def handle_data(context, data):
current_time = get_datetime().time()
# When model is empty
if len(context.model.get_renko_prices()) == 0:
context.model = pyrenko.renko()
history = data.history(context.asset,
'price',
bar_count=context.n_history,
frequency=context.tf)
# Get daily absolute returns
diffs = history.diff(context.diff_lag).abs()
diffs = diffs[~np.isnan(diffs)]
# Calculate IQR of daily returns
iqr_diffs = np.percentile(diffs, [25, 75])
# Find the optimal brick size
opt_bs = opt.fminbound(lambda x: -evaluate_renko(
brick=x, history=history, column_name='score'),
iqr_diffs[0],
iqr_diffs[1],
disp=0)
# Build the model
context.last_brick_size = opt_bs
context.model.set_brick_size(brick_size=opt_bs, auto=False)
context.model.build_history(prices=history)
# Open a position
order_target_percent(
context.asset,
context.leverage * context.model.get_renko_directions()[-1])
else:
last_price = data.history(
context.asset,
'price',
bar_count=1,
frequency='1440T',
)
# Just for output and debug
prev = context.model.get_renko_prices()[-1]
prev_dir = context.model.get_renko_directions()[-1]
num_created_bars = context.model.do_next(last_price)
# If the last price moves in the backward direction we should rebuild the model
if np.sign(context.portfolio.positions[context.asset].amount *
context.model.get_renko_directions()[-1]) == -1:
order_target_percent(context.asset, 0.0)
context.model = pyrenko.renko()
# or we cover the part of the position
elif context.part_cover_ratio > 0.0 and num_created_bars != 0:
order_target(
context.asset,
context.portfolio.positions[context.asset].amount *
(1.0 - context.part_cover_ratio))
def handle_data(self, data):
from catalyst.api import (
order_percent,
order_target,
order_target_percent,
order_target_value,
order_value,
)
for style in [MarketOrder(), LimitOrder(10),
StopOrder(10), StopLimitOrder(10, 10)]:
with assert_raises(UnsupportedOrderParameters):
order(self.asset, 10, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order(self.asset, 10, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_value(self.asset, 300, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_value(self.asset, 300, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_percent(self.asset, .1, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_percent(self.asset, .1, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target(self.asset, 100, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target(self.asset, 100, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_value(self.asset, 100,
limit_price=10,
style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_value(self.asset, 100,
stop_price=10,
style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_percent(self.asset, .2,
limit_price=10,
style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_percent(self.asset, .2,
stop_price=10,
style=style)
def handle_data(context, data):
if not context.blotter.open_orders:
if context.portfolio.positions and context.portfolio.positions[
context.asset].amount > 0.5:
order_target(context.asset,
0,
limit_price=(data.current(context.asset, 'price') +
0.00013))
else:
order_target(context.asset,
1,
limit_price=(data.current(context.asset, 'price') +
0.00003))
record(btc=data.current(context.asset, 'price'))
def handle_data(context, data):
if not context.blotter.open_orders:
if (context.portfolio.positions
and context.portfolio.positions[context.asset].amount >= 2):
order(context.asset,
-2,
limit_price=(data.current(context.asset, 'price') -
0.00000002))
else:
order_target(context.asset,
3,
limit_price=(data.current(context.asset, 'price') +
0.00000002))
record(btc=data.current(context.asset, 'price'))
def weAreShort(context, analysis):
s1 = context.asset
TP = context.cost_basis - context.lower
SL = context.cost_basis + context.upper
Crit = context.cost_basis
position = context.portfolio.positions[context.asset]
log.info(
"We are Short. Holdings: {amount} @ {cost_basis}".format(
amount=position.amount, cost_basis=context.cost_basis
)
)
if context.price > Crit:
