如何解决stocktsats Stoch RSI 值与交易视图值不匹配
def good_buying_condition_stoch_ema_ema(the_exchange,symbol,timeframe):
#print ("finding the good to go long/buy signal")
r = dict();
hist_data = get_historical_data(the_exchange,timeframe)
stock_data = create_stock(hist_data) #stock stats data
stock_data.KDJ_WINDOW=14
stock_data.KDJ_ParaM=(3.0 / 14.0,3.0 / 14.0)
#stock_data._calc_kd(stock_data['close']);
stock_data.MACD_EMA_SHORT = 50
stock_data.MACD_EMA_LONG = 200
macd = stock_data._get_macd(stock_data)
gap = stock_data['kdjk'][499] - stock_data['kdjd'][499]
abs_gap = abs(gap);
r['datetime'] = stock_data['timestamp'][499]
r['symbol'] = symbol
r['timeframe'] = timeframe
r['close'] = stock_data['close'][499]
r['kdjk3'] = stock_data['kdjk_3'][499]
r['kdjd3'] = stock_data['kdjd_3'][499]
r['kdjk'] = stock_data['kdjk'][499]
r['kdjd'] = stock_data['kdjd'][499]
r['macd'] = stock_data['macd'][499]
stock_data.KDJ_WINDOW=7
stock_data._calc_kd(stock_data['close']);
r['kdjk7'] = stock_data['kdjk_7'][499]
r['kdjd7'] = stock_data['kdjd_7'][499]
stock_data.KDJ_WINDOW=14
stock_data._calc_kd(stock_data['close']);
r['kdjk14'] = stock_data['kdjk_14'][499]
r['kdjd14'] = stock_data['kdjd_14'][499]
if (gap < 1 and gap > -1): r['side'] = 'HODL'
elif (gap > 1): r['side'] = 'buy'
elif (gap < -1): r['side'] = 'sell'
else: r['side'] = 'unkNown'
if (r['side'] == "buy"):
if (stock_data['kdjk'][499] < 20): r['confidence'] = "strong"
elif (stock_data['kdjk'][499] < 75): r['confidence'] = "ok"
elif (stock_data['kdjk'][499] < 80): r['confidence'] = "questionable"
elif (stock_data['kdjk'][499] < 90): r['confidence'] = "risky"
elif (stock_data['kdjk'][499] < 100): r['confidence'] = "extreme risk"
elif (r['side'] == "sell"):
if (stock_data['kdjk'][499] < 20): r['confidence'] = "extreme risk"
elif (stock_data['kdjk'][499] < 40): r['confidence'] = "risky"
elif (stock_data['kdjk'][499] < 60): r['confidence'] = "questionable"
elif (stock_data['kdjk'][499] < 80): r['confidence'] = "ok"
elif (stock_data['kdjk'][499] < 100): r['confidence'] = "strong"
else: r['confidence'] = 'unkNown'
r['momentum'] = gap
if (stock_data['macd'][499] > 0 and stock_data['macd'][499] < 1):
r['trend'] = "bullish"
elif (stock_data['macd'][499] > 1 ):
r['trend'] = "uncertain"
elif (stock_data['macd'][499] < 0 ):
r['trend'] = "bearish"
#### FINTA tech indicators
#ohlc = resample(hist_data,"1m")
#ema50 = TA.EMAIndicator(close=hist_data.close,n=50)
#hist_data['ema50_tpj'] = ema50
#print ("TA.Stoch K = ",TA.Stoch(hist_data['close'],period=14)[499])
#print ("TA.Stoch D = ",TA.StochD(hist_data['close'],period=3,stoch_period=14)[499])
#print ("TA.EMA 9 = ",TA.EMA(hist_data)[499])
#print ("TA.EMA 50 = ",TA.EMA(hist_data,50)[499])
#print ("TA.EMA 200 = ",200)[499])
r['ta_ema9'] = TA.EMA(hist_data)[499]
r['ta_ema50'] = TA.EMA(hist_data,50)[499]
r['ta_ema200'] = TA.EMA(hist_data,200)[499]
r['ta_atr14'] = TA.ATR(hist_data,14)[499]
r['atr14'] = stock_data['atr'][499]
r['min14'] = hist_data.close.rolling(14).min()[499];
r['max14'] = hist_data.close.rolling(14).max()[499];
print ("returning",r)
return r
def get_historical_data(exchange,coin_pair,timeframe):
"""Get Historical data (ohlcv) from a coin_pair
"""
# optional: exchange.fetch_ohlcv(coin_pair,'1h',since)
data = exchange.fetch_ohlcv(coin_pair,timeframe)
# update timestamp to human readable timestamp
data = [[exchange.iso8601(candle[0])] + candle[1:] for candle in data]
header = ['Timestamp','Open','High','Low','Close','Volume']
