def test_avg_cross(self):
sma9 = indicators.SMA(self.ohlc.close, period=9)
sma21 = indicators.SMA(self.ohlc.close, period=21)
strategy = strategies.AvgCrossStrategy(self.ohlc.close, sma9.df.sma9,
sma21.df.sma21)
result = strategy.backtest(self.ohlc.close)
self.assertEqual(result.start, datetime(2020, 1, 8, 1, 30, 0))
self.assertEqual(result.end, datetime(2020, 1, 18, 11, 15, 0))
self.assertEqual(result.trades, 27)
self.assertTrue(math.isclose(result.netret, 9.8, rel_tol=1e-02))
self.assertTrue(math.isclose(result.sharpe, 0.0533, rel_tol=1e-03))
self.assertTrue(math.isclose(result.maxdrawdown, -3.94, rel_tol=1e-02))
self.assertEqual(result.maxdrawdownduration, 207)
def test_SMA(self):
sma = pd.read_csv('tests/data/BTCUSDT_15m_SMA9.csv',
index_col='time',
parse_dates=True)
mysma = indicators.SMA(self.ohlc.close, period=9)
isclose = np.isclose(sma.sma9, mysma.df.sma9, equal_nan=True)
self.assertTrue(np.all(isclose))
def drawEMA(ax, data, periods=[5, 10, 20, 30, 60, 120]):
for period in periods:
if data.__contains__('ema%d' % period):
ema = data['ema%d' % period]
elif data.__contains__('ema'):
ema = data['ema']
else:
close = data['close']
ema = ind.SMA(close, period)
ax.plot(ema, label='ema%d' % period)
def drawSMA(ax, data, periods=[5, 10, 20, 30, 60, 120]):
for period in periods:
if data.__contains__('sma_%d' % period):
sma = data['sma_%d' % period]
elif data.__contains__('sma'):
sma = data['sma']
else:
close = data['close']
sma = ind.SMA(close, period)
ax.plot(sma, label='sma_%d' % period, linewidth=1)
def __init__(self, sym='SPY', sd=dt.datetime(2008,1, 1), \
ed=dt.datetime(2009,12,31),sv=100000,verbose=False):
self.sym = sym
self.verbose = verbose
self.shares = 0
self.position = 0
self.sv = sv
self.cash = sv
self.pp = 0
#period
n = 28
chart = False
#take in, normalize data
df = ut.get_data([sym], pd.date_range(sd - dt.timedelta(100), ed))[sym]
normed = df / df.ix[0]
#get features
sma = ind.SMA(normed, n)
smap = ind.standardize(normed / sma)
bbp = ind.standardize(ind.bband(normed, n, chart)[0])
stdev = ind.standardize(normed.rolling(window=n, min_periods=n).std())
mom = ind.standardize(ind.momentum(normed, n, chart))
momvol = ind.standardize(mom / stdev)
#daily returns & combined features
rets = pd.DataFrame(df)
daily_rets = rets[sym].copy()
daily_rets[1:] = (rets[sym].ix[1:] / rets[sym].ix[:-1].values) - 1
daily_rets.ix[0] = 0
#print df
#df = pd.DataFrame(df).assign(normed = normed).assign(smap=smap).\
# assign(stdev=stdev).assign(bbp=bbp).assign(mom=mom)[sd:]
df = pd.DataFrame(df).assign(normed = normed).assign(smap=smap).\
assign(bbp=bbp).assign(mom=mom)[sd:]
#print df
daily_rets.ix[0] = 0
df = df.assign(dr=daily_rets)
#print df
self.df = df
self.market = df.iterrows()
self.curr = self.market.next()
self.action = self.Action()
#self.features = ['smap', 'stdev', 'bbp', 'mom']
self.features = ['smap', 'bbp', 'mom']
def report_of_SMA(prices, n):
'''Takes the input(a list of prices and N-day) to
print the report of simple moving average and signal strategies'''
a_list_of_SMAverage = indicators.SMA(prices, n)
CLASS = signal_strategies.SMA_SIGNAL(prices, n, a_list_of_SMAverage)
x = CLASS.simple_signal()
date_close = [[], []]
for b in information:
date_close[0].append(b[0])
