def run_clusters(strategy_class, clustering_tickers, cluster_num, epochs_num,
training_start, training_end, backtest_start, backtest_end,
is_graph, is_elbow):
""" Run the test given command-line args.
Cluster.
For each cluster, train a strategy on that cluster.
For each stock in that cluster, run a backtest.
Graph results.
"""
print "\nGathering data..."
ticker_list, raw_stock_data_list = Manager.getRawStockDataList(
clustering_tickers, training_start, training_end, 252)
normalized_stock_data_list = [
Manager.preprocessData(x) for x in raw_stock_data_list
]
print "\nClustering..."
tickers, clusters = createClusters(ticker_list, normalized_stock_data_list,
cluster_num)
print "# of stocks: " + str(len(normalized_stock_data_list))
print "# of clusters: " + str(len(clusters))
print ""
for t, c in itertools.izip(tickers, clusters):
print "\tCluster: " + str(len(c)), "stocks: ",
for symbol in t:
print symbol,
print ""
if is_graph:
graphClusters(clusters)
if is_elbow:
graphElbowMethod(normalized_stock_data_list)
for t, cluster in itertools.izip(tickers, clusters):
settings.STRATEGY_OBJECT = trainStrategy(strategy_class, cluster,
epochs_num)
for ticker in t:
print "Cluster:", t
print "Stock:", ticker
tmp_ticks, tmp_data = Manager.getRawStockDataList([ticker],
training_start,
training_end,
252)
settings.BACKTEST_STOCK = ticker
settings.PRE_BACKTEST_DATA = tmp_data[0]
print "Create Algorithm..."
algo_obj = TradingAlgorithm(initialize=initialize,
handle_data=handle_data)
try:
backtest_data = load_bars_from_yahoo(stocks=[ticker, 'SPY'],
start=backtest_start,
end=backtest_end)
try:
perf = algo_obj.run(backtest_data)
analyze([ticker], [perf])
except ValueError, e:
print str(e)
except IOError, e:
print "Stock Error: could not load", ticker, "from Yahoo."
print "Only testing one cluster for now - Done!"
return
def handle_data(context, data):
#assert context.portfolio.cash > 0.0, "ERROR: negative context.portfolio.cash"
#assert len(context.raw_data) == context.training_data_length; "ERROR: "
# data stored as (open, high, low, close, volume, price)
feed_data = ([
data[context.security].open,
data[context.security].high,
data[context.security].low,
data[context.security].close
#data[context.security].volume,
#data[context.security].close,
])
#keep track of history.
context.raw_data.pop(0)
context.raw_data.append(feed_data)
context.normalized_data = Manager.preprocessData(context.raw_data)[:-2]
prediction = context.strategy.predict(context.normalized_data)[-1]
print "Value: $%.2f Cash: $%.2f Predict: %.5f" % (context.portfolio.portfolio_value, context.portfolio.cash, prediction[0])
# Do nothing if there are open orders:
if has_orders(context, data):
print('has open orders - doing nothing!')
# Put entire position in
elif prediction > 0.5:
order_target_percent(context.security, .95)
# Take entire position out
else:
order_target_percent(context.security, 0)
#order_target_percent(context.security, -.99)
record(BENCH=data[context.security].price)
record(SPY=data[context.benchmark].price)
def handle_data(context, data):
#assert context.portfolio.cash > 0.0, "ERROR: negative context.portfolio.cash"
#assert len(context.raw_data) == context.training_data_length; "ERROR: "
# data stored as (open, high, low, close, volume, price)
feed_data = ([
data[context.security].open, data[context.security].high,
data[context.security].low, data[context.security].close
#data[context.security].volume,
#data[context.security].close,
])
#keep track of history.
context.raw_data.pop(0)
context.raw_data.append(feed_data)
context.normalized_data = Manager.preprocessData(context.raw_data)[:-2]
prediction = context.strategy.predict(context.normalized_data)[-1]
print "Value: $%.2f Cash: $%.2f Predict: %.5f" % (
context.portfolio.portfolio_value, context.portfolio.cash,
prediction[0])
# Do nothing if there are open orders:
if has_orders(context, data):
print('has open orders - doing nothing!')
# Put entire position in
elif prediction > 0.5:
order_target_percent(context.security, .95)
# Take entire position out
else:
order_target_percent(context.security, 0)
#order_target_percent(context.security, -.99)
record(BENCH=data[context.security].price)
record(SPY=data[context.benchmark].price)
def initialize(context):
print "Initialize..."
context.security = symbol(settings.BACKTEST_STOCK)
context.benchmark = symbol('SPY')
context.strategy = settings.STRATEGY_OBJECT
context.raw_data = settings.PRE_BACKTEST_DATA
context.normalized_data = Manager.preprocessData(context.raw_data)[:-2]
print "Backtest symbol:", context.security
print "Capital Base:", context.portfolio.cash
def initialize(context):
print "Initialize..."
context.security = symbol(settings.BACKTEST_STOCK)
context.benchmark = symbol('SPY')
context.strategy = settings.STRATEGY_OBJECT
context.raw_data = settings.PRE_BACKTEST_DATA
context.normalized_data = Manager.preprocessData(context.raw_data)[:-2]
print "Backtest symbol:", context.security
print "Capital Base:", context.portfolio.cash
def run_clusters(strategy_class, clustering_tickers, cluster_num, epochs_num, training_start,
training_end, backtest_start, backtest_end, is_graph, is_elbow):
""" Run the test given command-line args.
Cluster.
For each cluster, train a strategy on that cluster.
For each stock in that cluster, run a backtest.
Graph results.
"""
print "\nGathering data..."
ticker_list, raw_stock_data_list = Manager.getRawStockDataList(clustering_tickers, training_start, training_end, 252)
normalized_stock_data_list = [Manager.preprocessData(x) for x in raw_stock_data_list]
print "\nClustering..."
tickers, clusters = createClusters(ticker_list, normalized_stock_data_list, cluster_num)
print "# of stocks: " + str(len(normalized_stock_data_list))
print "# of clusters: " + str(len(clusters))
print ""
for t, c in itertools.izip(tickers, clusters):
print "\tCluster: " + str(len(c)), "stocks: ",
for symbol in t:
print symbol,
print ""
if is_graph:
graphClusters(clusters)
if is_elbow:
graphElbowMethod(normalized_stock_data_list)
for t, cluster in itertools.izip(tickers, clusters):
settings.STRATEGY_OBJECT = trainStrategy(strategy_class, cluster, epochs_num)
for ticker in t:
print "Cluster:", t
print "Stock:", ticker
tmp_ticks, tmp_data = Manager.getRawStockDataList([ticker], training_start, training_end, 252)
settings.BACKTEST_STOCK = ticker
settings.PRE_BACKTEST_DATA = tmp_data[0]
print "Create Algorithm..."
algo_obj = TradingAlgorithm(initialize=initialize, handle_data=handle_data)
try:
backtest_data = load_bars_from_yahoo(stocks=[ticker, 'SPY'], start=backtest_start, end=backtest_end)
try:
perf = algo_obj.run(backtest_data)
analyze([ticker], [perf])
except ValueError, e:
print str(e)
except IOError, e:
print "Stock Error: could not load", ticker, "from Yahoo."
print "Only testing one cluster for now - Done!"
return
