def get_indicators(self):
short_mavg = ti.sma(self.open,
int(self.strategy_value['short_window']))
medium_mavg = ti.sma(self.open,
int(self.strategy_value['medium_window']))
long = ti.sma(self.open, int(self.strategy_value['long_window']))
return short_mavg, medium_mavg, long
def on_candle_update(self):
ma1 = ti.sma(self.handler.close, period=10)
ma2 = ti.sma(self.handler.close, period=20)
if ti.crossover(ma1, ma2)[0]:
self.handler.buy()
elif ti.crossover(ma2, ma1)[0]:
self.handler.sell()
def get_moving_average_analysis(data, current_moving_average, time_period):
time_period_unit_moving_average = tulipy.sma(data, time_period)
# equalize array size
min_len_arrays = min(len(time_period_unit_moving_average), len(current_moving_average))
# compute difference between 1 unit values and others ( >0 means currently up the other one)
values_difference = (current_moving_average[-min_len_arrays:] - time_period_unit_moving_average[-min_len_arrays:])
values_difference = DataUtil.drop_nan(values_difference)
if len(values_difference):
# indexes where current_unit_moving_average crosses time_period_unit_moving_average
crossing_indexes = TrendAnalysis.get_threshold_change_indexes(values_difference, 0)
multiplier = 1 if values_difference[-1] > 0 else -1
# check at least some data crossed 0
if crossing_indexes:
normalized_data = DataUtil.normalize_data(values_difference)
current_value = min(abs(normalized_data[-1])*2, 1)
if math.isnan(current_value):
return 0
# check <= values_difference.count()-1if current value is max/min
if current_value == 0 or current_value == 1:
chances_to_be_max = TrendAnalysis.get_estimation_of_move_state_relatively_to_previous_moves_length(
crossing_indexes,
values_difference)
return multiplier*current_value*chances_to_be_max
# other case: maxima already reached => return distance to max
else:
return multiplier*current_value
# just crossed the average => neutral
return 0
def sma(self, name='sma', period=5):
if len(self.df_ohlc) > period:
self._add_column_to_ohlc(
name, ti.sma(self.df_ohlc.priceclose.values, period=period))
return True
else:
return False
def return_last_minimums_sell(stock):
firstminim = 0
firstindex = 0
closing_price_list = stock['Close'].tolist()
numpyclose = np.asarray(closing_price_list)
sma = ti.sma(numpyclose, 26)
list_of_sma = list(np.flip(sma))
minimlist = [list_of_sma[0]]
for index, value in enumerate(list_of_sma[1:-1]):
if value < list_of_sma[index] and value < list_of_sma[index + 2]:
if firstminim == 0:
firstminim = value
firstindex = index
minimlist.append(firstminim)
continue
else:
if abs((value - firstminim) /
firstminim) < 0.02 and (index - firstindex) < 10:
firstminim = value
firstindex = index
continue
else:
firstminim = value
firstindex = index
minimlist.append(firstminim)
return [minimlist, list_of_sma]
def eval_impl(self):
self.eval_note = 0
close_values = self.last_candle_data[
PriceIndexes.IND_PRICE_CLOSE.value]
sma_values = tulipy.sma(close_values, 6)
last_ma_value = sma_values[-1]
if last_ma_value == 0:
self.eval_note = 0
else:
last_price = close_values[-1]
current_ratio = last_price / last_ma_value
if current_ratio > 1:
# last_price > last_ma_value => sell ? => eval_note > 0
if current_ratio >= 2:
self.eval_note = 1
else:
self.eval_note = current_ratio - 1
elif current_ratio < 1:
# last_price < last_ma_value => buy ? => eval_note < 0
self.eval_note = -1 * (1 - current_ratio)
else:
self.eval_note = 0
