc[-1])
db.session.add(signal_line_entity)
histogram_entity = TechnicalIndicator(date_reference, period, c,
'HISTOGRAM',
macdhist.iloc[-1])
db.session.add(histogram_entity)
db.session.commit()
except Exception:
db.session.rollback()
print('Already has value MACDS ' + str(date_reference))
def calculate_sum_google_trends(period, date_reference, length, coins=None):
date_window = date_reference - period * length
if not coins:
coins = all_tickers
for c in coins:
google_trends = db.session.query(GoogleTrends). \
filter(and_(GoogleTrends.date <= date_reference,
GoogleTrends.date > date_window,
or_(GoogleTrends.coin == c[5:], GoogleTrends.coin == 'crypto'))). \
order_by(GoogleTrends.date.desc()).all()
coin_trend = [v.value for v in google_trends if ('USDT_' + v.coin) == c][::-1]
market_trend = [v.value for v in google_trends if v.coin == 'crypto'][::-1]
try:
trends_entity = TechnicalIndicator(date_reference, period, c, 'TREND_COIN', np.sum(coin_trend) + np.sum(market_trend))
db.session.add(trends_entity)
db.session.commit()
except Exception:
db.session.rollback()
print('Already has value GoogleTrends ' + str(date_reference))
def calculate_momentum(period, date_reference, length, coins=None):
if not coins:
coins = all_tickers
for c in coins:
closes = db.session.query(Ticker). \
filter(and_(Ticker.date <= date_reference, Ticker.coin == c, Ticker.period == period)). \
order_by(Ticker.date.desc()).all()
close = [v.close for v in closes][::-1]
df = pd.DataFrame(close, columns=['close'])
momentum = tb.MOM(df.close, timeperiod=length)
try:
momentum_entity = TechnicalIndicator(date_reference, period, c,
'MOMENTUM', momentum.iloc[-1])
db.session.add(momentum_entity)
db.session.commit()
except Exception:
db.session.rollback()
print('Already has value MOMENTUM ' + str(date_reference))
def calculate_rsi(length, period, date_reference, coins=None):
if not coins:
coins = all_tickers
for c in coins:
values = db.session.query(Ticker). \
filter(and_(Ticker.date <= date_reference, Ticker.coin == c, Ticker.period == period)). \
order_by(Ticker.date.desc()).all()
close = [v.close for v in values][::-1]
df = pd.DataFrame(columns=['close'])
df['close'] = close
rsi_value = tb.RSI(df.close, timeperiod=14)
try:
rsi_entity = TechnicalIndicator(date_reference, period, c,
'RSI' + str(length),
rsi_value.iloc[-1])
db.session.add(rsi_entity)
db.session.commit()
except Exception:
db.session.rollback()
print('Already has value RSI ' + str(date_reference) + ' ' + c +
' ' + str(length) + ' ' + str(rsi_value.iloc[-1]))
def calculate_ma(length, period, date_reference, coins=None, _type='SMA'):
if not coins:
coins = all_tickers
for c in coins:
closes = db.session.query(Ticker).\
filter(and_(Ticker.date <= date_reference, Ticker.coin==c, Ticker.period==period)).\
order_by(Ticker.date.desc()).limit(length).all()
close = [v.close for v in closes][::-1]
df = pd.DataFrame(close, columns=['close'])
if _type == 'SMA':
ma = tb.MA(df.close, timeperiod=length)
elif _type == 'EMA':
ma = tb.EMA(df.close, timeperiod=length)
try:
ma_value = TechnicalIndicator(date_reference, period, c,
_type + str(length), ma.iloc[-1])
db.session.add(ma_value)
db.session.commit()
except Exception:
db.session.rollback()
print('Already has value ' + _type + ' ' + str(date_reference) +
' ' + str(length) + ' ' + str(ma.iloc[-1]))
def calculate_obv(period, date_reference, coins=None):
if not coins:
coins = all_tickers
for c in coins:
values = db.session.query(Ticker). \
filter(and_(Ticker.date <= date_reference, Ticker.coin == c, Ticker.period == period)). \
order_by(Ticker.date.desc()).all()
close = [v.close for v in values][::-1]
volume = [v.volume for v in values][::-1]
df = pd.DataFrame(columns=['close', 'volume'])
df['close'] = close
df['volume'] = volume
real = tb.OBV(df.close, df.volume)
try:
obv_entity = TechnicalIndicator(date_reference, period, c, 'OBV', real.iloc[-1])
db.session.add(obv_entity)
db.session.commit()
except Exception:
db.session.rollback()
print('Already has value OBV ' + str(date_reference))
return