高频交易AS模型

比特量化

已于 2022-03-06 09:41:39 修改

阅读量2.6k

点赞数 1

文章标签： python 高频交易

于 2022-03-06 09:41:16 首次发布

本文链接：https://blog.youkuaiyun.com/bitquant/article/details/123305888

版权

本文探讨了高频交易中的Avellaneda-Stoikov市场制造策略，通过引用相关研究资料，深入理解如何在限价订单簿中进行有效交易。通过对<avellaneda_market_making>和<LimitOrderBook.pdf>等文献的学习，可以了解如何在实际操作中实施这种策略。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

# -*- coding: utf-8 -*-
"""
Created on Thu Oct 12 16:40:52 2017

@author: Marco Dibo

"High-frequency trading in a limit order book"
by Marco Avellaneda and Sasha Stoikov"

"""


## Montecarlo simulation with numpy ##
import math
import numpy as np
import matplotlib.pyplot as plt
import random


#Parameters for mid price simulation:

S0 = 100. #initial price
T = 1.0  #time
sigma = 2 #volatility
M = 200 #number of time steps
dt = T/M #time step
Sim = 1000  #number of simulations
gamma = 0.1 #risk aversion 
k = 1.5
A = 140
I = 1 


#Results:

AverageSpread = []
Profit = []
Std = []

for i in range(1, Sim+1):
    
    ###reservation price:
    ### r(s,t) = s - q * gamma * sigma**2 * (T-t)    
    
    S = np.zeros((M+1,I))
    Bid = np.zeros((M+1,I))
    Ask = np.zeros((M+1,I))
    ReservPrice = np.zeros((M+1,I))
    spread = np.zeros((M+1,I))
    deltaB = np.zeros((M+1,I))
    deltaA = np.zeros((M+1,I))
    q = np.zeros((M+1,I))
    w = np.zeros((M+1,I))
    equity = np.zeros((M+1,I))
    reserve_relation =</