date_close[1].append(b[4])
print('\nSYMBOL: ' + s + '\nSTRATEGY: Simple Moving Average')
print('\n{:10} {:6} {:9} {:5}'.format('DATE', 'CLOSE', 'INDICATOR',
'SIGNAL'))
for i in range(len(date_close[0])):
print('{:10} {:6} {:9} {:5}'.format(date_close[0][i],
date_close[1][i],
a_list_of_SMAverage[i], x[i]))
def testPolicy(symbol = "AAPL", sd=dt.datetime(2010,1,1), \
ed = dt.datetime(2011,12,31), sv=100000):
chart = False
n = 14
#symbol = [symbol]
prices = get_data(symbol, pd.date_range(sd, ed))
#print prices
#ffill and drop SPY
prices.fillna(method='ffill', inplace=True)
prices.fillna(method='bfill', inplace=True)
prices = prices[symbol]
#Generate indicators
sma = ind.SMA(prices, n)
smap = ind.standardize(prices / sma)
bbp = ind.standardize(ind.bband(prices, n, chart)[0])
stdev = ind.standardize(prices.rolling(window=n, min_periods=n).std())
mom = ind.standardize(ind.momentum(prices, n, chart))
momvol = ind.standardize(mom / stdev)
orders = prices.copy()
orders.ix[:, :] = np.nan
smap_cross = pd.DataFrame(0, index=smap.index, columns=smap.columns)
smap_cross[smap >= 1] = 1
smap_cross[1:] = smap_cross.diff()
smap_cross.ix[0] = 0
orders[(smap < 0.95) & (bbp < 0) & (stdev < 0.1) & (mom < 0)] = 1000
orders[(smap > 1.05) & (bbp > 1) & (stdev > 0.3) & (mom > 0)] = -1000
orders[(smap_cross != 0)] = 0
orders.ffill(inplace=True)
orders.fillna(0, inplace=True)
orders[1:] = orders.diff()
#orders.ix[0] = 1000
#orders.ix[-1] = -1000
#orders = orders.loc[(orders!=0).any(axis=1)]
#orders.ix[0] = 0
#orders.ix[-1] = orders.ix[-2]*-1
#print orders
return orders
def main():
parser = argparse.ArgumentParser()
parser.add_argument("file")
parser.add_argument("--sma", type=int, nargs='+', help='Adds SMA to the price graph')
parser.add_argument("--ema", type=int, nargs='+', help='Adds EMA to the price graph')
parser.add_argument("--rsi", type=int, help='Adds RSI in a subplot')
parser.add_argument("--macd", type=int, nargs=3, help='Adds MACD in a subplot')
parser.add_argument("--bbands", type=int, nargs=2, help='Adds Bollinger Bands to the price graph')
parser.add_argument("--strategy", type=int, nargs='+', help='Adds a strategy, 1 : EMA Cross')
args = parser.parse_args()
ohlc = pd.read_csv(args.file, index_col='time', parse_dates=True)
close = ohlc['close']
# calculates the number of sublots
rows = 1
if args.rsi != None:
rows += 1
if args.macd != None:
rows += 1
fig = make_subplots(rows=rows, cols=1, shared_xaxes=True)
# plots close bar graph with averages
row = 1
fig.add_trace(go.Scatter(x=ohlc.index, y=close, name="Close"), row=row, col=1)
if args.ema != None:
for period in args.ema:
ema = indicators.EMA(close, period)
fig.add_trace(go.Scatter(x=ohlc.index, y=ema.df['ema{}'.format(period)], name="EMA {}".format(period)), row=row, col=1)
if args.sma != None:
for period in args.sma:
sma = indicators.SMA(close, period)
fig.add_trace(go.Scatter(x=ohlc.index, y=sma.df['sma{}'.format(period)], name="SMA {}".format(period)), row=row, col=1)
# plots Bollinger Bands
if args.bbands != None:
bbands = indicators.BollingerBands(close, args.bbands[0])
fig.add_trace(go.Scatter(x=ohlc.index, y=bbands.df.ma), row=row, col=1)
fig.add_trace(go.Scatter(x=ohlc.index, y=bbands.df.upper), row=row, col=1)
fig.add_trace(go.Scatter(x=ohlc.index, y=bbands.df.lower), row=row, col=1)
row += 1
row += 1
# plots RSI
if args.rsi != None:
rsi = indicators.RSI(close, args.rsi)
fig.add_trace(go.Scatter(x=ohlc.index, y=rsi.df.rsi, name="RSI {}".format(args.rsi)), row=row, col=1)
row += 1
# plots MACD
if args.macd != None:
macd = indicators.MACD(close, args.macd[0], args.macd[1], args.macd[2])
fig.add_trace(go.Scatter(x=ohlc.index, y=macd.df.MACD, name="MACD {} {} {}".format(args.macd[0], args.macd[1], args.macd[2])), row=row, col=1)
fig.add_trace(go.Scatter(x=ohlc.index, y=macd.df.signal, name="MACD Signal"), row=row, col=1)
row += 1
# plots strategy
if args.strategy != None:
if args.strategy[0] == 1:
fast_ema = indicators.EMA(ohlc.close, period=args.strategy[1])
slow_ema = indicators.EMA(ohlc.close, period=args.strategy[2])
strategy = strategies.AvgCrossStrategy(ohlc.close, fast_ema.data(), slow_ema.data())
fig.add_trace(go.Scatter(x=close.loc[strategy.signals['positions'] == 1.0].index, y=close.loc[strategy.signals['positions'] == 1.0],
mode='markers', marker=dict(size=12, symbol='triangle-up', color='green'), name="Buy"), row=1, col=1)
fig.add_trace(go.Scatter(x=close.loc[strategy.signals['positions'] == -1.0].index, y=close.loc[strategy.signals['positions'] == -1.0],
mode='markers', marker=dict(size=12, symbol='triangle-down', color='red'), name="Sell"), row=1, col=1)
elif args.strategy[0] == 2:
macd = indicators.MACD(ohlc.close, args.strategy[1], args.strategy[2], args.strategy[3])
strategy = strategies.MACDStrategy(ohlc.close, macd)
fig.add_trace(go.Scatter(x=close.loc[strategy.signals['positions'] == 1.0].index, y=close.loc[strategy.signals['positions'] == 1.0],
mode='markers', marker=dict(size=12, symbol='triangle-up', color='green'), name="Buy"), row=1, col=1)
fig.add_trace(go.Scatter(x=close.loc[strategy.signals['positions'] == -1.0].index, y=close.loc[strategy.signals['positions'] == -1.0],
mode='markers', marker=dict(size=12, symbol='triangle-down', color='red'), name="Sell"), row=1, col=1)
elif args.strategy[0] == 3:
bb1 = indicators.BollingerBands(ohlc.close, 20, 1)
bb2 = indicators.BollingerBands(ohlc.close, 20, 2)
strategy = strategies.DBBStrategy(ohlc.close, bb1, bb2)
fig.add_trace(go.Scatter(x=close.loc[strategy.signals['positions'] == 1.0].index, y=close.loc[strategy.signals['positions'] == 1.0],
mode='markers', marker=dict(size=12, symbol='triangle-up', color='green'), name="Buy"), row=1, col=1)
fig.add_trace(go.Scatter(x=close.loc[strategy.signals['positions'] == -1.0].index, y=close.loc[strategy.signals['positions'] == -1.0],
mode='markers', marker=dict(size=12, symbol='triangle-down', color='red'), name="Sell"), row=1, col=1)
elif args.strategy[0] == 4:
rsi = indicators.RSI(ohlc.close, 9)
macd = indicators.MACD(ohlc.close, 12, 26, 9)
strategy = strategies.RSIMACDStrategy(ohlc.close, rsi, macd)
fig.add_trace(go.Scatter(x=close.loc[strategy.signals['positions'] == 1.0].index, y=close.loc[strategy.signals['positions'] == 1.0],
mode='markers', marker=dict(size=12, symbol='triangle-up', color='green'), name="Buy"), row=1, col=1)
fig.add_trace(go.Scatter(x=close.loc[strategy.signals['positions'] == -1.0].index, y=close.loc[strategy.signals['positions'] == -1.0],
