将四大行分别纳入网格策略之中。将资金分成N份，每上涨或下跌 X 个百分点为一格，每次下跌触及到一格时，买入一份资金，上涨触及到一格时，卖出一份。有不同参数可以调整，可自行测试。
参数解释：
每个网格几个百分点 – 每上涨或下跌多少个百分点为一格
初始每笔交易资金 – 每份资金多少钱
是否动态交易资金 – 如果选择是，则代表每份资金会动态调整。比如：初始设置为10000，随着时间推移，总资产翻了一倍，那此时每份资产就变成了20000。在牛市中会更激进，但熊市中会更保守，反弹时可能会因买入的份额少而造成需要更久时间修复回撤
from nmapi import *
# 初始化函数，设定基准等等
def init(context):
set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
log.set_level('order', 'warning') # 设置日志级别
g.securities = ['601939.SH','601398.SH','601288.SH','601988.SH']
g.net_range = / 100
g.per_share =
g.per_share_init = g.per_share
g.is_dynamic_cash_pool = ''
g.days = 120
g.down = 0.2
g.up = 0.2
g.net = {}
g.base_price = {}
g.buy_shares = {}
run_schedule(trade, 'daily', time='15:00')
# 交易函数
def trade(context):
if g.is_dynamic_cash_pool == '是':
g.per_share = context.portfolio.total_value / (context.portfolio.starting_cash / g.per_share_init)
if len(g.net) != len(g.securities):
not_bought_sec_avg_price = {}
not_bought_sec_list = []
for sec in g.securities:
if sec not in g.net:
not_bought_sec_list.append(sec)
for sec in not_bought_sec_list:
try:
hist = attribute_history(sec, count=g.days, unit='1d', fields=['close'])
if not hist.empty:
avg_price = hist['close'].mean()
not_bought_sec_avg_price[sec] = round(avg_price, 2)
except Exception as e:
log.error(f"错误: {e}")
for sec in g.securities:
if sec not in g.net:
if sec not in not_bought_sec_avg_price:
continue
avg_price = not_bought_sec_avg_price[sec]
current_data = get_current_data()
cur_price = current_data[sec].last_price
low_price = (1 - g.down) * avg_price
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