Python talib.SMA Examples
The following are 30 code examples for showing how to use talib.SMA(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
| Project: jesse Author: jesse-ai File: vwmacd.py License: MIT License | 7 votes |
|
def vwmacd(candles: np.ndarray, fastperiod=12, slowperiod=26, signalperiod=9, sequential=False) -> VWMACD:
"""
VWMACD - Volume Weighted Moving Average Convergence/Divergence
:param candles: np.ndarray
:param fastperiod: int - default: 12
:param slow_period: int - default: 26
:param signal_period: int - default: 9
:param sequential: bool - default: False
:return: VWMACD(macd, signal, hist)
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
vwma_slow = talib.SMA(candles[:, 2] * candles[:, 5], slowperiod) / talib.SMA(candles[:, 5], slowperiod)
vwma_fast = talib.SMA(candles[:, 2] * candles[:, 5], fastperiod) / talib.SMA(candles[:, 5], fastperiod)
vwmacd = vwma_fast - vwma_slow
signal = talib.EMA(vwmacd, signalperiod)
hist = vwmacd - signal
if sequential:
return VWMACD(vwmacd, signal, hist)
else:
return VWMACD(vwmacd[-1], signal[-1], hist[-1])
Example 2
| Project: pandas_talib Author: femtotrader File: wrapper.py License: MIT License | 6 votes |
|
def main():
basepath = os.path.dirname(__file__)
filename = os.path.join(basepath, "..", "data", "AAPL_GOOGL_IBM_20140101_20141201.xls")
d = pd.read_excel(filename, sheetname=None)
panel = pd.Panel.from_dict(d)
#print(panel.loc['Open','2014-02-03','AAPL'])
panel = panel.iloc[:,1:,:]
panel.major_axis.name = "Date"
#print(panel)
df = panel.loc[:,:,'AAPL']
#print(df)
result = SMA(df['Close'].values, timeperiod=4) # Function API
#result = SMA(df, timeperiod=4, price='Close') # Abstract API
print(result)
Example 3
| Project: technical Author: freqtrade File: indicators.py License: GNU General Public License v3.0 | 6 votes |
|
def ema(dataframe, period, field='close'):
import talib.abstract as ta
return ta.EMA(dataframe, timeperiod=period, price=field)
# HT_TRENDLINE Hilbert Transform - Instantaneous Trendline
# KAMA Kaufman Adaptive Moving Average
# MA Moving average
# MAMA MESA Adaptive Moving Average
# MAVP Moving average with variable period
# MIDPOINT MidPoint over period
# MIDPRICE Midpoint Price over period
# SAR Parabolic SAR
# SAREXT Parabolic SAR - Extended
# SMA Simple Moving Average
Example 4
| Project: technical Author: freqtrade File: indicators.py License: GNU General Public License v3.0 | 6 votes |
|
def osc(dataframe, periods=14) -> ndarray:
"""
1. Calculating DM (i).
If HIGH (i) > HIGH (i - 1), DM (i) = HIGH (i) - HIGH (i - 1), otherwise DM (i) = 0.
2. Calculating DMn (i).
If LOW (i) < LOW (i - 1), DMn (i) = LOW (i - 1) - LOW (i), otherwise DMn (i) = 0.
3. Calculating value of OSC:
OSC (i) = SMA (DM, N) / (SMA (DM, N) + SMA (DMn, N)).
