import cufflinks as cf cf.go_offline() import numpy as np import pandas as pd set_slippage_avg_cost...30.38,25.46, 8.53, 8.05, 11.04, 24.95, 5.19, 6.8, 8.19, 5.44, 21.05, 7.06, 6.67, 18.61, 5.44, 2.9] no_slippage_avg_cost...8.54, 8.06, 11.05, 24.98, 5.2, 6.81, 8.2, 5.45, 21.08, 7.07, 6.68,18.63,5.45,2.9] diff = (np.array(no_slippage_avg_cost...) - np.array(set_slippage_avg_cost)) / np.array(set_slippage_avg_cost) pd.Series(diff).iplot(kind='histogram...', bins=100, title='(np.array(no_slippage_avg_cost) - np.array(set_slippage_avg_cost)) / np.array(set_slippage_avg_cost
---- def __init__(self, entryPrice, entryDt, exitPrice, exitDt, volume, rate, slippage...成交金额 self.commission = self.turnover*rate # 手续费成本 self.slippage...= slippage*2*size*abs(volume) # 滑点成本 self.pnl = ((self.exitPrice - self.entryPrice...) * volume * size - self.commission - self.slippage) # 净盈亏...我们可以看到，这个class初始化之后的对象其实含有了佣金，slippage和pnl。
1234567891011121314 { "MA2009.CZCE": { "rate": 0.0001, "slippage": 1, "size": 10, "pricetick...": 1 }, "rb2010.SHFE": { "rate": 0.0001, "slippage": 1, "size": 10, "pricetick": 1 }}...start=startDate, end=endDate, rate=self.setting[vt_symbol]["rate"], slippage...=self.setting[vt_symbol]["slippage"], size=self.setting[vt_symbol]["size"], pricetick
Interval_Pips[20]= {0, 20,20,15,15,20,20,30}; //-加仓时的交易量倍数 int Interval_Lots[20]= {1, 2, 1, 2, 1, 3, 2, 4}; int slippage...result=OrderSend(Symbol(), OP_BUY, NormalizeDouble(lots*Interval_Lots[buys],Digitslots), Ask, slippage...result=OrderSend(Symbol(), OP_SELL, NormalizeDouble(lots*Interval_Lots[sells],Digitslots), Bid, slippage...result=OrderSend(Symbol(),OP_SELL,NormalizeDouble(lots*Interval_Lots[sells],Digitslots),Bid,slippage...result=OrderSend(Symbol(),OP_BUY,NormalizeDouble(lots*Interval_Lots[buys],Digitslots),Ask,slippage
# 开启针对非集合竞价阶段的涨停，滑点买入价格以高概率在接近涨停的价格买入 slippage.sbb.g_enable_limit_up = True # 将集合竞价阶段的涨停买入成功概率设置为0，如果设置为...0.2即20%概率成功买入 slippage.sbb.g_pre_limit_up_rate = 0 # 开启针对非集合竞价阶段的跌停，滑点卖出价格以高概率在接近跌停的价格卖出 slippage.ssb.g_enable_limit_down...= True # 将集合竞价阶段的跌停卖出成功概率设置为0, 如果设置为0.2即20%概率成功卖出 slippage.ssb.g_pre_limit_down_rate = 0 # **自定义手续费...买入动作重写** # # 构建一个保守买入算法，以开盘价和最高价的平均值作为买入,而不是两者均值 # In[26]: from abupy import AbuSlippageBuyBase, slippage...import numpy as np class AbuSlippageBuyMean2(AbuSlippageBuyBase): """示例日内滑点均价买入类""" @slippage.sbb.slippage_limit_up
slippageModel)与setVolumeLimit(volumeLimit) 后者其实就是修改前面的那个最大可成交比例，而前面这个就是设置滑点模型，其传入的参数是一个pyalgotrade.broker.slippage.SlippageModel...a.没有滑点 pyalgotrade.broker.slippage.NoSlippage() b.VolumeShareSlippage滑点模型。...pyalgotrade.broker.slippage.VolumeShareSlippage(priceImpact=0.1) priceImpact (float.) – 你的订单对交易价格的影响程度有多大...fill strategy设置 fill_stra = broker.fillstrategy.DefaultStrategy(volumeLimit=0.1) sli_stra = broker.slippage.NoSlippage
string symbol, // symbol int cmd, // operation double volume, // volume double price, // price int slippage..., // slippage double stoploss, // stop loss double takeprofit, // take profit string comment=NULL, //