mode='markers', marker=dict(size=12, symbol='triangle-down', color='red'), name="Sell"), row=1, col=1)
fig.show()
stock_lib.Ping()
#price_data = ReadCsv("../data/SPFB.BR-4.20_200101_200311.csv")
#float_array_type = ctypes.c_double * len(price_data)
#my_dll.BlackScholes_Foo(float_array_type(*price_data), len(price_data))
fig = plt.figure()
#ax = plt.axes(projection='3d')
price_len = 1000 # len(price_data)
x = np.arange(0, price_len)
ax = fig.add_subplot(2, num_columns, 1)
price_data = GenerateStockPrices(len(x), 1.0)
ax.plot(x, price_data)
price_ema = indicators.SMA(price_data, 3)
ax.plot(x, price_ema)
ax = fig.add_subplot(2, num_columns, 2)
stochastic_data = indicators.StochasticOscillator(price_data, 20)
ax.plot(x, stochastic_data)
stochastic_data_smoothed = indicators.EMA(stochastic_data, 0.5)
ax.plot(x, stochastic_data_smoothed)
stochastic_data_smoothed1 = indicators.SMA(stochastic_data, 3)
ax.plot(x, stochastic_data_smoothed1)
ax.grid()
#ax = fig.add_subplot(1, num_columns, 2, projection='3d')
##ax = fig.add_subplot(111, projection='3d')
#
#X = np.arange(-5, 5, 0.25)
#Y = np.arange(-5, 5, 0.25)
def testPolicy(symbol="JPM",
sd=dt.datetime(2008, 1, 1),
ed=dt.datetime(2009, 12, 31),
sv=100000):
orders_df = market.make_df_to_match_trading_days(
colnames=['Date', 'Symbol', 'Order', 'Shares'],
symbol='JPM',
sd=sd,
ed=ed)
stock_price_library = market.get_stock_prices([symbol], sd, ed)
#iterate through stock price library and short before all the drops and buy for all the gains
orders_df['Symbol'] = symbol #assign to JPM
orders_df['Order'] = "" #initialize with no orders
orders_df['Shares'] = 0.0 #initialize with no shares
orders_df['Date'] = orders_df.index
state = 0
short_price = 0.0
days_in_short = 0
#load indicators
bollinger_df = indicators.get_bollinger(symbol, sd=sd, ed=ed)
STOK_df = indicators.stochastic_oscillator_20(symbol, sd=sd, ed=ed)
SMA_df = indicators.SMA(symbol, sd=sd, ed=ed)
for row in orders_df.itertuples(index=True):
order_date = getattr(row, 'Date')
symbol = getattr(row, 'Symbol')
# print "The symbol is ", symbol
order = getattr(row, 'Order')
shares = getattr(row, 'Shares')
bollinger_high_today = bollinger_df.loc[order_date]['bb1']
bollinger_low_today = bollinger_df.loc[order_date]['bb2']
STOK_D_today = STOK_df.loc[order_date]['%D']
stock_price_today = stock_price_library.loc[order_date][symbol]
P_over_SMA_today = SMA_df.loc[order_date]['P/SMA']
total_votes = []
bb_vote = 0
bb_weight = 1
stochastic_vote = 0
stochastic_weight = 1
sma_vote = 0
sma_weight = 0.5
if stock_price_today > bollinger_high_today:
bb_vote = -1 #sell!
elif stock_price_today < bollinger_low_today:
bb_vote = 1 #buy!
if STOK_D_today < 20:
stochastic_vote = 1 #buy!
elif STOK_D_today > 80:
stochastic_vote = -1 #get out of there!
if P_over_SMA_today < 0.7:
sma_vote = -1 #looks like a selling opp
total_votes.append(bb_vote * bb_weight)
total_votes.append(stochastic_vote * stochastic_weight)
total_votes.append(sma_vote * sma_weight)
average_vote = sum(total_votes) / len(total_votes)
if state < 0:
days_in_short += 1
if state < 0 and stock_price_today > 1.2 * short_price or days_in_short > 45:
#if the price has risen 20% above our short price
#or if we have been in a short position for more than 45
#get out of our position and wait for next move.