:param dataframe:
:param periods:
:return:
"""
df = dataframe
df['DM'] = (df['high'] - df['high'].shift()).apply(lambda x: max(x, 0))
df['DMn'] = (df['low'].shift() - df['low']).apply(lambda x: max(x, 0))
return Series.rolling_mean(df.DM, periods) / (
Series.rolling_mean(df.DM, periods) + Series.rolling_mean(df.DMn, periods))
Example 5
| Project: jesse Author: jesse-ai File: acosc.py License: MIT License | 6 votes |
|
def acosc(candles: np.ndarray, sequential=False) -> AC:
"""
Acceleration / Deceleration Oscillator (AC)
:param candles: np.ndarray
:param sequential: bool - default=False
:return: AC(osc, change)
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
med = talib.MEDPRICE(candles[:, 3], candles[:, 4])
ao = talib.SMA(med, 5) - talib.SMA(med, 34)
res = ao - talib.SMA(ao, 5)
mom = talib.MOM(res, timeperiod=1)
if sequential:
return AC(res, mom)
else:
return AC(res[-1], mom[-1])
Example 6
| Project: jesse Author: jesse-ai File: sma.py License: MIT License | 6 votes |
|
def sma(candles: np.ndarray, period=5, source_type="close", sequential=False) -> Union[float, np.ndarray]:
"""
SMA - Simple Moving Average
:param candles: np.ndarray
:param period: int - default: 5
:param source_type: str - default: "close"
:param sequential: bool - default=False
:return: float | np.ndarray
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
source = get_candle_source(candles, source_type=source_type)
res = talib.SMA(source, timeperiod=period)
return res if sequential else res[-1]
Example 7
| Project: jesse Author: jesse-ai File: ao.py License: MIT License | 6 votes |
|
def ao(candles: np.ndarray, sequential=False) -> AO:
"""
Awesome Oscillator
:param candles: np.ndarray
:param sequential: bool - default=False
:return: AO(osc, change)
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
med = talib.MEDPRICE(candles[:, 3], candles[:, 4])
res = talib.SMA(med, 5) - talib.SMA(med, 34)
mom = talib.MOM(res, timeperiod=1)
if sequential:
return AO(res, mom)
else:
return AO(res[-1], mom[-1])
Example 8
| Project: catalyst Author: enigmampc File: talib_simple.py License: Apache License 2.0 | 6 votes |
|
def isBuy(context, analysis):
# Bullish SMA Crossover
if (getLast(analysis, 'sma_test') == 1):
# Bullish MACD
if (getLast(analysis, 'macd_test') == 1):
return True
# # Bullish Stochastics
# if(getLast(analysis, 'stoch_over_sold') == 1):
# return True
# # Bullish RSI
# if(getLast(analysis, 'rsi_over_sold') == 1):
# return True
return False
Example 9
| Project: catalyst Author: enigmampc File: talib_simple.py License: Apache License 2.0 | 6 votes |
|
def isSell(context, analysis):
# Bearish SMA Crossover
if (getLast(analysis, 'sma_test') == 0):
# Bearish MACD
if (getLast(analysis, 'macd_test') == 0):
return True
# # Bearish Stochastics
# if(getLast(analysis, 'stoch_over_bought') == 0):
# return True
# # Bearish RSI
# if(getLast(analysis, 'rsi_over_bought') == 0):
# return True
return False
Example 10
| Project: Rqalpha-myquant-learning Author: DingTobest File: ma20_60.py License: Apache License 2.0 | 6 votes |
|
def handle_bar(context, bar_dict):
prices = history_bars(context.algoInfo, context.SHORTPERIOD+1, '1d', 'close')
short_avg = talib.SMA(prices, context.SHORTPERIOD)
plot("short avg", short_avg[-1])
cur_position = context.portfolio.positions[context.algoInfo].quantity
shares = context.portfolio.cash/bar_dict[context.algoInfo].close
if short_avg[-1] > prices[-1] and short_avg[-2] < prices[-2] and cur_position > 0:
order_target_value(context.algoInfo, 0)
