def RSI(stock, n = 14):
prices, = get_stock_atrributes_data(stock, ['close'])
deltas = np.diff(prices)
seed = deltas[:n + 1]
up = seed[seed >= 0].sum() / n
down = -seed[seed < 0].sum() / n
rs = up / down
rsi = np.zeros_like(prices)
rsi[:n] = 100. - 100. / (1. + rs)
for i in range(n, len(prices)):
delta = deltas[i - 1] # cause the diff is 1 shorter
if delta > 0:
upval = delta
downval = 0.
else:
upval = 0.
downval = -delta
up = (up * (n - 1) + upval) / n
down = (down * (n - 1) + downval) / n
rs = up / down
rsi[i] = 100. - 100. / (1. + rs)
return rsi[n:]
def EMA(stock, N):
closingPrices, = get_stock_atrributes_data(stock, ['close'])
alpha = 2.0 / (N + 1)
curEMA = closingPrices[0]
res = [curEMA]
for p in closingPrices[1:]:
curEMA = (1 - alpha) * curEMA + alpha * p
res.append(curEMA)
return res
def test_MACD(stock):
ax = plt.subplot(111)
closingPrices, = get_stock_atrributes_data(stock, ['close'])
plt.plot(closingPrices, label='close price')
eS, eF = MACD(stock)
plt.plot(eS, label='slow')
plt.plot(eF, label='fast')
plt.legend()
plt.show()
def test_EMA(stock):
ax = plt.subplot(111)
closingPrices, = get_stock_atrributes_data(stock, ['close'])
plt.plot(closingPrices, label='close price')
plt.plot(EMA(stock, 10), label='EMA_10')
plt.plot(EMA(stock, 20), label='EMA_20')
plt.plot(EMA(stock, 100), label='EMA_100')
plt.legend()
plt.show()
def test(stock):
ax = plt.subplot(111)
closingPrices, = get_stock_atrributes_data(stock, ['close'])
plt.plot(closingPrices, label = 'close price')
plt.plot(EMA(stock, 10), label = 'EMA')
plt.plot(SMA(stock, 10), label = 'SMA')
plt.plot(RSI(stock), label = 'RSI')
plt.legend()
plt.show()
def stockFeatures(stock, lag, scaling=True):
closePrice, volume, openPrice, highPrice, lowPrice = get_stock_atrributes_data(
stock, ['close', 'volume', 'open', 'high', 'low'])
if scaling:
closePrice = scaleData(closePrice)
volume = scaleData(volume)
openPrice = scaleData(openPrice)
highPrice = scaleData(highPrice)
lowPrice = scaleData(lowPrice)
features = []
for c, v, o, h, l in zip(closePrice, volume, openPrice, highPrice,
lowPrice):
features.append([c, v, o, h, l])
features = getDataWithLag(features, lag)
res = []
for list in features:
res.append([item for sublist in list for item in sublist])
return closePrice, res
#print stockFeatures('YHOO', 5)
def SMA(stock, N):
closingPrices, = get_stock_atrributes_data(stock, ['close'])
return [np.mean(closingPrices[i: i + N]) for i in range(len(closingPrices) - N)]
def closingPriceFeature(stock, lag, scaling=True):
closingPrices, = get_stock_atrributes_data(stock, ['close'])
if scaling:
scalePrices = scaleData(closingPrices)
feature = getDataWithLag(scalePrices, lag)
return closingPrices, feature
