def generate_indicator(ticker):
# load the content from file
f = 'data/' + ticker + '.txt'
tickerData = mlab.csv2rec(f)
tickerData.sort()
prices = tickerData.adj_close
volumes = tickerData.volume
# only proceed if it's not a penny stock, with high liquidity
# if
# generate the MA
ma20 = ti.moving_average(prices, 20, type='simple')
ma200 = ti.moving_average(prices, 20, type='simple')
# RSI
rsi = ti.relative_strength(prices)
# MACD
emaSlow, emaFast, macd = ti.moving_average_convergence(prices,
nslow=26,
nfast=12)
ema9 = ti.moving_average(macd, 9, type='exponential')
return [tickerData, ma20, ma200, rsi, macd, ema9]
def ma_crossover(ohlc_data):
slow_lookback = 20
fast_lookback = 10
slow_ma = ti.moving_average(ohlc_data.tail(slow_lookback + 2), 20).tail(2)
fast_ma = ti.moving_average(ohlc_data.tail(fast_lookback + 2), 10).tail(2)
if fast_ma.iloc[0, 4] < slow_ma.iloc[0, 4] and fast_ma.iloc[
1, 4] > slow_ma.iloc[1, 4]:
return "buy"
elif fast_ma.iloc[0, 4] > slow_ma.iloc[0, 4] and fast_ma.iloc[
1, 4] < slow_ma.iloc[1, 4]:
return "sell"
else:
return "none"
def check_indicator(ticker):
try:
# [tickerData,ma20,ma200,rsi,macd,ema9]=generate_indicator(ticker)
f = 'data/' + ticker + '.txt'
tickerData = mlab.csv2rec(f)
tickerData.sort()
prices = tickerData.adj_close
volumes = tickerData.volume
# only proceed if it's not a penny stock, with high liquidity, with sufficient records
if statistics.mean(volumes[-10:]) < 150000:
return False
if statistics.mean(prices[-10:]) < 1:
return False
if len(prices) < 200:
return False
# generate the MA
ma20 = ti.moving_average(prices, 20, type='simple')
ma200 = ti.moving_average(prices, 200, type='simple')
# RSI
rsi = ti.relative_strength(prices)
# MACD
emaSlow, emaFast, macd = ti.moving_average_convergence(prices,
nslow=26,
nfast=12)
ema9 = ti.moving_average(macd, 9, type='exponential')
# check RSI
RSICheck = check_rsi(rsi)
# check MACD
MACDCheck = check_macd(macd, ema9)
buySignal = RSICheck and MACDCheck
if buySignal:
1
# plot_graph.plot(tickerData,ticker)
return buySignal
except FileNotFoundError:
# print('Error with ticker '+ticker)
return False
va='top',
transform=ax1.transAxes,
fontsize=textsize)
ax1.set_title('%s daily' % ticker)
# plot the price and volume data
dx = r.adj_close - r.close
low = r.low + dx
high = r.high + dx
deltas = np.zeros_like(prices)
deltas[1:] = np.diff(prices)
up = deltas > 0
ax2.vlines(r.date[up], low[up], high[up], color='black', label='_nolegend_')
ax2.vlines(r.date[~up], low[~up], high[~up], color='black', label='_nolegend_')
ma20 = ti.moving_average(prices, 20, type='simple')
ma200 = ti.moving_average(prices, 200, type='simple')
linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)')
linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)')
last = r[-1]
s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % (
today.strftime('%d-%b-%Y'), last.open, last.high, last.low, last.close,
last.volume * 1e-6, last.close - last.open)
t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize)
props = font_manager.FontProperties(size=10)
leg = ax2.legend(loc='center left', shadow=True, fancybox=True, prop=props)
leg.get_frame().set_alpha(0.5)
def plot(r, ticker):
today = datetime.date.today()
plt.rc('axes', grid=True)
plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5)
textsize = 9
left, width = 0.1, 0.8
rect1 = [left, 0.7, width, 0.2]
rect2 = [left, 0.3, width, 0.4]
rect3 = [left, 0.1, width, 0.2]
fig = plt.figure(facecolor='white')
axescolor = '#f6f6f6'
ax1 = fig.add_axes(rect1, axisbg=axescolor)
ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)
ax2t = ax2.twinx()
ax3 = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1)
prices = r.adj_close
rsi = ti.relative_strength(prices)
fillcolor = 'darkgoldenrod'
ax1.plot(r.date, rsi, color=fillcolor)
ax1.axhline(70, color=fillcolor)
ax1.axhline(30, color=fillcolor)
ax1.fill_between(r.date,
rsi,
70,
where=(rsi >= 70),
facecolor=fillcolor,
edgecolor=fillcolor)
ax1.fill_between(r.date,
rsi,
30,
where=(rsi <= 30),
facecolor=fillcolor,
edgecolor=fillcolor)
ax1.text(0.6,
0.9,
'>70 = overbought',
va='top',
transform=ax1.transAxes,
fontsize=textsize)
ax1.text(0.6,
0.1,
'<30 = oversold',
transform=ax1.transAxes,
fontsize=textsize)
ax1.set_ylim(0, 100)
ax1.set_yticks([30, 70])
ax1.text(0.025,
0.95,
'RSI (14)',
va='top',
transform=ax1.transAxes,
fontsize=textsize)
ax1.set_title('%s daily' % ticker)
# plot the price and volume data
dx = r.adj_close - r.close
low = r.low + dx
high = r.high + dx
deltas = np.zeros_like(prices)
deltas[1:] = np.diff(prices)
up = deltas > 0
ax2.vlines(r.date[up],
low[up],
high[up],
color='black',
label='_nolegend_')
ax2.vlines(r.date[~up],
low[~up],
high[~up],
color='black',
label='_nolegend_')
ma20 = ti.moving_average(prices, 20, type='simple')
ma200 = ti.moving_average(prices, 200, type='simple')
linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)')
linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)')
last = r[-1]
s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % (
today.strftime('%d-%b-%Y'), last.open, last.high, last.low, last.close,
last.volume * 1e-6, last.close - last.open)
t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize)
props = font_manager.FontProperties(size=10)
leg = ax2.legend(loc='center left', shadow=True, fancybox=True, prop=props)
leg.get_frame().set_alpha(0.5)
volume = (r.close * r.volume) / 1e6 # dollar volume in millions
vmax = volume.max()
poly = ax2t.fill_between(r.date,
volume,
0,
label='Volume',
facecolor=fillcolor,
edgecolor=fillcolor)
ax2t.set_ylim(0, 5 * vmax)
ax2t.set_yticks([])
# compute the MACD indicator
fillcolor = 'darkslategrey'
nslow = 26
nfast = 12
nema = 9
emaslow, emafast, macd = ti.moving_average_convergence(prices,
nslow=nslow,
nfast=nfast)
ema9 = ti.moving_average(macd, nema, type='exponential')
ax3.plot(r.date, macd, color='black', lw=2)
ax3.plot(r.date, ema9, color='blue', lw=1)
ax3.fill_between(r.date,
macd - ema9,
0,
alpha=0.5,
facecolor=fillcolor,
edgecolor=fillcolor)
ax3.text(0.025,
0.95,
'MACD (%d, %d, %d)' % (nfast, nslow, nema),
va='top',
transform=ax3.transAxes,
fontsize=textsize)
# ax3.set_yticks([])
# turn off upper axis tick labels, rotate the lower ones, etc
for ax in ax1, ax2, ax2t, ax3:
if ax != ax3:
for label in ax.get_xticklabels():
label.set_visible(False)
else:
for label in ax.get_xticklabels():
label.set_rotation(30)
label.set_horizontalalignment('right')
ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')
class MyLocator(mticker.MaxNLocator):
def __init__(self, *args, **kwargs):
mticker.MaxNLocator.__init__(self, *args, **kwargs)
def __call__(self, *args, **kwargs):
return mticker.MaxNLocator.__call__(self, *args, **kwargs)
# at most 5 ticks, pruning the upper and lower so they don't overlap
# with other ticks
# ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))
# ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))
ax2.yaxis.set_major_locator(MyLocator(5, prune='both'))
ax3.yaxis.set_major_locator(MyLocator(5, prune='both'))
plt.show()
def ma(df, n):
ma_df = ti.moving_average(df, n)
return ma_df