121. 122. 123. 188. Best Time to Buy and Sell Stock *HARD* 309. Best Time to Buy and Sell Stock with...

本文探讨了股票交易中的多种策略及其算法实现,包括单次交易、多次交易、限制次数交易和冷却期交易等,涉及从基本算法到复杂场景的解决方法。

121.

Say you have an array for which the ith element is the price of a given stock on day i.

If you were only permitted to complete at most one transaction (ie, buy one and sell one share of the stock), design an algorithm to find the maximum profit.

class Solution {
public:
    int maxProfit(vector<int>& prices) {
        int n = prices.size();
        if(n < 1)
            return 0;
        int mini = prices[0], ans = 0, i;
        for(i = 1; i < n; i++)
        {
            if(prices[i]-mini > ans)
                ans = prices[i]-mini;
            if(prices[i] < mini)
                mini = prices[i];
        }
        return ans;
    }
};

 

122.

Say you have an array for which the ith element is the price of a given stock on day i.

Design an algorithm to find the maximum profit. You may complete as many transactions as you like (ie, buy one and sell one share of the stock multiple times). However, you may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again).

class Solution {
public:
    int maxProfit(vector<int>& prices) {
        int n = prices.size();
        if(n < 1)
            return 0;
        int mini = prices[0], maxi = prices[0], ans = 0, i;
        for(i = 1; i < n; i++)
        {
            if(prices[i] > maxi)
                maxi = prices[i];
            else if(prices[i] < maxi)
            {
                ans += maxi - mini;
                maxi = mini = prices[i];
            }
        }
        ans += maxi - mini;
        return ans;
    }
};

 

123.

Say you have an array for which the ith element is the price of a given stock on day i.

Design an algorithm to find the maximum profit. You may complete at most two transactions.

class Solution {
public:
    int maxProfit(vector<int>& prices) {
        int n = prices.size();
        if(n < 1)
            return 0;
        vector<int> forward(n, 0), backward(n, 0);
        int mini, maxi, ans, i;
        forward[0] = 0;
        mini = prices[0];
        for(i = 1; i < n; i++)
        {
            forward[i] = max(forward[i-1], prices[i] - mini);
            if(prices[i] < mini)
                mini = prices[i];
        }
        backward[n-1] = 0;
        maxi = prices[n-1];
        for(i = n-2; i >= 0; i--)
        {
            backward[i] = max(backward[i+1], maxi - prices[i]);
            if(prices[i] > maxi)
                maxi = prices[i];
        }
        ans = 0;
        for(i = 0; i < n; i++)
        {
            ans = max(ans, forward[i] + backward[i]);
        }
        return ans;
    }
};

 

188.

Say you have an array for which the ith element is the price of a given stock on day i.

Design an algorithm to find the maximum profit. You may complete at most k transactions.

Note:
You may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again).

class Solution {
public:
    int maxProfit(int k, vector<int>& prices) {
        int n = prices.size(), i, j;
        if(n < 1)
            return 0;
        if(k >= (n>>1))
        {
            int ans = 0;
            for(i = 0; i < n-1; i++)
            {
                if(prices[i+1]-prices[i] > 0)
                    ans += prices[i+1]-prices[i];
            }
            return ans;
        }
        vector<int> buy(k+1, INT_MIN), sell(k+1, 0);
        for(i = 0; i < n; i++)
        {
            for(j = 1; j <= k; j++)
            {
                buy[j] = max(buy[j], sell[j-1] - prices[i]);
                sell[j] = max(sell[j], buy[j] + prices[i]);
            }
        }
        return sell[k];
    }
};

buy[i]表示买i个最多剩多少钱。sell[i]表示卖i个最多有多少钱。

buy[j] = max(buy[j], sell[j-1] - prices[i]);  //看买prices[i]是否有原来划算
class Solution {
public:
    int maxProfit(int k, vector<int>& prices) {
        int n = prices.size(), i, j;
        if(n < 1)
            return 0;
        if(k >= (n>>1))
        {
            int ans = 0;
            for(i = 0; i < n-1; i++)
            {
                if(prices[i+1]-prices[i] > 0)
                    ans += prices[i+1]-prices[i];
            }
            return ans;
        }
        vector<vector<int>> dp(n, vector<int>(k+1, 0)); //dp[i][j]表示到第i天卖j个最多赚多少钱
        for(i = 1; i <= k; i++)
        {
            int buy = -prices[0];
            for(j = 0; j < n; j++)
            {
                dp[j][i] = max(j > 0 ? dp[j-1][i] : 0, buy + prices[j]);
                buy = max(buy, dp[j][i-1] - prices[j]);
            }
        }
        return dp[n-1][k];
    }
};

和上面一个算法思路一样。

 

309.

Say you have an array for which the ith element is the price of a given stock on day i.

Design an algorithm to find the maximum profit. You may complete as many transactions as you like (ie, buy one and sell one share of the stock multiple times) with the following restrictions:

  • You may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again).
  • After you sell your stock, you cannot buy stock on next day. (ie, cooldown 1 day)

Example:

prices = [1, 2, 3, 0, 2]
maxProfit = 3
transactions = [buy, sell, cooldown, buy, sell]
class Solution {
public:
    int maxProfit(vector<int>& prices) {
        int n = prices.size();
        if(n <= 1)
            return 0;
        vector<int> sell(n+1, 0);
        int buy = -prices[0], i;
        for(i = 2; i <= n; i++)
        {
            sell[i] = max(sell[i-1], buy + prices[i-1]);
            buy = max(buy, sell[i-2] - prices[i-1]);
        }
        return sell[n];
    }
};

sell[i-2]表示cooldown[i-1]。

转载于:https://www.cnblogs.com/argenbarbie/p/5459311.html

评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值