self.notify_evaluator_thread_managers(self.__class__.__name__)
def draw_macd_buy(stock, name):
closing_price_list = stock['Close'].tolist()
date_list = list(stock.index)
numpyclose = np.asarray(closing_price_list)
macd1, macd2, macdhistogram = ti.macd(numpyclose, 8, 17, 9) # for buy signals it should be 8,17,9
sma = ti.sma(numpyclose, 17)
plt.figure(figsize=(15, 8))
ax1 = plt.subplot(212)
ax2 = plt.subplot(211, sharex=ax1, title=name)
numberofactivedays = len(date_list[16:])
arrangeddates = np.arange(numberofactivedays)
ax1.plot(arrangeddates, macd1, 'r')
ax1.plot(arrangeddates, macd2, 'y')
ax1.bar(arrangeddates, macdhistogram)
ax1.plot(arrangeddates, [0] * len(date_list[16:]))
ax2.plot(arrangeddates, closing_price_list[16:])
ax2.plot(arrangeddates, sma, 'r')
ax1.set_xticks(np.append(arrangeddates[0::20], arrangeddates[-1]))
ax2.set_xticks(np.append(arrangeddates[0::20], arrangeddates[-1]))
ax1.set_xticklabels([date.strftime("%Y-%m-%d") for date in date_list[16:]][0::20] +
[[date.strftime("%Y-%m-%d") for date in date_list[16:]][-1]])
ax2.set_xticklabels([date.strftime("%Y-%m-%d") for date in date_list[16:]][0::20] +
[[date.strftime("%Y-%m-%d") for date in date_list[16:]][-1]])
ax1.tick_params(rotation=30)
ax2.tick_params(rotation=30)
plt.show()
async def evaluate(self, cryptocurrency, symbol, time_frame, candle_data,
candle):
self.eval_note = commons_constants.START_PENDING_EVAL_NOTE
if len(candle_data) >= self.long_period_length:
time_units = [5, self.long_period_length]
current_moving_average = tulipy.sma(candle_data, 2)
results = [
self.get_moving_average_analysis(candle_data,
current_moving_average,
time_unit)
for time_unit in time_units
]
if len(results):
self.eval_note = numpy.mean(results)
else:
self.eval_note = commons_constants.START_PENDING_EVAL_NOTE
if self.eval_note == 0:
self.eval_note = commons_constants.START_PENDING_EVAL_NOTE
await self.evaluation_completed(
cryptocurrency,
symbol,
time_frame,
eval_time=evaluators_util.get_eval_time(full_candle=candle,
time_frame=time_frame))
async def eval_impl(self):
self.eval_note = 0
period = 6
close_values = self.last_candle_data[
PriceIndexes.IND_PRICE_CLOSE.value]
if len(close_values) > period:
sma_values = tulipy.sma(close_values, period)
last_ma_value = sma_values[-1]
if last_ma_value == 0:
self.eval_note = 0
else:
last_price = close_values[-1]
current_ratio = last_price / last_ma_value
if current_ratio > 1:
# last_price > last_ma_value => sell ? => eval_note > 0
if current_ratio >= 2:
self.eval_note = 1
else:
self.eval_note = current_ratio - 1
elif current_ratio < 1:
# last_price < last_ma_value => buy ? => eval_note < 0
self.eval_note = -1 * (1 - current_ratio)
else:
self.eval_note = 0
await self.notify_evaluator_task_managers(self.get_name())
def sma(self, data, period, ba, ohlc):
series_arr = []
for item in data[:period]:
value = round(float(item[ba][ohlc]), 5)
series_arr.append(value)
DATA = np.array(series_arr)
sma = ti.sma(DATA, period=period)
return sma
def test_sma(self):
expected = np.array([82.426, 82.738, 83.094, 83.318, 83.628,
83.778, 84.254, 84.994, 85.574, 86.218,
86.804]),
actual = ti.sma(CLOSE, period=5)
self.assertTrue(np.allclose(actual, expected))
def compute(self, data_dict):
close = data_dict.get('close')
open = data_dict.get('open')
ret = np.log(close / open)
return ti.sma(ret, self.lookback)
def test_sma(data):
arr = data["close"].to_numpy(copy=True).astype(float)
