从该端点返回的JSON与您的模型不完全匹配。
下面这一行告诉程序如何解析响应：
股票当前股票信息= jsonConvert.DeserializeObject（读取器.读取到结束（））;
...但响应如下所示：

{
    "Meta Data": {
        "1. Information": "Daily Prices (open, high, low, close) and Volumes",
        "2. Symbol": "MSFT",
        "3. Last Refreshed": "2018-12-10 16:00:02",
        "4. Output Size": "Compact",
        "5. Time Zone": "US/Eastern"
    },
    "Time Series (Daily)": {
        "2018-12-10": {
            "1. open": "104.8000",
            "2. high": "107.9800",
            "3. low": "103.8900",
            "4. close": "107.5900",
            "5. volume": "39050766"
        },
        "2018-12-07": {
            "1. open": "108.3800",
            "2. high": "109.4500",
            "3. low": "104.3000",
            "4. close": "104.8200",
            "5. volume": "45044937"
        }...
    ...
    ...
    ...

}
你试图把整个响应变成一个Stock对象，这是行不通的。这个响应不是一个Stock，它是一个有两个对象的响应，其中一个有很多Stock对象。
您 * 可以 * 尝试为此创建一个模型 ，但我建议将整个响应转换为JObject（NewtonSoftJSON .NET中的另一个对象）。
下面是一个DotNetFiddle，我将它放在一起来演示它是如何工作的。
https://dotnetfiddle.net/Iz8UsD
如果我能补充更多有用的信息，请告诉我。
编辑： 您可能可以在这里使用强类型模型，问题是每个Stock都有不同的JSON名称。我不知道如何让解析器将每个Stock解析为Stock，同时仍然保留数据。今晚我将不得不使用它。

Joe解释的问题是，返回的JSON的形状不允许轻松Map到C#对象。
下面是一个使用C#从AlphaVantage检索股票价格的更简单/更快捷的方法。
它涉及到调用标准的API价格端点，但添加'&datatype=csv'作为URL参数。响应的格式使它很容易Map到C# Poco对象。在下面的示例中，我只是将它Map到AlphaVantageData对象。
下面是示例代码。您可以在Gystlin here中实时运行此代码

using System;
using System.Collections.Generic;
using System.Linq;
using ServiceStack;
using ServiceStack.Text;

public class AlphaVantageData
{
     public DateTime Timestamp { get; set; }
     public decimal Open { get; set; }

     public decimal High { get; set; }
     public decimal Low { get; set; }

     public decimal Close { get; set; }
     public decimal Volume { get; set; }
}

 // retrieve monthly prices for Microsoft
 var symbol = "MSFT";
 var monthlyPrices = $"https://www.alphavantage.co/query?function=TIME_SERIES_MONTHLY&symbol={symbol}&apikey=demo&datatype=csv"
            .GetStringFromUrl().FromCsv<List<AlphaVantageData>>();

 monthlyPrices.PrintDump();

 // some simple stats
 var maxPrice = monthlyPrices.Max(u => u.Close);
 var minPrice = monthlyPrices.Min(u => u.Close);

我发布这个回复是因为我有一个工作代码，它可以从AlphaVantage Time Series（Daily）解析出一个股票报价列表。然后它会对报价进行一些比较，寻找枢轴点。这部分需要工作，但列表可以从每日提要中正确获取。

using Newtonsoft.Json.Linq;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Net.Http;
using System.Text;

namespace StockQuotes
{
    public class Program
    {
        static void Main(string[] args)
        {

            List<string> quoteTickerSymbols = new List<string>();

            if (args.Length < 1)
            { quoteTickerSymbols.Add("msft"); }
            else
            {
                string[] data = args[0].Split(',');
                quoteTickerSymbols.Add(data.ToString());
            }

            //please use your own API key.
            string APIKey = "";
            string response = "";
            string nameOfStock = "";
            HttpClient client = new HttpClient();
            StringBuilder sb = new StringBuilder();
            List<StockQuote> quotes = new List<StockQuote>();

            for (int i = 0; i < quoteTickerSymbols.Count;)
            {
                sb.Append($"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=");
                sb.Append(quoteTickerSymbols[i]);
                sb.Append("&apikey=");
                sb.Append(APIKey);

                string request = sb.ToString();

                response = client.GetStringAsync(request).Result;
                JObject jsonData = JObject.Parse(response);

                nameOfStock = quoteTickerSymbols[i];
                try
                {
                    foreach (JToken item in jsonData.SelectTokens("['Time Series (Daily)']"))
                    {
                        foreach (var datedCollection in item.Children().Children())
                        {
                            sb.Clear();
                            string[] pathArray = datedCollection.Path.Split('.');

                            // the date that's after the Time Series (Daily) part of the path
                            sb.Append(pathArray[1]);

                            DataSetByDay dataSet = datedCollection.ToObject<DataSetByDay>();

                            var quote = datedCollection.Select(x => new StockQuote()
                            {
                                Name = nameOfStock,
                                date = Convert.ToDateTime(sb.ToString()),
                                open = dataSet.open,
                                high = dataSet.high,
                                low = dataSet.low,
                                close = dataSet.close,
                                volume = dataSet.volume
                            });

                            quotes.Add(quote.First());
                            dataSet = null;
                        }
                    }
                }
                catch (Exception err)
                {

                    // write out the issue and then skip the entry.
                    Console.WriteLine(err.Message, err.InnerException, err.Source);
                    i++;
                }

                // move to next token
                i++;

                // at the end, compare the quotes.
                for (int j = 0; j < quotes.Count() - 4; j++)
                {
                    if (quotes[j].open > quotes[j + 1].high && quotes[j].close < quotes[j + 1].low)
                    {
                        Console.ForegroundColor = ConsoleColor.Red;
                        Console.WriteLine("{0}", quotes[j].Name);
                        Console.WriteLine("Pivot downside {0}", quotes[j].date);
                    }
                    if (quotes[j].open < quotes[j + 1].low && quotes[j].close > quotes[j + 1].high)
                    {
                        Console.ForegroundColor = ConsoleColor.Green;
                        Console.WriteLine("{0}", quotes[j].Name);
                        Console.WriteLine("Pivot upside {0}", quotes[j].date);
                    }
                }
            }
        }
    }
}

这也是需要的。存储信息。

using Newtonsoft.Json;
using System;

namespace StockQuotes
{
    // each daily record from the website
    internal class DataSetByDay
    {
        [JsonProperty("1. open")]
        public decimal open { get; set; }

        [JsonProperty("2. high")]
        public decimal high { get; set; }

        [JsonProperty("3. low")]
        public decimal low { get; set; }

        [JsonProperty("4. close")]
        public decimal close { get; set; }
        [JsonProperty("5. volume")]
        public long volume { get; set; }
    }

    //this would append the date and the name of the stock to the time series.
    internal class StockQuote : DataSetByDay
    {
       public string Name { get; set; }
        public DateTime date { get; set; }
    }
}