order(s1, amount=(context.ORDER_SIZE * context.multiplyBy * context.level))
context.in_long = True
context.in_short = False
context.level += 1
log.info(
"Kill Short! GO LONG! Sold {amount} @ {price}".format(
amount=position.amount, price=context.price
)
)
elif context.price < TP:
context.in_long = False
context.in_short = False
context.level = 1
order_target(s1, 0)
log.info(
"We made it! Sold {amount} @ {price}".format(
amount=position.amount, price=context.price
)
)
elif context.price > SL:
context.in_long = False
context.in_short = False
context.level = 1
order_target(s1, 0)
log.info(
"We lost it all! Sold {amount} @ {price}".format(
amount=position.amount, price=context.price
)
)
else:
log.info("no buy or sell opportunity found")
def weAreLong(context, analysis):
s1 = context.asset
TP = context.cost_basis + context.upper
SL = context.cost_basis - context.lower
Crit = context.cost_basis - context.distance
position = context.portfolio.positions[context.asset]
log.info('We Are Long. Holdings: {amount} @ {cost_basis}'.format(
amount=position.amount, cost_basis=context.cost_basis))
if context.price < Crit:
order(
s1,
amount=-(context.ORDER_SIZE * context.multiplyBy * context.level))
context.in_long = False
context.in_short = True
context.level += 1
log.info('Kill Long! GO SHORT! Sold {amount} @ {price}'.format(
amount=position.amount, price=context.price))
elif context.price > TP:
context.in_long = False
context.in_short = False
context.level = 1
order_target(s1, 0)
log.info('We made it! Sold {amount} @ {price}'.format(
amount=position.amount, price=context.price))
elif context.price < SL:
context.in_long = False
context.in_short = False
context.level = 1
order_target(s1, 0)
log.info('We lost it all! Sold {amount} @ {price}'.format(
amount=position.amount, price=context.price))
else:
log.info('no buy or sell opportunity found')
def handle_data(context, data):
"""
在每个交易周期上运行的策略
"""
context.i += 1 # 记录交易周期
if context.i < LONG_WIN + 1:
# 如果交易周期过短，无法计算均线，则跳过循环
log.warning('交易周期过短，无法计算指标')
return
if context.i % TRADE_WIN != 0:
return
# 获取当前周期内有效的加密货币
context.available_asset_pool = [asset
for asset in context.asset_pool
if asset.start_date <= data.current_dt]
context.up_cross_signaled = set() # 初始化金叉的交易对集合
context.down_cross_signaled = set() # 初始化死叉的交易对集合
for asset in context.available_asset_pool:
# 遍历每一个加密货币对
# 获得历史价格
frequency = '{}T'.format(TRADE_WIN) # '5T'
hitory_data = data.history(asset,
'close',
bar_count=LONG_WIN + 1,
frequency=frequency,
)
if len(hitory_data) >= LONG_WIN + 1:
# 保证新的货币有足够的时间计算均线
# 计算双均线
short_avgs = hitory_data.rolling(window=SHORT_WIN).mean()
long_avgs = hitory_data.rolling(window=LONG_WIN).mean()
# 双均线策略
# 短期均线上穿长期均线
if (short_avgs[-2] < long_avgs[-2]) and (short_avgs[-1] >= long_avgs[-1]):
# 形成金叉
context.up_cross_signaled.add(asset)
# 短期均线下穿长期均线
if (short_avgs[-2] > long_avgs[-2]) and (short_avgs[-1] <= long_avgs[-1]):
# 形成死叉
context.down_cross_signaled.add(asset)
# 卖出均线死叉信号的持仓交易对
for asset in context.portfolio.positions:
if asset in context.down_cross_signaled:
order_target(asset, 0)
# 买入均线金叉信号的持仓股
for asset in context.up_cross_signaled:
if asset not in context.portfolio.positions:
close_price = data.current(asset, 'close')
available_cash = get_available_cash(context)
if available_cash > 0:
# 如果有可用现金
# 每个交易对平均分配现金
cash_for_each_asset = available_cash / len(context.available_asset_pool)
amount_to_buy = cash_for_each_asset / close_price # 计算购买的数量
if amount_to_buy >= asset.min_trade_size:
# 购买的数量大于最小购买数量
order(asset, amount_to_buy)
# 持仓比例
pos_level = context.portfolio.positions_value / context.portfolio.portfolio_value
# 记录每个交易周期的现金
record(cash=context.portfolio.cash, pos_level=pos_level)
# 输出信息
log.info('日期：{}，资产：{:.2f}，持仓比例：{:.6f}%，持仓产品：{}'.format(
data.current_dt, context.portfolio.portfolio_value, pos_level * 100,
', '.join([asset.asset_name for asset in context.portfolio.positions]))
)
def handle_data(context, data):
current_time = get_datetime().time()
if current_time.hour == 0 and current_time.minute == 0:
print("Current date is " + str(get_datetime().date()))
