Question

我有一些与买卖比特币有关的清单。一个是（买入或卖出）价格，另一个是相关日期。当我绘制在不同时间段与不同时间段内从购买/出售中赚取（或损失）的总资金时，结果是“震荡”-并非我所期望的。而且我认为我的逻辑可能是错误的

我的原始输入列表如下：

dates=['2013-05-12 00:00:00', '2013-05-13 00:00:00', '2013-05-14 00:00:00', ....]

prices=[114.713, 117.18, 114.5, 114.156,...]

#simple moving average of prices calced over a short period
sma_short_list = [None, None, None, None, 115.2098, 116.8872, 118.2272, 119.42739999999999, 121.11219999999999, 122.59219999999998....]

#simple moving average of prices calced over a longer period
sma_long_list = [...None, None, None, None, 115.2098, 116.8872, 118.2272, 119.42739999999999, 121.11219999999999, 122.59219999999998....]

基于移动平均交叉（基于https://stackoverflow.com/a/14884058/2089889计算），我将以发生交叉的日期/价格买卖比特币。

我想弄清楚（这种方法到今天为止能给我带来多少收益）与（几天前我开始采用这种方法的时间）

但是

我遇到麻烦的是，生成的图确实很不连贯。首先，我认为这是因为我的买入多于卖出（反之亦然），所以我尝试考虑这一点。但这仍然很不稳定。注意，以下代码在循环for days_ago in reversed(range(0,approach_started_days_ago)):中被调用，因此每次执行以下代码时，它都应该吐出如果我 days_ago开始使用该方法会赚多少钱 em>（我称为 bank ），而断断续续的情节是 days_ago 与 bank

dates = data_dict[file]['dates'] prices = data_dict[file]['prices'] sma_short_list = data_dict[file]['sma'][str(sma_short)] sma_long_list = data_dict[file]['sma'][str(sma_long)] prev_diff=0 bank = 0.0 buy_amt, sell_amt = 0.0,0.0 buys,sells, amt, first_tx_amt, last_tx_amt=0,0,0, 0, 0 start, finish = len(dates)-days_ago,len(dates) for j in range(start, finish): diff = sma_short_list[j]-sma_long_list[j] amt=prices[j] #If a crossover of the moving averages occured if diff*prev_diff<0: if first_tx_amt==0: first_tx_amt = amt #BUY if diff>=0 and prev_diff<=0: buys+=1 bank = bank - amt #buy_amt = buy_amt+amt #print('BUY ON %s (PRICE %s)'%(dates[j], prices[j])) #SELL elif diff<=0 and prev_diff>=0: sells+=1 bank = bank + amt #sell_amt = sell_amt + amt #print('SELL ON %s (PRICE %s)'%(dates[j], prices[j])) prev_diff=diff last_tx_amt=amt #if buys > sells, subtract last if buys > sells: bank = bank + amt elif sells < buys: bank = bank - amt #THIS IS RELATED TO SOME OTHER APPROACH I TRIED #a = (buy_amt) / buys if buys else 0 #b = (sell_amt) / sells if sells else 0 #diff_of_sum_of_avg_tx_amts = a - b start_date = datetime.now()-timedelta(days=days_ago) return bank, start_date

Answer 1

我认为在“银行”中的金额就是我已售出的金额-我已购买的金额

但是，如果第一个跨界交易是一个卖出交易，我就不愿计算在内（我假设我做的第一个交易将是一个买入交易。

然后，如果我最后一笔交易是买入的（对我的银行不利），我会将今天的价格计入我的“银行”中

if last_tx_type=='buy':
    sell_amt=sell_amt+prices[len(prices)-1] #add the current amount to the sell amount if the last purchase you made is a buy
if sell_first==True:
    sell_amt = sell_amt - first_tx_amt #if the first thing you did was sell, you do not want to add this to money made b/c it was with apriori money

bank = sell_amt-buy_amt

绘制（时间段内的离散总和）与（时间段）的关系会产生具有不连续性的图形

1 个答案: