def test_run():
Traindates = pd.date_range('2008-1-1', '2009-12-31')
TestDates = pd.date_range('2010-1-1', '2011-12-31')
syms = ['AAPL']
prices_train = get_data(syms, Traindates)
prices_test = get_data(syms, TestDates)
prices_train = prices_train[syms]
prices_test = prices_test[syms]
bb_train = BB(prices_train, lookback=20)
bb_train = bb_train.fillna(method='bfill')
bb_test = BB(prices_test, lookback=20)
bb_test = bb_test.fillna(method='bfill')
macd_train = MACD(prices_train)
macd_test = MACD(prices_test)
rsi_train = RSI(prices_train, lookback=14)
rsi_test = RSI(prices_test, lookback=14)
df_train = pd.concat([prices_train, bb_train, macd_train, rsi_train],
axis=1)
df_train = df_train.fillna(method='bfill')
df_test = pd.concat([prices_test, bb_test, macd_test, rsi_test], axis=1)
df_test = df_test.fillna(method='bfill')
indicators_train = pd.DataFrame(columns=[
'MACD_ZERO', 'MACD_SIGNAL', 'ACROSS_BAND', 'BB_value', 'RSI',
'Decision'
])
indicators_test = pd.DataFrame(columns=[
'MACD_ZERO', 'MACD_SIGNAL', 'ACROSS_BAND', 'BB_value', 'RSI',
'Decision'
])
indicators_train['BB_value'] = (df_train[syms[0]] - bb_train['Middle Band']
) / (bb_train['Upper Band'] -
bb_train['Middle Band'])
indicators_train['MACD_ZERO'] = macd_train['MACD Line']
indicators_train['MACD_SIGNAL'] = macd_train['Signal Line']
indicators_train['ACROSS_BAND'] = bb_train['Middle Band']
indicators_train['RSI'] = rsi_train['RSI']
indicators_train = (indicators_train - indicators_train.mean(axis=0)
) / indicators_train.std(axis=0)
indicators_test['BB_value'] = (
df_test[syms[0]] - bb_test['Middle Band']) / (bb_test['Upper Band'] -
bb_test['Middle Band'])
indicators_test['MACD_ZERO'] = macd_test['MACD Line']
indicators_test['MACD_SIGNAL'] = macd_test['Signal Line']
indicators_test['ACROSS_BAND'] = bb_test['Middle Band']
indicators_test['RSI'] = rsi_test['RSI']
indicators_test = (indicators_test - indicators_test.mean(axis=0)
) / indicators_test.std(axis=0)
YBUY = 0.05
YSELL = -0.05
for (i_row, row), (i_future,
future) in zip(df_train.iterrows(),
df_train.shift(-21).iterrows()):
if (future[syms[0]] / row[syms[0]] - 1
) > YBUY: # if 21 days return exceed YBUY, then BUY Decision=1
indicators_train.ix[i_row, 'Decision'] = 1
if (
future[syms[0]] / row[syms[0]] - 1
) < YSELL: # if 21 days return less than YSELL, then SELL Decision=-1
indicators_train.ix[i_row, 'Decision'] = -1
indicators_train = indicators_train.fillna(0)
for (i_row, row), (i_future, future) in zip(df_test.iterrows(),
df_test.shift(-21).iterrows()):
if (future[syms[0]] / row[syms[0]] - 1
) > YBUY: # if 21 days return exceed YBUY, then BUY Decision=1
indicators_test.ix[i_row, 'Decision'] = 1
if (
future[syms[0]] / row[syms[0]] - 1
) < YSELL: # if 21 days return less than YSELL, then SELL Decision=-1
indicators_test.ix[i_row, 'Decision'] = -1
indicators_test = indicators_test.fillna(0)
#print indicators
file_dir_train = os.path.join('orders', 'indicators_train.csv')
file_dir_test = os.path.join('orders', 'indicators_test.csv')
indicators_train.to_csv(file_dir_train, header=False, index=False)
indicators_test.to_csv(file_dir_test, header=False, index=False)
inf_train = open('orders/indicators_train.csv')
inf_test = open('orders/indicators_test.csv')
data_train = np.array(
[map(float,
s.strip().split(',')) for s in inf_train.readlines()])
data_test = np.array(
[map(float,
s.strip().split(',')) for s in inf_test.readlines()])
predY_list = [0] * data_test.shape[0]
for i in range(10): # bag 10 times
learner = dt.DTclass(leaf_size=5, verbose=False)
train_rows = int(round(1.0 * data_train.shape[0]))
test_rows = int(round(1.0 * data_test.shape[0]))
Xtrain = data_train[:train_rows, 0:-1]
Ytrain = data_train[:train_rows, -1]
Xtest = data_test[:train_rows, 0:-1]
Ytest = data_test[:train_rows, -1]
learner.addEvidence(Xtrain, Ytrain) # training step
predY = learner.query(Xtest) # query
predY_list = predY_list + predY
predY = predY_list / 10.0
prices_test['decision'] = predY