if short_avg[-1] < prices[-1] > 0 and short_avg[-2] > prices[-2]:
order_shares(context.algoInfo, shares)
Example 11
| Project: Rqalpha-myquant-learning Author: DingTobest File: ma20_5.py License: Apache License 2.0 | 6 votes |
|
def handle_bar(context, bar_dict):
prices = history_bars(context.algoInfo, context.SHORTPERIOD+1, '1d', 'close')
short_avg = talib.SMA(prices, context.SHORTPERIOD)
plot("short avg", short_avg[-1])
cur_position = context.portfolio.positions[context.algoInfo].quantity
shares = context.portfolio.cash/bar_dict[context.algoInfo].close
if short_avg[-1] > prices[-1] and short_avg[-2] < prices[-2] and cur_position > 0:
order_target_value(context.algoInfo, 0)
if short_avg[-1] < prices[-1] > 0 and short_avg[-2] > prices[-2]:
order_shares(context.algoInfo, shares)
Example 12
| Project: Rqalpha-myquant-learning Author: DingTobest File: ma20_20.py License: Apache License 2.0 | 6 votes |
|
def handle_bar(context, bar_dict):
prices = history_bars(context.algoInfo, context.SHORTPERIOD+1, '1d', 'close')
short_avg = talib.SMA(prices, context.SHORTPERIOD)
plot("short avg", short_avg[-1])
cur_position = context.portfolio.positions[context.algoInfo].quantity
shares = context.portfolio.cash/bar_dict[context.algoInfo].close
if short_avg[-1] > prices[-1] and short_avg[-2] < prices[-2] and cur_position > 0:
order_target_value(context.algoInfo, 0)
if short_avg[-1] < prices[-1] > 0 and short_avg[-2] > prices[-2]:
order_shares(context.algoInfo, shares)
Example 13
| Project: Rqalpha-myquant-learning Author: DingTobest File: ma20_120.py License: Apache License 2.0 | 6 votes |
|
def handle_bar(context, bar_dict):
prices = history_bars(context.algoInfo, context.SHORTPERIOD+1, '1d', 'close')
short_avg = talib.SMA(prices, context.SHORTPERIOD)
plot("short avg", short_avg[-1])
cur_position = context.portfolio.positions[context.algoInfo].quantity
shares = context.portfolio.cash/bar_dict[context.algoInfo].close
if short_avg[-1] > prices[-1] and short_avg[-2] < prices[-2] and cur_position > 0:
order_target_value(context.algoInfo, 0)
if short_avg[-1] < prices[-1] > 0 and short_avg[-2] > prices[-2]:
order_shares(context.algoInfo, shares)
Example 14
| Project: Rqalpha-myquant-learning Author: DingTobest File: ma20_250.py License: Apache License 2.0 | 6 votes |
|
def handle_bar(context, bar_dict):
prices = history_bars(context.algoInfo, context.SHORTPERIOD+1, '1d', 'close')
short_avg = talib.SMA(prices, context.SHORTPERIOD)
plot("short avg", short_avg[-1])
cur_position = context.portfolio.positions[context.algoInfo].quantity
shares = context.portfolio.cash/bar_dict[context.algoInfo].close
if short_avg[-1] > prices[-1] and short_avg[-2] < prices[-2] and cur_position > 0:
order_target_value(context.algoInfo, 0)
if short_avg[-1] < prices[-1] > 0 and short_avg[-2] > prices[-2]:
order_shares(context.algoInfo, shares)
Example 15
| Project: Rqalpha-myquant-learning Author: DingTobest File: ma20_10.py License: Apache License 2.0 | 6 votes |
|
def handle_bar(context, bar_dict):
prices = history_bars(context.algoInfo, context.SHORTPERIOD+1, '1d', 'close')
short_avg = talib.SMA(prices, context.SHORTPERIOD)
plot("short avg", short_avg[-1])
cur_position = context.portfolio.positions[context.algoInfo].quantity
shares = context.portfolio.cash/bar_dict[context.algoInfo].close
if short_avg[-1] > prices[-1] and short_avg[-2] < prices[-2] and cur_position > 0:
order_target_value(context.algoInfo, 0)
if short_avg[-1] < prices[-1] > 0 and short_avg[-2] > prices[-2]:
