本文整理汇总了Python中zipline.api.symbol函数的典型用法代码示例。如果您正苦于以下问题:Python symbol函数的具体用法?Python symbol怎么用?Python symbol使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了symbol函数的20个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: handle_data
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = history(100, '1d', 'price').mean()
long_mavg = history(300, '1d', 'price').mean()
# price_history = data.history(assets=symbol('TEST'), fields="price", bar_count=5, frequency="1d")
# Trading logic
if short_mavg[0] > long_mavg[0]:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(symbol('AAPL'), 100)
elif short_mavg[0] < long_mavg[0]:
order_target(symbol('AAPL'), 0)
# Save values for later inspection
record(AAPL=data[symbol('AAPL')].price,
short_mavg=short_mavg[0],
long_mavg=long_mavg[0])
开发者ID:Ernestyj,项目名称:PyProj,代码行数:25,代码来源:TradingAlgo.py
示例2: handle_data
def handle_data(context, data):
curr_price = data[symbol('USCRWTIC INDEX')].price
curr_date = data[symbol('USCRWTIC INDEX')].datetime
curr_positions = context.portfolio.positions[symbol('USCRWTIC INDEX')].amount
cash = context.portfolio.cash
local_historical_data = context.oil_historical_data
local_historical_data = local_historical_data['USCRWTIC INDEX'][['price']]
df_to_forecast = local_historical_data[local_historical_data.index <= curr_date]
result = forecast_ts.run(df = df_to_forecast, ts_list = None, freq = 'B', forecast_horizon = 6, start_date = curr_date.strftime('%Y-%m-%d'), method = context.method, processing_params = context.processing_params, expert_params = context.expert_params)
estimated_return = result.iloc[-1].values[-1]
# estimated_return = np.random.rand()-1
print cash, curr_positions, curr_price, curr_date, estimated_return
if estimated_return < 0 and curr_positions == 0:
order(symbol('USCRWTIC INDEX'), -100)
elif estimated_return > 0 and curr_positions != 0:
order(symbol('USCRWTIC INDEX'), 100)
开发者ID:ricardompassos,项目名称:backtest,代码行数:30,代码来源:oil_example_2.py
示例3: handle_data
def handle_data(context, data):
#trading algorithm (executed on every event)
#skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
#compute short and long moving averages:
short_mavg = history(100, '1d', 'price').mean()
long_mavg = history(300, '1d', 'price').mean()
buy = False
sell = False
#trading logic
if (short_mavg[0] > long_mavg[0]) and not context.invested:
buy = True
context.invested = True
order_target(symbol('AAPL'), 100)
elif (short_mavg[0] < long_mavg[0]) and context.invested:
sell = True
context.invested = False
order_target(symbol('AAPL'), -100)
#save values for plotting
record(AAPL = data[symbol('AAPL')].price,
short_mavg = short_mavg[0],
long_mavg = long_mavg[0],
buy=buy,
sell=sell)
开发者ID:vsmolyakov,项目名称:fin,代码行数:32,代码来源:momentum.py
示例4: initialize
def initialize(context):
# Turn off the slippage model
set_slippage(slippage.FixedSlippage(spread=0.0))
# Set the commission model
set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.0))
context.day = -1 # using zero-based counter for days
context.set_benchmark(symbol('DIA'))
context.assets = []
print('Setup investable assets...')