#print prices_port
orders = pd.DataFrame(columns=['Date', 'Symbol', 'Order', 'Shares'])
holding = 0 # holding =200 long; holding=-200 short
window = date.datetime(2008, 1, 1) # window should more than 21
for (i_row, row) in prices_test.iterrows():
if row['decision'] < 0 and (i_row -
window).days > 21 and holding > -200:
orders.loc[len(orders)] = [
i_row, syms[0], 'SELL',
str(200 + holding)
]
holding -= 200 + holding
window = i_row
plt.axvline(i_row, color='r')
if row['decision'] > 0 and (i_row -
window).days > 21 and holding < 200:
orders.loc[len(orders)] = [
i_row, syms[0], 'BUY',
str(200 - holding)
] # max buy
holding += 200 - holding
window = i_row
plt.axvline(i_row, color='g')
file_dir = os.path.join('orders', 'orders_ML_test.csv')
orders.to_csv(file_dir)
of_ML_test = "./orders/orders_ML_test.csv"
#of = "./orders/orders.csv"
sv = 100000
#calculate benchmark of testing data
dfprices = pd.DataFrame(prices_test)
dfprices['Cash'] = sv - 200 * dfprices.ix[0, 'AAPL']
dfprices['Holding'] = 200
dfprices['Stock values'] = dfprices['Holding'] * dfprices['AAPL']
dfprices['Port_val'] = dfprices['Cash'] + dfprices['Stock values']
dfprices['Benchmark'] = dfprices['Port_val'] / dfprices['Port_val'].ix[
0, :]
benchmark = dfprices['Benchmark']
#calculate rule based on test period
orders_test = pd.DataFrame(columns=['Date', 'Symbol', 'Order', 'Shares'])
holding_RB = 0 # holding =200 long; holding=-200 short
window_RB = date.datetime(2010, 1, 1) # window should more than 21
for (i_row, row), (i_prev, prev) in zip(df_test.iterrows(),
df_test.shift(1).iterrows()):
if prev['MACD Line'] >= 0 and row['MACD Line'] < 0 and (
i_row - window_RB).days > 21 and holding_RB > -200:
orders_test.loc[len(orders_test)] = [
i_row, syms[0], 'SELL',
str(200 + holding_RB)
]
holding_RB -= 200 + holding_RB
window_RB = i_row
if prev['MACD Line'] <= 0 and row['MACD Line'] > 0 and (
i_row - window_RB).days > 21 and holding_RB < 200:
orders_test.loc[len(orders_test)] = [
i_row, syms[0], 'BUY',
str(200 - holding_RB)
] # max buy
holding_RB += 200 - holding_RB
window_RB = i_row
if prev['MACD Line'] >= prev['Signal Line'] and row['MACD Line'] < row[
'Signal Line'] and (
i_row - window_RB
).days > 21 and holding_RB > -200 and row['RSI'] > 70:
orders_test.loc[len(orders_test)] = [
i_row, syms[0], 'SELL',
str(200 + holding_RB)
]
holding_RB -= 200 + holding_RB
window_RB = i_row
if prev['MACD Line'] <= prev['Signal Line'] and row['MACD Line'] > row[
'Signal Line'] and (i_row -
window_RB).days > 21 and holding_RB < 200:
orders_test.loc[len(orders_test)] = [
i_row, syms[0], 'BUY',
str(200 - holding_RB)
] # max buy
holding_RB += 200 - holding_RB
window_RB = i_row
if prev[syms[0]] <= prev['Lower Band'] and row[
syms[0]] > row['Lower Band'] and (
i_row - window_RB
).days > 21 and holding_RB < 200: # cross up Lower Band
orders_test.loc[len(orders_test)] = [
i_row, syms[0], 'BUY',
str(200 - holding_RB)
] # max buy
holding_RB += 200 - holding_RB
window_RB = i_row
if prev[syms[0]] <= prev['Middle Band'] and row[
syms[0]] > row['Middle Band'] and (
i_row - window_RB).days > 21 and holding_RB > -200 and row[
'RSI'] > 70: # cross up Middle Band
orders_test.loc[len(orders_test)] = [
i_row, syms[0], 'SELL',
str(200 + holding_RB)
]
holding_RB -= 200 + holding_RB
window_RB = i_row
# if prev[syms[0]] >= prev['Upper Band'] and row[syms[0]] < row['Upper Band'] and (i_row-window).days>21 and holding>-200 and row['RSI']>70: # cross down Upper Band
# orders.loc[len(orders)] = [i_row, syms[0], 'SELL', str(200+holding)]
# holding -= 200+holding
# window = i_row
# plt.axvline(i_row, color='r')
if prev[syms[0]] >= prev['Middle Band'] and row[
syms[0]] < row['Middle Band'] and (
i_row - window_RB
).days > 21 and holding_RB < 200: # cross down Middle Band
orders_test.loc[len(orders_test)] = [
i_row, syms[0], 'BUY',
str(200 - holding_RB)
]
holding_RB += 200 - holding_RB
window_RB = i_row
file_dir_RB_test = os.path.join('orders', 'orders_RB_test.csv')
