Python talib.SMA屬性代碼示例

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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) 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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)) 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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]) 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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]) 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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] 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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]) 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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) 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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) 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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  # 買1價
        self.buy_amount = None  # 買1量
        self.sell_price = None  # 賣1價
        self.sell_amount = None  # 賣1量
        self.ts = None  # 深度更新時間

        self.market_price = None  # 市價
        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()


    # 日誌初始化 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import SMA [as 別名]
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)

    # 市價