for ticker in asset_tickers:
#print(ticker)
context.assets.append(symbol(ticker))
context.n_asset = len(context.assets)
context.n_portfolio = 40 # num mean-variance efficient portfolios to compute
context.today = None
context.tau = None
context.min_data_window = 756 # min of 3 yrs data for calculations
context.first_rebal_date = None
context.first_rebal_idx = None
context.weights = None
# Schedule dynamic allocation calcs to occur 1 day before month end - note that
# actual trading will occur on the close on the last trading day of the month
schedule_function(rebalance,
date_rule=date_rules.month_end(days_offset=1),
time_rule=time_rules.market_close())
# Record some stuff every day
schedule_function(record_vars,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_close())
开发者ID:returnandrisk,项目名称:meucci-python,代码行数:29,代码来源:dynamic_allocation_performance_analysis.py
示例5: func0
def func0(context, data):
order(symbol('AAPL UW EQUITY'), 10)
print 'price: ', data[symbol('AAPL UW EQUITY')].price
# order(symbol('AAPL UW EQUITY'), -10)
print context.portfolio.cash
print context.get_datetime().date()
print "==========================="
开发者ID:ricardompassos,项目名称:backtest,代码行数:7,代码来源:buy_and_hold_my.py
示例6: handle_data
def handle_data(context, data):
curr_price = data[symbol('USCRWTIC INDEX')].price
curr_positions = context.portfolio.positions[symbol('USCRWTIC INDEX')].amount
cash = context.portfolio.cash
print cash, curr_positions, curr_price
# context.counter += 1
# if context.counter > 500:
# print "Cancelou"
# cancel_order(context.order_id)
# else:
# print 'ola'
random_order = np.random.rand()
if random_order > 0.5 and curr_positions == 0:
order(symbol('USCRWTIC INDEX'), 100)
elif random_order < 0.5 and curr_positions != 0:
order(symbol('USCRWTIC INDEX'), -100)
开发者ID:ricardompassos,项目名称:backtest,代码行数:26,代码来源:oil_example_simple.py
示例7: handle_data
def handle_data(context, data):
# context.i+=1
# if context.i<=5:
# return
# 循环每只股票
closeprice= history(5,'1d','close')
for security in context.stocks:
vwap=(closeprice[symbol(security)][-2]+closeprice[symbol(security)][-3]+closeprice[symbol(security)][-4])/3
price = closeprice[symbol(security)][-2]
print get_datetime(),security,vwap,price
# # 如果上一时间点价格小于三天平均价*0.995,并且持有该股票,卖出
if price < vwap * 0.995:
# 下入卖出单
order(symbol(security),-300)
print get_datetime(),("Selling %s" % (security))
# 记录这次卖出
#log.info("Selling %s" % (security))
# 如果上一时间点价格大于三天平均价*1.005,并且有现金余额,买入
elif price > vwap * 1.005:
# 下入买入单
order(symbol(security),300)
# 记录这次买入
print get_datetime(),("Buying %s" % (security))
开发者ID:liyizheng0513,项目名称:zipline-chinese,代码行数:25,代码来源:stock_select.py
示例8: handle_data
def handle_data(context, data):
context.i += 1
stock_name = context.panel.axes[0][0]
if context.i == 60:
order(symbol(stock_name), 10)
if context.i == 150:
order(symbol(stock_name), -10)
record(Prices=data[symbol(stock_name)].price)
开发者ID:skye17,项目名称:newfront,代码行数:8,代码来源:order_then_sell.py
示例9: handle_data
def handle_data(context, data):
print "================================="
print "New iteration"
print data
order(symbol('AAPL'), 10)
record(AAPL=data[symbol('AAPL')].price)
开发者ID:ricardompassos,项目名称:backtest,代码行数:8,代码来源:buyapple.py
示例10: _handle_data
def _handle_data(self, context, data):
for movimiento in context.movimientos:
clave_emisora = movimiento.emisora
fecha_movimiento = movimiento.fecha
fecha = data[symbol(clave_emisora)].dt
delta = fecha_movimiento - fecha.replace(tzinfo=None)
num_acciones = movimiento.num_acciones
if(delta.days == 0):
order(symbol(clave_emisora), num_acciones)
开发者ID:ivansabik,项目名称:tradinglab-mexico,代码行数:9,代码来源:modelos.py
示例11: initialize
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
开发者ID:nsbradford,项目名称:quantraider,代码行数:9,代码来源:trader.py
示例12: handle_data
def handle_data(context, data):
context.panel # Here we have access to training data also.
# Make solution using the result of learning:
if not int(data[symbol('AAPL')].price) % context.result:
order(symbol('AAPL'), 10)
# Record some values for analysis in 'analyze()'.
sids = context.panel.axes[0].values
prices = [data[symbol(sid)].price for sid in sids]
record(Prices=prices)
record(Prediction=3 * data[symbol('AAPL')].price - 2.2 * context.previous)