order_shares(context.algoInfo, shares)
Example 16
| Project: Rqalpha-myquant-learning Author: DingTobest File: ma20.py License: Apache License 2.0 | 6 votes |
|
def handle_bar(context, bar_dict):
prices = history_bars(context.algoInfo, context.SHORTPERIOD+1, '1d', 'close')
short_avg = talib.SMA(prices, context.SHORTPERIOD)
plot("short avg", short_avg[-1])
cur_position = context.portfolio.positions[context.algoInfo].quantity
shares = context.portfolio.cash/bar_dict[context.algoInfo].close
if short_avg[-1] > prices[-1] and short_avg[-2] < prices[-2] and cur_position > 0:
order_target_value(context.algoInfo, 0)
if short_avg[-1] < prices[-1] > 0 and short_avg[-2] > prices[-2]:
order_shares(context.algoInfo, shares)
Example 17
| Project: Rqalpha-myquant-learning Author: DingTobest File: timming.py License: Apache License 2.0 | 6 votes |
|
def handle_bar(context, bar_dict):
prices = history_bars(context.algoInfo, context.SHORTPERIOD+1, '1d', 'close')
short_avg = talib.SMA(prices, context.SHORTPERIOD)
plot("short avg", short_avg[-1])
cur_position = context.portfolio.positions[context.algoInfo].quantity
shares = context.portfolio.cash/bar_dict[context.algoInfo].close
if short_avg[-1] > prices[-1] and short_avg[-2] < prices[-2] and cur_position > 0:
order_target_value(context.algoInfo, 0)
if short_avg[-1] < prices[-1] > 0 and short_avg[-2] > prices[-2]:
order_shares(context.algoInfo, shares)
Example 18
| Project: Rqalpha-myquant-learning Author: DingTobest File: ma20.py License: Apache License 2.0 | 6 votes |
|
def handle_bar(context, bar_dict):
prices = history_bars(context.algoInfo, context.SHORTPERIOD+1, '1d', 'close')
short_avg = talib.SMA(prices, context.SHORTPERIOD)
plot("short avg", short_avg[-1])
cur_position = context.portfolio.positions[context.algoInfo].quantity
shares = context.portfolio.cash/bar_dict[context.algoInfo].close
if short_avg[-1] > prices[-1] and short_avg[-2] < prices[-2] and cur_position > 0:
order_target_value(context.algoInfo, 0)
if short_avg[-1] < prices[-1] > 0 and short_avg[-2] > prices[-2]:
order_shares(context.algoInfo, shares)
Example 19
| Project: Rqalpha-myquant-learning Author: DingTobest File: indexTimming.py License: Apache License 2.0 | 6 votes |
|
def stoploss(context, bar_dict):
if context.trend != 'up':
return
if context.positionIndex == 1:
prices = history_bars(context.stocks[1], 21, "1d", "close")
elif context.positionIndex == 2:
prices = history_bars(context.stocks[2], 21, "1d", "close")
else:
return
short_avg = talib.SMA(prices, 20)
if prices[-1] < short_avg[-1] and prices[-2] > short_avg[-2]:
for stock in context.portfolio.positions:
if bar_dict[stock].is_trading:
order_target_percent(stock, 0)
Example 20
| Project: pyfx Author: jmelett File: new_strategy.py License: MIT License | 6 votes |
|
def annotate_data(self, feed, timeframe):
# Get SMAs
for k, v in six.iteritems(self.sma_intervals):
feed[k] = talib.SMA(feed['closeMid'].values, v)
# Get MACD
# NOTE: talib.MACD() returns (macd, signal, hist)
feed['macd'], _, feed['macd_hist'] = talib.MACD(
feed['closeMid'].values,
fastperiod=12,
slowperiod=26,
signalperiod=9
)
# Get RSI
feed['rsi'] = talib.RSI(feed['closeMid'].values)
return feed
Example 21
| Project: OpenTrader Author: OpenTrading File: SMARecipe.py License: GNU Lesser General Public License v3.0 | 5 votes |
|
def dMakeIngredients(self, dFeeds):
"""
dMakeIngredients takes a dictionary of feeds dFeeds
with at least one key from lRequiredFeeds to work on.