orders_df.set_value(order_date, 'Order', 'BUY')
orders_df.set_value(order_date, 'Shares', abs(0 - state))
state = 0 #reset
days_in_short = 0
else:
if average_vote < 0: #this means I want to sell
if abs(-1000 -
state) == 0: #legality check for sale transaction
pass
else:
orders_df.set_value(order_date, 'Order', 'SELL')
orders_df.set_value(order_date, 'Shares',
abs(-1000 - state))
short_price = stock_price_today
state = -1000 #short state
if average_vote > 0: #this means I want to buy
if abs(1000 - state) == 0: #legality check for buy transaction
pass
else:
orders_df.set_value(order_date, 'Order', 'BUY')
orders_df.set_value(order_date, 'Shares',
abs(1000 - state))
state = 1000 #long state
days_in_short = 0
else:
pass
#my best policy (based on seeing the future)
return orders_df
import database_tools as dt
import indicators
import datetime
print "standard_deviation " + str(
indicators.standard_deviation("TXT", datetime.datetime.today(), 10))
print "simple_moving_average " + str(
indicators.SMA("TXT", datetime.datetime.today(), 10))
print "volatility " + str(
indicators.volatility("TXT", datetime.datetime.today(), 10))
print "WilliamsR " + str(
indicators.willamsR("TXT", datetime.datetime.today(), 10))
print "RSI " + str(indicators.RSI("TXT", datetime.datetime.today(), 10))
print "average_true_range " + str(
indicators.ATR("TXT", datetime.datetime.today(), 10))
print "EMA " + str(indicators.EMA("TXT", datetime.datetime.today(), 10))
print "bollinger_bands_customized " + str(
indicators.bollinger_bands_customized("TXT", datetime.datetime.today(),
10))
def __init__(self, sym='SPY', sd=dt.datetime(2008,1, 1), \
ed=dt.datetime(2009,12,31),sv=100000,verbose=False, experiment=False, impact=0.0):
self.sym = sym
self.verbose = verbose
self.shares = 0
self.position = 2
self.sv = sv
self.cash = sv
self.pp = 0
self.impact = impact
self.experiment = experiment
#n = 3 #period
chart = False
#take in, normalize data
df = ut.get_data([sym], pd.date_range(sd - dt.timedelta(100), ed))[sym]
#print df
#print sd, ed
normed = df / df.ix[0]
#print normed
#daily returns & combined features
rets = pd.DataFrame(df)
daily_rets = rets[sym].copy()
daily_rets[1:] = (rets[sym].ix[1:] / rets[sym].ix[:-1].values) - 1
daily_rets.ix[0] = 0
#Simple moving average
sma5 = normed.rolling(5).mean()
sma15 = normed.rolling(15).mean()
sma20 = normed.rolling(20).mean()
sma25 = normed.rolling(25).mean()
sma30 = normed.rolling(30).mean()
sma40 = normed.rolling(40).mean()
#print sma5
# Volatility
vol5 = normed.rolling(5).std()
vol10 = normed.rolling(10).std()
vol20 = normed.rolling(20).std()
vol30 = normed.rolling(30).std()
# Bollinger bands
sma_bb = normed.rolling(5).mean()
sma_bb_std = normed.rolling(5).std()
bb = (normed -
(sma_bb - 2 * sma_bb_std)) / ((sma_bb + 2 * sma_bb_std) -
(sma_bb - 2 * sma_bb_std))
# Moving average convergence/divergence
#ema12 = pd.ewma(np.asarray(normed), span=12)
#ema26 = pd.ewma(np.asarray(normed), span=26)
#macd = ema12 - ema26
# Momentum
momentum2 = normed / normed.shift(2) - 1
momentum5 = normed / normed.shift(5) - 1
momentum10 = normed / normed.shift(10) - 1
# Combine into new dataframe
df = pd.DataFrame(df).assign(sma5=normed / sma5 - 1).assign(\
bb=bb).assign(momentum2=momentum2).assign(normed=normed).\
assign(vol10=vol10).assign(vol20=vol20).assign(vol30=vol30).assign(vol10=vol10)\
.assign(vol5=vol5).assign(\
momentum5=momentum5).assign(momentum10=momentum10)[sd:]
df = df.assign(dr=daily_rets)
#print df
# Determine optimal features for states
corr_df = df.corr().abs()
corr = corr_df['dr'][:]
corr.ix[0] = 0
corr['normed'] = 0
icorr = np.asarray(corr)
scorr = icorr.argsort(
)[-4:][::-1] # select top 3 features and daily returns
scorr = scorr[1:] # remove daily returns from possible features
optimal_ftrs = []
for i in scorr:
optimal_ftrs.append(corr_df.columns[i])
if experiment:
#print "Experiment 1 ACTIVATED"
n = 14
sma = ind.SMA(normed, n)
smap = ind.standardize(normed / sma)
bbp = ind.standardize(ind.bband(normed, n, chart)[0])
stdev = ind.standardize(
normed.rolling(window=n, min_periods=n).std())
mom = ind.standardize(ind.momentum(normed, n, chart))
momvol = ind.standardize(mom / stdev)
df = pd.DataFrame(df).assign(normed=normed).assign(
smap=smap).assign(stdev=stdev).assign(bbp=bbp).assign(
mom=mom)[sd:]
optimal_ftrs = ['smap', 'stdev', 'bbp', 'mom']
#df = pd.DataFrame(df).assign(normed = normed).assign(smap=smap).assign(bbp=bbp).assign(mom=mom)[sd:]
#df = pd.DataFrame(df).assign(normed = normed)[sd:]
self.df = df
self.market = df.iterrows()
self.curr = self.market.next()
self.action = self.Action()
self.features = optimal_ftrs