下面这个结构体，用于交易配置tradeSettings: struct tradeSettings { ulong slippage; double price; double...怎么使用这个结构体呢： tradeSettings trade; trade.slippage = 50; trade.stopLoss = StopLoss * _Point; 与变量声明类似，我们声明
backtest_initial_cash=10000000, backtest_commission_ratio=0.0001, backtest_slippage_ratio...00', backtest_initial_cash=30000, backtest_commission_ratio=0.0001, backtest_slippage_ratio..., backtest_initial_cash=1000000, backtest_commission_ratio=0.0001, backtest_slippage_ratio...backtest_initial_cash=10000000, backtest_commission_ratio=0.0001, backtest_slippage_ratio...敬请通过适当调整回测参数 1.backtest_commission_ratio回测佣金比例 2.backtest_slippage_ratio回测滑点比例 3.backtest_transaction_ratio
1000, default_qty_type=strategy.percent_of_equity, default_qty_value=33, calc_on_order_fills=false, slippage...slippage (const int) 滑点以tick表示。这个值被添加到市场单/止损单的执行价格中或从中减去，以使执行价格对策略不太有利。...例如，如果syminfo.mintick为0.01 并且`slippage`设置为5，则多头市价单将在实际价格上方5 * 0.01=0.05点处进入。此设置也可以在策略的“设置/属性”标签页中更改。...参数设置，所以每笔交易量为initial_capital * default_qty_value %，即为33% , calc_on_order_fills=false //不在订单成交后重新计算策略 , slippage
ADJUST_PREV后复权:ADJUST_POST backtest_initial_cash回测初始资金 backtest_commission_ratio回测佣金比例 backtest_slippage_ratio...ADJUST_PREV, backtest_initial_cash=100000, backtest_commission_ratio=0.0001, backtest_slippage_ratio
以下是我们将支付的净交易成本： trade_cost(qty) = (2 * qty * exchange_fee) + (qty * spread) + slippage_buy(qty) + slippage_sell...slippage_buy(qty)和slippage_sell(qty)是市场流通性不足导致的价格滑动。我们的交易量越大，支付的价格滑动成本就越高，因为我们无法以最佳价格交易全部数量。
Ribes引用了一个专业术语“滑点（slippage）”，滑点是一个外汇交易专业术语，对于外汇交易者来说，常见的令人困扰的问题就是，当按下买卖键时，出现的成交价却不同于原先的报价，这种现象就叫做滑点。
orderAddresses[1], orderAddresses[2], _exchangeType);require(price > _minPrice || _0xPrice > _minPrice, "Slippage...failed, price on other exchanges still needs to be higher than minPricerequire(price > _minPrice, "Slippage
class pyalgotrade.broker.slippage.``SlippageModel 基类: object 滑点模型的基类。 注意 这是一个基类，不应直接使用。...class pyalgotrade.broker.slippage.``NoSlippage 基类: pyalgotrade.broker.slippage.SlippageModel 无滑点模型。...class pyalgotrade.broker.slippage....默认情况下，它使用pyalgotrade.broker.slippage.NoSlippage滑点模型。...参数: slippageModel (pyalgotrade.broker.slippage.SlippageModel) – 滑点模型。
#5 # 根据不同的时间段设置滑点与手续费 # 输入：context（见API） # 输出：none def set_slip_fee(context): # 将滑点设置为0 set_slippage...', capital_base=10000000, slippage...= Slippage(value=0.001, unit='perValue'), # 滑点设置成百分比滑点0.001 commission
(perc, slip_open=True, slip_limit=True, slip_match=True, slip_out=False) 配置滑点以百分比为基础 set_slippage_fixed.../slippage/ 回测无法保证真实市场条件。...有两种方法可以更改其行为： 使用方法来配置滑点 BackBroker.set_slippage_perc(perc, slip_open=True, slip_limit=True, slip_match.../slippage.py --plot 01 2005-03-22 23:59:59 SELL Size: -1 / Price: 3040.55 02 2005-04-11 23:59:59 BUY.../slippage.py --slip_perc 0.015 01 2005-03-22 23:59:59 SELL Size: -1 / Price: 3040.55 02 2005-04-11 23
个订单类型 double volume, // 交易手数 double price, // 开仓价格或挂单价格 int slippage
contamination fragment germline haplotype map_qual multiallelic normal_artifact panel_of_normals PASS position slippage
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