# Record current price to use it in future.
context.previous = data[symbol('AAPL')].price
开发者ID:skye17,项目名称:frontopolar_site,代码行数:12,代码来源:default.py
示例13: handle_data
def handle_data(context, data):
# check if the spot is outside CI of MPP
day_option_df = context.options[context.options['date'] == get_datetime()]
call_sums = call_otm(day_option_df, 'FB', get_datetime())
put_sums = put_otm(day_option_df, 'FB', get_datetime())
add_to_window(context, 10, max_pain_strike(call_sums, put_sums), 'FB')
ci = CI(context.window, 1)
price = history(1, '1d', 'price').iloc[0,0]
if price < ci[0]: order_target_percent(symbol('FB'), 1)
elif price > ci[1]: order_target_percent(symbol('FB'), 0)
开发者ID:vishalv95,项目名称:MaxPain,代码行数:12,代码来源:backtest.py
示例14: handle_data
def handle_data(context, data):
context.cur_time += 1
month = get_datetime().date().month
is_january = (month == 1)
new_prices = np.array([data[symbol(symbol_name)].price for symbol_name in context.symbols], dtype='float32')
record(Prices=new_prices)
new_prices = new_prices.reshape((context.N_STOCKS, 1))
# print context.returns_history.shape
# print new_prices.shape
# print context.previous_prices.shape
context.returns_history = np.concatenate([context.returns_history, new_prices / context.previous_prices], axis=1)
context.previous_prices = new_prices
if context.month != month:
# Trading in the beginning of month
context.month_sizes.append(context.day_of_month)
context.day_of_month = 1
context.count_month += 1
context.month_sizes.append(context.day_of_month)
context.day_of_month = 1
if context.count_month > N_MONTHS:
# Deleting too old returns
if context.count_month > N_MONTHS + 1:
context.returns_history = np.delete(context.returns_history, range(context.month_sizes[-14]), axis=1)
model_input = preprocess_data_for_model(context.returns_history, context.month_sizes[-13:], context.scaler)
is_january_column = np.array([is_january] * context.N_STOCKS).reshape((context.N_STOCKS, 1))
model_input = np.concatenate([is_january_column, model_input], axis=1)
# print 'Input shape', model_input.shape
predicted_proba = context.model.predict_proba(model_input)
# print predicted_proba
'''
half_trade = len(context.symbols) * 1 / 10
args_sorted = np.argsort(predicted_proba[:, 0])
buy_args = args_sorted[:half_trade]
sell_args = args_sorted[-half_trade:]
for arg in buy_args:
order_target(symbol(context.symbols[arg]), 1)
for arg in sell_args:
order_target(symbol(context.symbols[arg]), -1)
'''
for i in range(context.N_STOCKS):
if predicted_proba[i, 0] > 0.5:
order_target(symbol(context.symbols[i]), 1)
else:
order_target(symbol(context.symbols[i]), -1)
else:
context.day_of_month += 1
context.month = month
开发者ID:skye17,项目名称:newfront,代码行数:53,代码来源:website_trade2.py
示例15: handle_data
def handle_data(context, data):
#On-Line Moving Average Reversal (OLMAR)
context.days += 1
if context.days < context.window_length:
return
if context.init:
rebalance_portfolio(context, data, context.b_t)
context.init=False
return
m = context.m #num assets
x_tilde = np.zeros(m) #relative mean deviation
b = np.zeros(m) #weights
#compute moving average price for each asset
mavgs = history(context.window_length, '1d', 'price').mean()
#mavgs = data.history(context.sids, 'price', context.window_length, '1d').mean()
for i, stock in enumerate(context.stocks):
price = data[stock]['price']
x_tilde[i] = mavgs[i] / price
x_bar = x_tilde.mean()
market_rel_dev = x_tilde - x_bar #relative deviation
exp_return = np.dot(context.b_t, x_tilde)
weight = context.eps - exp_return
variability = (np.linalg.norm(market_rel_dev))**2
if variability == 0.0:
step_size = 0
else:
step_size = np.max((0, weight/variability))
b = context.b_t + step_size * market_rel_dev
b_norm = simplex_projection(b)
rebalance_portfolio(context, data, b_norm)
context.b_t = b_norm
#save values for plotting
record(AAPL = data[symbol('AAPL')].price,
MSFT = data[symbol('MSFT')].price,
step_size = step_size,
variability = variability
)
开发者ID:vsmolyakov,项目名称:fin,代码行数:52,代码来源:olmar.py
示例16: handle_data
def handle_data(self, context, data):
