1092. To Buy or Not to Buy 解析

本文介绍了一个简单的字符串匹配问题及其实现代码。通过输入两个字符串,程序判断第一个字符串是否包含第二个字符串的所有字符,并计算多余或缺少的字符数量。

Eva又双叒叕来了!!!!!

无良商家只卖一串,可(ma)怜(fan)的Eva却只需要她需要的。

看那一串够不够Eva想要的,够算她多买了多少,不够算少了多少。

#include <iostream>
#include <vector>
#include <string>
#include <cstring>

#define MAX 256

using namespace std;

int owner[MAX];
string s1, s2;
int missing = 0, need = 0;

int main() {
	getline(cin, s1);
	getline(cin, s2);

	for (int i = 0; i < s1.size(); i++) {
		owner[int(s1[i])] ++;
	}

	for (int i = 0; i < s2.size(); i++) {
		if (owner[int(s2[i])]-- > 0) {
			need++;
		}
		else
			missing++;
	}

	if (missing) {
		cout << "No " << missing << endl;
	}
	else {
		cout << "Yes " << s1.size() - need << endl;
	}

	return 0;

}


from jqdata import * from jqfactor import get_factor_values import pandas as pd import numpy as np def initialize(context): set_option("avoid_future_data", True) g.hold_days = 0 run_daily(select, time='before_open') # 修改为调用已定义的select函数 run_daily(trade_stocks, time='9:30') def select(context): # 获取股票池 stocks = get_index_stocks('000016.XSHG') valid_stocks = [s for s in stocks if not is_st_stock(s, context.previous_date)] if len(valid_stocks) > 0: kdj_data = get_kdj(valid_stocks, check_date=context.previous_date) buy_list = [] for stock in kdj_data: k, d, j = kdj_data[stock] if j > k and k > d and d < 20: # KDJ金叉条件 buy_list.append(stock) g.buy_list = buy_list g.hold_days = 0 logger.info(f"Selected buy list: {g.buy_list}") else: logger.warning("No valid stocks found after filtering ST stocks.") g.buy_list = [] def trade_stocks(context): # 卖出不在买入选股列表中的股票或持有超过5天的股票 for stock in list(context.portfolio.positions.keys()): if stock not in getattr(g, 'buy_list', []) or g.hold_days >= 3: order_target(stock, 0) # 买入选中的股票 if hasattr(g, 'buy_list') and g.buy_list and g.hold_days < 3: cash_per_stock = context.portfolio.available_cash / len(g.buy_list) for stock in g.buy_list: if stock not in context.portfolio.positions: order_value(stock, cash_per_stock) if hasattr(g, 'hold_days'): g.hold_days += 1 def get_kdj(securities, check_date, N=9, M1=3, M2=3): # 计算KDJ指标,确保数据为DataFrame格式 kdj_dict = {} for stock in securities: try: price_data = get_price(stock, end_date=check_date, frequency='daily', fields=['close'], fq='pre', count=N) # 将Panel转换为DataFrame(如果有必要) if isinstance(price_data, pd.Panel): price_data = price_data.to_frame(filter_observations=False).unstack(level=0) price_data.columns = price_data.columns.droplevel(0) if not price_data.empty: close_prices = price_data['close'].dropna().values if len(close_prices) >= N: low_list = close_prices[-N:] high_list = close_prices[-N:] rsv = (close_prices[-1] - min(low_list)) / (max(high_list) - min(low_list)) * 100 if max(high_list) != min(low_list) else 0 k = 50 if 'k' not in locals() else 1 / 3 * rsv + 2 / 3 * k d = 50 if 'd' not in locals() else 1 / 3 * k + 2 / 3 * d j = 3 * k - 2 * d kdj_dict[stock] = (k, d, j) else: logger.warning(f"Stock {stock} has insufficient data for KDJ calculation.") else: logger.warning(f"Stock {stock} has no price data available.") except Exception as e: logger.error(f"Error getting price data for stock {stock}: {e}") return kdj_dict def is_st_stock(stock, date): try: st_data = get_extras('is_st', stock, end_date=date, count=1) return st_data[stock][0] if stock in st_data else False except Exception as e: logger.error(f"Error checking ST status for stock {stock}: {e}") return False把这个代码的部分进行修改使他策略收益为正,且策略收益大于基准收益。