ti_sma = np.round(np.append([np.nan for i in range(9)],
ti.sma(arr, period=10)),
decimals=3)
sma = indicator.Indicate(data["close"])\
.smooth_moving_average(10)['close_sma_10']\
.astype(float)
assert np.allclose(ti_sma, sma, atol=1e-03, equal_nan=True)
def sma_potential_sell(stock):
closing_price_list = stock['Close'].tolist()
numpyclose = np.asarray(closing_price_list)
sma = ti.sma(numpyclose, 26)
if sma[-50] > sma[-5] > sma[-1] > closing_price_list[
-1] and closing_price_list[-2] > sma[-2]:
return True
else:
return False
def sma_potential_buy(stock):
closing_price_list = stock['Close'].tolist()
numpyclose = np.asarray(closing_price_list)
sma = ti.sma(numpyclose, 17)
if sma[-50] < sma[-5] < sma[-1] < closing_price_list[
-1] and closing_price_list[-2] < sma[-2]:
return True
else:
return False
def get_indicators(self):
short_window = int(self.strategy_value['short_window'])
medium_window = int(self.strategy_value['medium_window'])
long_window = int(self.strategy_value['long_window'])
ma_window_short = int(self.strategy_value['ma_window_short'])
ma_window_long = int(self.strategy_value['ma_window_long'])
ultimate = ti.ultosc(self.high, self.low, self.close, short_window,
medium_window, long_window)
ma_long = ti.sma(self.open, ma_window_long)
ma_short = ti.sma(self.open, ma_window_short)
min_size = min(ma_long.size, ma_short.size, ultimate.size)
self.signal_shift = self.open.size - min_size
cut_u = ultimate.size - min_size
cut_l = ma_long.size - min_size
cut_m = ma_short.size - min_size
ultimate = ultimate[cut_u:]
ma_long = ma_long[cut_l:]
ma_short = ma_short[cut_m:]
return ultimate, ma_short, ma_long
def test_TA_basics():
rsi_result = tulipy.rsi(DATA, period=9)
assert all(74 < v < 86 for v in rsi_result)
assert len(rsi_result) == 6
sma_result = tulipy.sma(DATA, period=9)
assert all(74 < v < 86 for v in sma_result)
assert len(sma_result) == 7
bands = tulipy.bbands(DATA, period=9, stddev=2)
assert all(80 < v < 89 for band in bands for v in band)
assert len(bands) == 3
def start():
while True:
# [
# 1499040000000, // Open time
# "0.01634790", // Open
# "0.80000000", // High
# "0.01575800", // Low
# "0.01577100", // Close
# "148976.11427815", // Volume
# 1499644799999, // Close time
# "2434.19055334", // Quote asset volume
# 308, // Number of trades
# "1756.87402397", // Taker buy base asset volume
# "28.46694368", // Taker buy quote asset volume
# "17928899.62484339" // Ignore
# ]
try:
now = datetime.datetime.now()
is_close_position()
if (now.minute == 0 and now.second == 0) or (now.minute == 30 and now.second == 10):
print(now, 'UPDATE DATA OHLC')
ohlc = client.futures_klines(symbol=MARKET, interval=Client.KLINE_INTERVAL_30MINUTE)
close = []
for row in ohlc:
close.append(float(row[4]))
ma1 = ti.sma(np.array(close), period=10)
ma2 = ti.sma(np.array(close), period=20)
if POSITION['price'] == 0:
if ti.crossover(ma1, ma2)[0]:
create_position(price=float(row[4]), side=Client.SIDE_BUY, date=now)
elif ti.crossover(ma2, ma1)[0]:
create_position(price=float(row[4]), side=Client.SIDE_SELL, date=now)
print(now, 'GET POSITION ENTRY_PRICE[{}] ROE[{}] AMOUNT[{}] SIDE[{}]'.format(POSITION['price'],
POSITION['roe'],
POSITION['amount'],
POSITION['side']))
time.sleep(1)
except:
traceback.print_exc()
def ti_supertrend(
h: np.ndarray,
l: np.ndarray,
c: np.ndarray,
period: int,
factor: float,
) -> np.ndarray:
f_atr = factor * ta.sma(ta.tr(h, l, c), period)
out_size = len(f_atr)
slicer = slice(-out_size, None)