# we check if the model is empty we should get the data,
# calculate IQR, optimize the brick size, build the Renko chart, and open the order.
if len(context.model.get_renko_prices()) == 0:
context.model = renko()
history = data.history(context.asset,
"price",
bar_count=context.n_history,
frequency=context.tf)
# Get daily absolute returns
diffs = history.diff(1).abs()
diffs = diffs[~np.isnan(diffs)]
# Calculate Interquartile range of daily returns
iqr_diffs = np.percentile(diffs, [25, 75])
# Find the optimal brick size
opt_bs = opt.fminbound(
lambda x: -evaluate_renko(
brick=x, history=history, column_name="score"),
iqr_diffs[0],
iqr_diffs[1],
disp=0,
)
# Build the model
print("REBUILDING RENKO: " + str(opt_bs))
context.last_brick_size = opt_bs
context.model.set_brick_size(brick_size=opt_bs, auto=False)
context.model.build_history(prices=history)
# Open a position
if context.model.get_renko_directions()[-1] == 1:
order_target_percent(context.asset, 1)
elif context.model.get_renko_directions()[-1] == -1:
order_target_percent(context.asset, 0)
# Open a position
order_target_percent(
context.asset,
context.leverage * context.model.get_renko_directions()[-1])
# Store some information
record(
rebuilding_status=1,
brick_size=context.last_brick_size,
price=history[-1],
renko_price=context.model.get_renko_prices()[-1],
num_created_bars=0,
amount=context.portfolio.positions[context.asset].amount,
)
else:
last_price = data.history(
context.asset,
"price",
bar_count=1,
frequency="1440T",
)
# Just for output and debug
prev = context.model.get_renko_prices()[-1]
prev_dir = context.model.get_renko_directions()[-1]
num_created_bars = context.model.do_next(last_price)
if num_created_bars != 0:
print("New Renko bars created")
print("last price: " + str(last_price))
print("previous Renko price: " + str(prev))
print("current Renko price: " +
str(context.model.get_renko_prices()[-1]))
print("direction: " + str(prev_dir))
print("brick size: " + str(context.model.brick_size))
# Store some information
record(
rebuilding_status=0,
brick_size=context.last_brick_size,
price=last_price,
renko_price=context.model.get_renko_prices()[-1],
num_created_bars=num_created_bars,
amount=context.portfolio.positions[context.asset].amount,
)
# If the last price moves in the backward direction we should rebuild the model
if (np.sign(context.portfolio.positions[context.asset].amount *
context.model.get_renko_directions()[-1]) == -1):
order_target_percent(context.asset, 0.0)
context.model = renko()
# or we cover the part of the position
elif context.part_cover_ratio > 0.0 and num_created_bars != 0:
order_target(
context.asset,
context.portfolio.positions[context.asset].amount *
(1.0 - context.part_cover_ratio),
)
def handle_data(context, data):
current_time = get_datetime().time()
if current_time.hour == 0 and current_time.minute == 0:
print('Current date is ' + str(get_datetime().date()))
# When model is empty
if len(context.model.get_renko_prices()) == 0:
context.model = pyrenko.renko()
history = data.history(context.asset,
'price',
bar_count = context.n_history,
frequency = context.tf
)
# Get daily absolute returns
diffs = history.diff(24).abs()
diffs = diffs[~np.isnan(diffs)]
# Calculate IQR of daily returns
iqr_diffs = np.percentile(diffs, [25, 75])
# Find the optimal brick size
opt_bs = opt.fminbound(lambda x: -evaluate_renko(brick = x,
history = history, column_name = 'score'),
iqr_diffs[0], iqr_diffs[1], disp=0)
# Build the model
print('REBUILDING RENKO: ' + str(opt_bs))
context.last_brick_size = opt_bs
context.model.set_brick_size(brick_size = opt_bs, auto = False)
context.model.build_history(prices = history)
# Open a position
order_target_percent(context.asset, context.leverage * context.model.get_renko_directions()[-1])
# Store some information
record(
rebuilding_status = 1,
brick_size = context.last_brick_size,
price = history[-1],
renko_price = context.model.get_renko_prices()[-1],
num_created_bars = 0,
amount = context.portfolio.positions[context.asset].amount
)
else:
last_price = data.history(context.asset,
'price',
bar_count = 1,
frequency = '1440T',
)
# Just for output and debug
prev = context.model.get_renko_prices()[-1]
prev_dir = context.model.get_renko_directions()[-1]
num_created_bars = context.model.do_next(last_price)
if num_created_bars != 0:
print('New Renko bars created')
print('last price: ' + str(last_price))
print('previous Renko price: ' + str(prev))
print('current Renko price: ' + str(context.model.get_renko_prices()[-1]))
print('direction: ' + str(prev_dir))
print('brick size: ' + str(context.model.brick_size))
# Store some information
record(
rebuilding_status = 0,
brick_size = context.last_brick_size,
price = last_price,
renko_price = context.model.get_renko_prices()[-1],
num_created_bars = num_created_bars,
amount = context.portfolio.positions[context.asset].amount
)
# If the last price moves in the backward direction we should rebuild the model
if np.sign(context.portfolio.positions[context.asset].amount * context.model.get_renko_directions()[-1]) == -1:
order_target_percent(context.asset, 0.0)
context.model = pyrenko.renko()
# or we cover the part of the position
elif context.part_cover_ratio > 0.0 and num_created_bars != 0:
order_target(context.asset, context.portfolio.positions[context.asset].amount * (1.0 - context.part_cover_ratio))