# Implement your algorithm logic here.
# data[sid(X)] holds the trade event data for that security.
# context.portfolio holds the current portfolio state.
# Place orders with the order(SID, amount) method.
# TODO: implement your own logic here.
context.trade_days += 1
if context.trade_days <> 5 :
return
context.trade_days = 0
## checking the market status:
## if SPY > price one year ago, Market is in uptrend
## otherwise, market is in downtrend
hist = history(bar_count = 241, frequency='1d', field='price')
cash = context.portfolio.cash
current_price_spy = data[symbol(self.ticker_spy)].price
try:
if current_price_spy > hist[symbol(self.ticker_spy)][200] :
lst = self.top_rets(context.equities, 240)
lst_mean = lst['zero']
count = len(lst_mean)
for ticker in sector_tickers:
if ticker in lst_mean:
order_target_percent(symbol(ticker), 1.0/count)
else :
order_target_percent(symbol(ticker), 0)
order_target_percent(symbol(self.ticker_gld), 0)
order_target_percent(symbol(self.ticker_tlt), 0)
else :
for ticker in sector_tickers:
order_target_percent(symbol(ticker), 0)
order_target_percent(symbol(self.ticker_spy), 0)
order_target_percent(symbol(self.ticker_gld), 0.5)
order_target_percent(symbol(self.ticker_tlt), 0.5)
except:
pass
开发者ID:cocojumbo77,项目名称:Trading_Strategies,代码行数:49,代码来源:AAA.py
示例17: top_rets
def top_rets(self, tickers, win) :
hist = history(bar_count = 241, frequency='1d', field='price')
ret = ((hist/hist.shift(win)) - 1).tail(1)
mean_ret = float(np.median(ret))
max_ret = float(ret.max(axis=1))
spy_ret = float(ret[symbol(self.ticker_spy)])
lst = {}
lst['mean'] = []
lst['spy'] = []
lst['zero'] = []
lst['max'] = []
for ticker in tickers :
ticker_ret = float(ret[ticker])
if ticker_ret > mean_ret :
lst['mean'].append(ticker)
if ticker_ret > spy_ret:
lst['spy'].append(ticker)
if ticker_ret > 0:
lst['zero'].append(ticker)
if ticker_ret >= max_ret:
lst['max'].append(ticker)
return lst
开发者ID:cocojumbo77,项目名称:Trading_Strategies,代码行数:25,代码来源:AAA.py
示例18: handle_data
def handle_data(self, data):
######################################################
# 1. Compute regression coefficients between PEP and KO
params = self.ols_transform.handle_data(data, self.PEP, self.KO)
if params is None:
return
intercept, slope = params
######################################################
# 2. Compute spread and zscore
zscore = self.compute_zscore(data, slope, intercept)
self.record(zscores=zscore, PEP=data[symbol("PEP")].price, KO=data[symbol("KO")].price)
######################################################
# 3. Place orders
self.place_orders(data, zscore)
开发者ID:zluo,项目名称:zipline,代码行数:16,代码来源:pairtrade.py
示例19: initialize
def initialize(context):
# Let's set a look up date inside our backtest to ensure we grab the correct security
#set_symbol_lookup_date('2015-01-01')
# Use a very liquid set of stocks for quick order fills
context.symbol = symbol('SPY')
#context.stocks = symbols(['TWX','AIG','PSX','EMC','YHOO','MDY','TNA','CHK','FXI',
# 'PEP','SBUX','VZ','VWO','TWC','HAL','MDLZ','CAT','TSLA',
# 'MU','PM','WYNN','MET',NOV BRK_B SNDK ESRX YELP])
#set_universe(universe.DollarVolumeUniverse(99.5, 100))
#set_benchmark(symbol('SPY'))
# set a more realistic commission for IB, remove both this and slippage when live trading in IB
set_commission(commission.PerShare(cost=0.014, min_trade_cost=1.4))
# Default slippage values, but here to mess with for fun.
set_slippage(slippage.VolumeShareSlippage(volume_limit=0.25, price_impact=0.1))
# Use dicts to store items for plotting or comparison
context.next_pred_price = {} # Current cycles prediction
#Change us!
context.history_len = 500 # How many days in price history for training set
context.out_of_sameple_bin_size = 2
context.score_filter = -1000.0
context.action_to_move_percent = 0.0
# Register 2 histories that track daily prices,
# one with a 100 window and one with a 300 day window
add_history(context.history_len, '1d', 'price')
context.i = 0
开发者ID:21hub,项目名称:daily-stock-forecast,代码行数:33,代码来源:svr.py
示例20: positions
def positions(self):
now = datetime.datetime.now()
z_positions = protocol.Positions()
for pos in self._client.positions():
if isinstance(pos, list):
pos = TdxPosition(*pos)
sid = pos.sid
available = pos.available
z_position = protocol.Position(symbol(sid))
z_position.amount = pos.amount
z_position.cost_basis = pos.cost_basis
z_position.last_sale_price = pos.last_sale_price
z_position.last_sale_date = now
z_positions[symbol(sid)] = z_position
return z_positions
开发者ID:huangzhengyong,项目名称:zipline,代码行数:16,代码来源:tdx_broker.py
注:本文中的zipline.api.symbol函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。 |
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