# IMPORTANT!
h, l, c = h[slicer], l[slicer], c[slicer]
hl2 = (h + l) / 2
up = hl2 - f_atr
dn = hl2 + f_atr
st = np.empty(out_size)
dt = np.empty(out_size)
st[0] = up[0]
dt[0] = 1
for i in range(1, len(st)):
up1 = up[i - 1]
dn1 = dn[i - 1]
dt1 = dt[i - 1]
c1 = c[i - 1]
if c1 > up1 > up[i]:
up[i] = up1
if c1 < dn1 < dn[i]:
dn[i] = dn1
if (dt1 < 0 and dn1 < c[i]) or (dt1 > 0 and up1 > c[i]):
dt1 *= -1
dt[i] = dt1
if dt1 > 0:
st[i] = up[i]
else:
st[i] = dn[i]
ret = np.empty((out_size, 2))
ret[:, 0] = st
ret[:, 1] = dt
return ret
def __call__(self, Coin):
CloseValues = np.array(
[c[3] for c in Coin.Candlesticks[-(self.slow_sma_period + 20):]])
if CloseValues.shape[0] < self.slow_sma_period:
return 0
slow_SMA = ti.sma(CloseValues, period=self.slow_sma_period)[-1]
fast_SMA = ti.sma(CloseValues, period=self.fast_sma_period)[-1]
# BEAR TREND;
if slow_SMA > fast_SMA:
RSI_period = self.bear_rsi_period
score_adjust = self.bear_score_adjust
else:
RSI_period = self.bull_rsi_period
score_adjust = self.bull_score_adjust
Period = max(1, min(RSI_period, CloseValues.shape[0] - 1))
RSI = ti.rsi(CloseValues, period=Period)
return max(min(100, RSI[-1] - score_adjust), -100)
def eval_impl(self):
self.eval_note = START_PENDING_EVAL_NOTE
if len(self.data) > 1:
time_units = [5, 10]
current_moving_average = tulipy.sma(
self.data[PriceIndexes.IND_PRICE_CLOSE.value], 2)
results = [
self.get_moving_average_analysis(
self.data[PriceIndexes.IND_PRICE_CLOSE.value],
current_moving_average, i) for i in time_units
]
self.eval_note = numpy.mean(results)
if self.eval_note == 0:
self.eval_note = START_PENDING_EVAL_NOTE
async def ohlcv_callback(self, exchange: str, exchange_id: str,
cryptocurrency: str, symbol: str, time_frame, candle):
self.eval_note = 0
new_data = self.get_symbol_candles(exchange, exchange_id, symbol, time_frame). \
get_symbol_close_candles(20)
should_eval = symbol not in self.last_candle_data or \
not self._compare_data(new_data, self.last_candle_data[symbol])
self.last_candle_data[symbol] = new_data
if should_eval:
if len(self.last_candle_data[symbol]) > self.period:
self.last_moving_average_values[symbol] = tulipy.sma(self.last_candle_data[symbol],
self.period)
await self._evaluate_current_price(self.last_candle_data[symbol][-1], cryptocurrency, symbol,
evaluators_util.get_eval_time(full_candle=candle,
time_frame=time_frame))
def eval_impl(self):
self.eval_note = START_PENDING_EVAL_NOTE
long_period_length = 10
if len(self.data[PriceIndexes.IND_PRICE_CLOSE.value]) > long_period_length:
time_units = [5, long_period_length]
current_moving_average = tulipy.sma(self.data[PriceIndexes.IND_PRICE_CLOSE.value], 2)
results = [self.get_moving_average_analysis(self.data[PriceIndexes.IND_PRICE_CLOSE.value],
current_moving_average,
i)
for i in time_units]
if len(results):
self.eval_note = numpy.mean(results)
else:
self.eval_note = START_PENDING_EVAL_NOTE
if self.eval_note == 0:
self.eval_note = START_PENDING_EVAL_NOTE
def test_sma():
c = np.random.random(SIZE)
length = 14
sma = SMA(length)
for i in range(sma.offset):
nan = sma.next(c[i])
assert np.isnan(nan)
computed = ta.sma(c, length)
for i in range(len(computed)):
actual = sma.next(c[i + sma.offset])
expected = computed[i]
assert actual == approx(expected)
def save_macd_buy(stock_name):
stock = yf.download(tickers=stock_name,
interval="1wk",
period="2y",
threads=True)
stock.index = stock.index.where(~stock.index.duplicated(),
stock.index + timedelta(1))
# Remove all the NaN values
for index, row in stock.iterrows():
if math.isnan(row["Close"]) or math.isnan(row["Volume"]):
stock = stock.drop([index])
closing_price_list = stock['Close'].tolist()
date_list = list(stock.index)
numpyclose = np.asarray(closing_price_list)
macd1, macd2, macdhistogram = ti.macd(
numpyclose, 8, 17, 9) # for buy signals it should be 8,17,9
sma = ti.sma(numpyclose, 17)
plt.figure(figsize=(15, 8))
ax1 = plt.subplot(212)
ax2 = plt.subplot(211, sharex=ax1, title=stock_name)
numberofactivedays = len(date_list[16:])
arrangeddates = np.arange(numberofactivedays)
ax1.plot(arrangeddates, macd1, 'r')