orders_test.to_csv(file_dir_RB_test)
portvals_RB = compute_portvals(start_date=date.datetime(2010, 1, 1),
end_date=date.datetime(2011, 12, 31),
orders_file=file_dir_RB_test,
start_val=sv)
portvals_RB = portvals_RB / portvals_RB.ix[0, :]
benchmark = benchmark / benchmark.ix[0, :]
portvals_ML = compute_portvals(start_date=date.datetime(2010, 1, 1),
end_date=date.datetime(2011, 12, 31),
orders_file=of_ML_test,
start_val=sv)
portvals_ML = portvals_ML / portvals_ML.ix[0, :]
prices_test = prices_test / prices_test.ix[0, :]
plt.plot(prices_test.index,
portvals_RB,
label='Rule-based portfolio',
color='b')
plt.plot(prices_test.index,
portvals_ML,
label='ML-based portfolio Out of Sample',
color='g')
#plt.plot(prices_port.index, prices_port, label='APPL', color='m')
plt.plot(benchmark.index, benchmark, label='Benchmark', color='k')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend(loc='upper left')
plt.show()
def test_run():
#construct indicator and decision
dates = pd.date_range('2008-1-1', '2009-12-31')
syms = ['AAPL']
prices_all = get_data(syms, dates)
prices_port = prices_all[syms] # only price of each stock in portfolio
bb = BB(prices_port, lookback=20)
bb=bb.fillna(method='bfill')
macd = MACD(prices_port)
rsi = RSI(prices_port, lookback=14)
df = pd.concat([prices_port, bb, macd, rsi], axis=1)
df = df.fillna(method='bfill')
indicators = pd.DataFrame(columns=['MACD_ZERO', 'MACD_SIGNAL', 'ACROSS_BAND', 'BB_value', 'RSI','Decision'])
indicators['BB_value'] = (df[syms[0]] - bb['Middle Band']) / (bb['Upper Band'] - bb['Middle Band'])
indicators['MACD_ZERO']=macd['MACD Line']
indicators['MACD_SIGNAL'] = macd['Signal Line']
indicators['ACROSS_BAND'] = bb['Middle Band']
indicators['RSI'] = rsi['RSI']
indicators2 = indicators[['MACD_SIGNAL', 'MACD_ZERO']]
indicators2 = (indicators2 - indicators2.mean(axis=0)) / indicators2.std(axis=0)
indicators2['Color']='k'
# construct indicators
# for (i_row, row), (i_prev, prev) in zip(df.iterrows(), df.shift(1).iterrows()):
# if prev['MACD Line'] >= 0 and row['MACD Line'] < 0: # MACD prev>0,now<0, SELL MACD_ZERO=-1
# indicators.ix[i_row,'MACD_ZERO']=-1
# if prev['MACD Line'] <= 0 and row['MACD Line'] > 0: # MACD prev<0,now>0, BUY MACD_ZERO=1
# indicators.ix[i_row, 'MACD_ZERO'] = 1
# if prev['MACD Line'] >= prev['Signal Line'] and row['MACD Line'] < row['Signal Line']: # MACD prev>Signal Line,now<Signal line, SELL MACD_Signal=-1
# indicators.ix[i_row,'MACD_SIGNAL']=-1
# if prev['MACD Line'] <= prev['Signal Line'] and row['MACD Line'] > row['Signal Line']: # MACD prev<Signal Line,now>Signal line, BUY MACD_Signal=1
# indicators.ix[i_row, 'MACD_SIGNAL'] = 1
# if prev[syms[0]] <= prev['Lower Band'] and row[syms[0]] > row['Lower Band']: # cross up Lower Band, BUY Acroo_band=1
# indicators.ix[i_row, 'ACROSS_BAND'] = 1
# if prev[syms[0]] >= prev['Upper Band'] and row[syms[0]] < row['Upper Band']: # cross down Upper Band SELL Acroo_band=-1
# indicators.ix[i_row, 'ACROSS_BAND'] = -1
# if row['RSI']>70: #RSI>70 overbought, likely to sell it
# indicators.ix[i_row, 'RSI'] = -1
# if row['RSI'] < 30: #RSI<30, oversold, likely to buy it.
# indicators.ix[i_row, 'RSI'] = 1
# construct decision
indicators = (indicators - indicators.mean(axis=0)) / indicators.std(axis=0)
YBUY=0.05
YSELL=-0.05
for (i_row, row), (i_future, future) in zip(df.iterrows(), df.shift(-21).iterrows()):
if (future[syms[0]]/row[syms[0]]-1)> YBUY: # if 21 days return exceed YBUY, then BUY Decision=1
indicators.ix[i_row, 'Decision'] = 1
indicators2.ix[i_row, 'Color'] = 'g'
if (future[syms[0]] / row[syms[0]] - 1) < YSELL: # if 21 days return less than YSELL, then SELL Decision=-1
indicators.ix[i_row, 'Decision'] = -1
indicators2.ix[i_row, 'Color'] = 'r'
indicators=indicators.fillna(0)
#print indicators
file_dir = os.path.join('orders', 'indicators.csv')
file_dir2 = os.path.join('orders', 'Training data.csv')
indicators.to_csv(file_dir, header=False, index=False)
indicators2.to_csv(file_dir2)