It returns a dictionary of ingredients with the keys in lRequiredIngredients
and a copy of the config that it used as the key dIngredientsConfig.
"""
oC = self.oEnsureConfigObj()
assert oC is not None
iLongMa = oC['rLongMa']['iLongMa']
iShortMa = oC['rShortMa']['iShortMa']
bUseTalib = oC['rShortMa']['bUseTalib']
self.vCheckRequiredFeeds(dFeeds)
mFeedOhlc = dFeeds['mFeedOhlc']
iBeginValid = max(iLongMa, iShortMa)-1
iEndOhlc = len(mFeedOhlc)
if bUseTalib:
import talib
aShortMA = talib.SMA(mFeedOhlc.O.values, timeperiod=iShortMa)
aLongMA = talib.SMA(mFeedOhlc.O.values, timeperiod=iLongMa)
rShortMa = pandas.Series(aShortMA, name='O',
index=mFeedOhlc.O.index)
rLongMa = pandas.Series(aLongMA, name='O',
index=mFeedOhlc.O.index)
else:
rShortMa = pandas.rolling_mean(mFeedOhlc.O, iShortMa)
rLongMa = pandas.rolling_mean(mFeedOhlc.O, iLongMa)
rShortMa = rShortMa[iBeginValid:]
rLongMa = rLongMa[iBeginValid:]
mOhlc = mFeedOhlc[iBeginValid:]
self.oOm.vAppendHdf('recipe/ingredients/rShortMa', rShortMa)
self.oOm.vAppendHdf('recipe/ingredients/rLongMa', rLongMa)
self.dIngredients = dict(rShortMa=rShortMa, rLongMa=rLongMa,
mOhlc=mOhlc,
dIngredientsConfig=dict(oC))
return self.dIngredients
Example 22
| Project: vnpy_crypto Author: birforce File: market_app.py License: MIT License | 5 votes |
|
def __init__(self):
self.client = fcoin_client()
#self.client.stream.stream_depth.subscribe(self.depth)
#self.client.stream.stream_klines.subscribe(self.candle)
self.client.stream.stream_ticker.subscribe(self.ticker)
self.client.stream.stream_marketTrades.subscribe(self.trade)
self.fcoin = Fcoin()
self.fcoin.auth(config.key, config.secret)
self.buy_price = None # 买1价
self.buy_amount = None # 买1量
self.sell_price = None # 卖1价
self.sell_amount = None # 卖1量
self.ts = None # 深度更新时间
self.market_price = None # 市价
self.market_trade_list = None
self.total_bids = 0
self.total_asks = 0
self.filled_buy_order_list = []
self.order_list = defaultdict(lambda: None)
self.buy_order_id = None
self.dic_balance = defaultdict(lambda: None)
self.time_order = time.time()
self.price_list = []
self.candle_list = []
self.SMA = None
self._init_log()
# 日志初始化
Example 23
| Project: vnpy_crypto Author: birforce File: market_app.py License: MIT License | 5 votes |
|
def candle(self, data):
if len(self.candle_list) == 0:
self.candle_list = [{'timestamp': data['id'],
'open': data['open'],
'high': data['high'],
'low': data['low'],
'close': data['close'],
'volume': data['base_vol']}]
else:
last_candle = self.candle_list[-1]
if last_candle['timestamp'] == data['id']:
self.candle_list[-1] = {'timestamp': data['id'],
'open': data['open'],
'high': data['high'],
'low': data['low'],
'close': data['close'],
'volume': data['base_vol']}
else:
self.candle_list.append({'timestamp': data['id'],
'open': data['open'],
'high': data['high'],
'low': data['low'],
'close': data['close'],
'volume': data['base_vol']})
if len(self.candle_list) > 10:
self.candle_list.pop(0)
if len(self.candle_list) > 7:
close_array = np.array([item['close'] for item in self.candle_list])
self.SMA = talib.SMA(close_array, timeperiod=7)
# 市价
Example 24
| Project: vnpy_crypto Author: birforce File: wss_app.py License: MIT License | 5 votes |
|
def __init__(self):
self.client = fcoin_client()
self.client.stream.stream_depth.subscribe(self.depth)