ax1.plot(arrangeddates, macd2, 'y')
ax1.bar(arrangeddates, macdhistogram)
ax1.plot(arrangeddates, [0] * len(date_list[16:]))
ax1.set(ylabel='MACD')
ax2.plot(arrangeddates, closing_price_list[16:])
ax2.plot(arrangeddates, sma, 'r')
ax2.set(ylabel="Price")
ax1.set_xticks(np.append(arrangeddates[0::15], arrangeddates[-1]))
ax2.set_xticks(np.append(arrangeddates[0::15], arrangeddates[-1]))
ax1.set_xticklabels(
[date.strftime("%Y-%m-%d") for date in date_list[16:]][0::15] +
[[date.strftime("%Y-%m-%d") for date in date_list[16:]][-1]])
ax2.set_xticklabels(
[date.strftime("%Y-%m-%d") for date in date_list[16:]][0::15] +
[[date.strftime("%Y-%m-%d") for date in date_list[16:]][-1]])
ax1.tick_params(rotation=30)
ax2.tick_params(rotation=30)
plt.savefig("Support Files/" + stock_name + "-price" + ".png")
plt.close()
def test_atr():
h, l, c = np.random.random((3, SIZE))
period = 5
atr = ATR(period)
for i in range(atr.offset):
nan = atr.next(h[i], l[i], c[i])
assert np.isnan(nan)
computed = ta.sma(ta.tr(h, l, c), period)
for i in range(len(computed)):
actual = atr.next(h[i + atr.offset], l[i + atr.offset],
c[i + atr.offset])
expected = computed[i]
assert actual == approx(expected)
def calculate_financial_technical_data(comparision_type,comparision_value):
logger.debug(comparision_value)
data_list = []
fin_res=[]
for i in ohlcv_data2.keys():
data_list.append(ohlcv_data2[i][str(comparision_value)])
logger.debug(data_list)
if comparision_type == 'sma':
data = ( ti.sma(np.asarray(data_list), period=10) )
return data
elif comparision_type is 'stddev':
#dataaa = ( ti.stddev(np.asarray(data_list), period=5) )
#logger.debug(dataaa)
#data = dataaa.tolist()
fin_res = ti.stddev(np.asarray(data_list), period=5).tolist()
return fin_res
elif comparision_type is 'wma':
data = ( ti.wma(np.asarray(data_list), period=5) )
return data
elif comparision_type is 'zlema':
data = ( ti.zlema(np.asarray(data_list), period=5) )
return data
def financial_technical_analysis():
comparision_type = str(request.form.get('comparision'))
comparision_value = str(request.form.get('comparision_data'))
data_list = []
for i in ohlcv_data2.keys():
data_list.append(ohlcv_data2[i][str(comparision_value)])
if comparision_type == 'sma':
res = ( ti.sma(np.asarray(data_list), period=20) ).tolist()
print(res[-1])
return render_template('binance_tech_analysis_result.html', res = res[-1],comparision_type="Simple Moving Average",comparision_value=comparision_value.upper())
elif comparision_type == 'stddev':
res = ti.stddev(np.asarray(data_list), period=20).tolist()
print(res[-1])
return render_template('binance_tech_analysis_result.html', res = res[-1],comparision_type="Standard Deviation",comparision_value=comparision_value.upper())
elif comparision_type == 'wma':
res = ( ti.wma(np.asarray(data_list), period=20) ).tolist()
return render_template('binance_tech_analysis_result.html', res = res[-1],comparision_type="Weighted Moving Average",comparision_value=comparision_value.upper())
elif comparision_type == 'zlema':
res = ( ti.zlema(np.asarray(data_list), period=20) ).tolist()
return render_template('binance_tech_analysis_result.html', res = res[-1],comparision_type="Zero-Lag exponential moving average" ,comparision_value=comparision_value.upper())
def compute(self, data_dict):
returns = self.returns.get_numpy()
return ti.sma(returns, self.lookback)
#
# for index, row in binance_data.iterrows():
# CLOSE.append(row.Close)
# print(row.Close)
# ma1 = ti.sma(np.array(CLOSE), period=1)
# ma2 = ti.sma(np.array(CLOSE), period=2)
# print(ti.crossover(ma1, ma2))
for row in OHLC[::-1]:
# print(row['close'])
CLOSE.insert(0, row['close'])
if len(CLOSE) < 50:
continue
ma1 = ti.sma(np.array(CLOSE), period=10)
ma2 = ti.sma(np.array(CLOSE), period=20)
# print(ti.crossover(ma1, ma2))
is_close_position(row)
if POSITION['price'] > 0:
continue
if ti.crossover(ma1, ma2)[0]:
POSITION['price'] = row['close']
POSITION['side'] = 'BUY'
POSITION['date'] = row['date']
elif ti.crossover(ma2, ma1)[0]:
POSITION['price'] = row['close']
POSITION['side'] = 'SELL'
POSITION['date'] = row['date']
# print(TRADES)