06-15
from jqdata import * from jqfactor import get_factor_values import pandas as pd import numpy as np def initialize(context): set_option("avoid_future_data", True) g.hold_days = 0 run_daily(select, time='before_open') run_daily(trade_stocks, time='14:30') def select(context): # 获取股票池 stocks = get_index_stocks('000016.XSHG') valid_stocks = [s for s in stocks if not is_st_stock(s, context.previous_date)] if len(valid_stocks) > 0: kdj_data = get_kdj(valid_stocks, check_date=context.previous_date) buy_list = [] for stock in kdj_data: k, d, j = kdj_data[stock] if j > k and k > d and d < 20: # KDJ金叉条件 buy_list.append(stock) g.buy_list = buy_list g.hold_days = 0 logger.info(f"Selected buy list: {g.buy_list}") else: logger.warning("No valid stocks found after filtering ST stocks.") g.buy_list = [] def trade_stocks(context): # 卖出不在买入选股列表中的股票或持有超过5天的股票 for stock in list(context.portfolio.positions.keys()): if stock not in getattr(g, 'buy_list', []) or g.hold_days >= 5: order_target(stock, 0) # 买入选中的股票 if hasattr(g, 'buy_list') and g.buy_list and g.hold_days < 5: cash_per_stock = context.portfolio.available_cash / len(g.buy_list) for stock in g.buy_list: if stock not in context.portfolio.positions: order_value(stock, cash_per_stock) if hasattr(g, 'hold_days'): g.hold_days += 1 def get_kdj(securities, check_date, N=9, M1=3, M2=3): # 计算KDJ指标,确保数据为DataFrame格式 kdj_dict = {} for stock in securities: try: price_data = get_price(stock, end_date=check_date, frequency='daily', fields=['close'], fq='pre', count=N) # 将Panel转换为DataFrame(如果有必要) if isinstance(price_data, pd.Panel): price_data = price_data.to_frame(filter_observations=False).unstack(level=0) price_data.columns = price_data.columns.droplevel(0) if not price_data.empty: close_prices = price_data['close'].dropna().values if len(close_prices) >= N: low_list = close_prices[-N:] high_list = close_prices[-N:] rsv = (close_prices[-1] - min(low_list)) / (max(high_list) - min(low_list)) * 100 if max(high_list) != min(low_list) else 0 k = 50 if 'k' not in locals() else 1 / 3 * rsv + 2 / 3 * k d = 50 if 'd' not in locals() else 1 / 3 * k + 2 / 3 * d j = 3 * k - 2 * d kdj_dict[stock] = (k, d, j) else: logger.warning(f"Stock {stock} has insufficient data for KDJ calculation.") else: logger.warning(f"Stock {stock} has no price data available.") except Exception as e: logger.error(f"Error getting price data for stock {stock}: {e}") return kdj_dict def is_st_stock(stock, date): try: st_data = get_extras('is_st', stock, end_date=date, count=1) return st_data[stock][0] if stock in st_data else False except Exception as e: logger.error(f"Error checking ST status for stock {stock}: {e}") return False帮我把这个代码修改 简化一下加入止盈卖出,止损卖出
06-15
内容概要:本文详细介绍了“秒杀商城”微服务架构的设计与实战全过程,涵盖系统从需求分析、服务拆分、技术选型到核心功能开发、分布式事务处理、容器化部署及监控链路追踪的完整流程。重点解决了高并发场景下的超卖问题,采用Redis预减库存、消息队列削峰、数据库乐观锁等手段保障数据一致性,并通过Nacos实现服务注册发现与配置管理,利用Seata处理跨服务分布式事务,结合RabbitMQ实现异步下单,提升系统吞吐能力。同时,项目支持Docker Compose快速部署和Kubernetes生产级编排,集成Sleuth+Zipkin链路追踪与Prometheus+Grafana监控体系,构建可观测性强的微服务系统。; 适合人群:具备Java基础和Spring Boot开发经验,熟悉微服务基本概念的中高级研发人员,尤其是希望深入理解高并发系统设计、分布式事务、服务治理等核心技术的开发者;适合工作2-5年、有志于转型微服务或提升架构能力的工程师; 使用场景及目标:①学习如何基于Spring Cloud Alibaba构建完整的微服务项目;②掌握秒杀场景下高并发、超卖控制、异步化、削峰填谷等关键技术方案;③实践分布式事务(Seata)、服务熔断降级、链路追踪、统一配置中心等企业级中间件的应用;④完成从本地开发到容器化部署的全流程落地; 阅读建议:建议按照文档提供的七个阶段循序渐进地动手实践,重点关注秒杀流程设计、服务间通信机制、分布式事务实现和系统性能优化部分,结合代码调试与监控工具深入理解各组件协作原理,真正掌握高并发微服务系统的构建能力。
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