self.client.stream.stream_klines.subscribe(self.candle)
self.client.stream.stream_ticker.subscribe(self.ticker)
self.fcoin = Fcoin()
self.fcoin.auth(config.key, config.secret)
self.buy_price = None #买1价
self.buy_amount = None #买1量
self.sell_price = None #卖1价
self.sell_amount = None #卖1量
self.ts = None #深度更新时间
self.market_price = None #市价
self.total_bids = 0
self.total_asks = 0
self.filled_buy_order_list = []
self.order_list = defaultdict(lambda: None)
self.buy_order_id = None
self.dic_balance = defaultdict(lambda: None)
self.time_order = time.time()
self.price_list = []
self.candle_list = None
self.SMA = None
self._init_log()
#日志初始化
Example 25
| Project: vnpy_crypto Author: birforce File: wss_app.py License: MIT License | 5 votes |
|
def candle(self, data):
if self.candle_list is None:
self.candle_list = [{'timestamp': data['id'],
'open': data['open'],
'high': data['high'],
'low': data['low'],
'close': data['close'],
'volume': data['base_vol']}]
else:
last_candle = self.candle_list[-1]
if last_candle['timestamp'] == data['id']:
self.candle_list[-1] = {'timestamp': data['id'],
'open': data['open'],
'high': data['high'],
'low': data['low'],
'close': data['close'],
'volume': data['base_vol']}
else:
self.candle_list.append({'timestamp': data['id'],
'open': data['open'],
'high': data['high'],
'low': data['low'],
'close': data['close'],
'volume': data['base_vol']})
if len(self.candle_list) > 10:
self.candle_list.pop(0)
if len(self.candle_list) >= 7:
close_array = np.array([item['close'] for item in self.candle_list])
self.SMA = talib.SMA(close_array, timeperiod=7)
#市价
Example 26
| Project: cbpro-trader Author: mcardillo55 File: IndicatorSubsystem.py License: GNU General Public License v3.0 | 5 votes |
|
def calculate_sma(self, period_name, closing_prices):
sma = talib.SMA(closing_prices, timeperiod=9)
self.current_indicators[period_name]['sma'] = sma[-1]
self.current_indicators[period_name]['sma_trend'] = sma[-1] - sma[-2]
Example 27
| Project: cbpro-trader Author: mcardillo55 File: IndicatorSubsystem.py License: GNU General Public License v3.0 | 5 votes |
|
def calculate_avg_volume(self, period_name, volumes):
avg_vol = talib.SMA(volumes, timeperiod=15)
self.current_indicators[period_name]['avg_volume'] = avg_vol[-1]
Example 28
| Project: QUANTAXIS Author: QUANTAXIS File: talib_series.py License: MIT License | 5 votes |
|
def SMA(Series, timeperiod=30):
return pd.Series(talib.SMA(Series.values, timeperiod), index=Series.index)
Example 29
| Project: strategy Author: myquant File: volume_mixin.py License: Apache License 2.0 | 5 votes |
|
def volume_up(self, volume):
sma = SMA(volume, timeperiod=self.short_timeperiod)
lma = SMA(volume, timeperiod=self.long_timeperiod) ## make sure last lma is a number
life = SMA(volume, timeperiod=self.life_timeperiod)
if len(sma) < 3:
return False
volume_enforce = sma[-1] > lma[-1] > life[-1]
speed_up = (volume[-1] - sma[-1]) > (sma[-1] - lma[-1]) > (lma[-1] - life[-1])
return volume_enforce and speed_up
Example 30
| Project: strategy Author: myquant File: ta_indicator_mixin.py License: Apache License 2.0 | 5 votes |
|
def sma_close(self, sym, frequency, period=30):
if not self.kbars_ready(sym, frequency):
return []
closes = self.close(sym, frequency)
ma = ta.SMA(closes, timeperiod=period)
return ma