df = pd.DataFrame(data,columns=header)
return df
def create_stock(historical_data):
"""Create StockData from historical data using stockstats
"""
stock = Sdf.retype(historical_data)
return stock
在 10 秒的循环中，上面产生以下输出：
returning {'datetime': '2021-05-26T15:55:00.000Z','symbol': 'XRP/USDT','timeframe': '1m','close': 0.9838,'kdjk3': 23.00115833047544,'kdjd3': 33.246639262609804,'kdjk': 31.557468092520086,'kdjd': 40.89122272232644,'macd': -0.00016899252758151295,'kdjk7': 29.02460416563654,'kdjd7': 40.37714935820455,'kdjk14': 31.502465082807905,'kdjd14': 41.35215340927536,'side': 'sell','confidence': 'risky','momentum': -9.333754629806354,'trend': 'bearish','ta_ema9': 0.9863129810243016,'ta_ema50': 0.9876962538671861,'ta_ema200': 0.9980103854792317,'ta_atr14': 0.003885714285714286,'atr14': 0.0038138082006646855,'min14': 0.9827,'max14': 0.9927}
returning {'datetime': '2021-05-26T15:56:00.000Z','close': 0.9831,'kdjk3': 14.450607500341013,'kdjd3': 26.42224572620021,'kdjk': 23.26053428390232,'kdjd': 34.77728953914806,'macd': -0.000447425790014111,'kdjk7': 18.859864087371616,'kdjd7': 32.94481190275474,'kdjk14': 23.223865610760864,'kdjd14': 35.07235377273352,'momentum': -11.516755255245737,'ta_ema9': 0.9856063848194415,'ta_ema50': 0.9875009374853446,'ta_ema200': 0.9978533449719774,'ta_atr14': 0.0036571428571428666,'atr14': 0.0035699647577600697,'max14': 0.9927}
baseCurrBal: 556.21882609 percent: .4 ticker['last']: 0.984
returning {'datetime': '2021-05-26T15:56:00.000Z','kdjk3': 20.006163055896863,'kdjd3': 28.27409757805216,'kdjk': 25.127200950569065,'kdjd': 35.399511761370306,'macd': -0.00039158533417382735,'kdjk7': 20.88884959461808,'kdjd7': 33.62114040517023,'kdjk14': 25.09053227742761,'kdjd14': 35.69457599495577,'momentum': -10.27231081080124,'ta_ema9': 0.9857463848194415,'ta_ema50': 0.9875283884657932,'ta_ema200': 0.9978603573934625,'ta_atr14': 0.003707142857142861,'atr14': 0.003619964757760064,'close': 0.9841,'kdjk3': 22.387115436848994,'kdjd3': 29.06774837170287,'kdjk': 25.927200950568974,'kdjd': 35.66617842803694,'macd': -0.0003676537102420552,'kdjk7': 21.758414812009285,'kdjd7': 33.91099547763397,'kdjk14': 25.890532277427518,'kdjd14': 35.96124266162241,'momentum': -9.73897747746797,'ta_ema9': 0.9858063848194416,'ta_ema50': 0.9875401531716997,'ta_ema200': 0.997863362716956,'max14': 0.9927}
baseCurrBal: 556.21882609 percent: .4 ticker['last']: 0.9839
returning {'datetime': '2021-05-26T15:56:00.000Z','close': 0.9843,'kdjk3': 23.974417024150412,'kdjd3': 29.596848900803344,'kdjk': 26.46053428390225,'kdjd': 35.8439562058147,'macd': -0.0003516992942875774,'kdjk7': 22.338124956936753,'kdjd7': 34.10423219260979,'kdjk14': 26.423865610760792,'kdjd14': 36.13902043940016,'momentum': -9.38342192191245,'ta_ema9': 0.9858463848194414,'ta_ema50': 0.9875479963089707,'ta_ema200': 0.9978653662659518,'ta_atr14': 0.003721428571428574,'atr14': 0.003634250472045777,'close': 0.9848,'kdjk3': 27.94267099240484,'kdjd3': 30.91960022355482,'kdjk': 27.79386761723572,'kdjd': 36.28840065025919,'macd': -0.0003118132544015495,'kdjk7': 23.787400319255745,'kdjd7': 34.587323980049455,'kdjk14': 27.757198944094263,'kdjd14': 36.583464883844655,'momentum': -8.494533033023473,'ta_ema9': 0.9859463848194416,'ta_ema50': 0.9875676041521483,'ta_ema200': 0.997870375138441,'ta_atr14': 0.0038000000000000095,'atr14': 0.0037128219006172126,'max14': 0.9927}
baseCurrBal: 556.21882609 percent: .4 ticker['last']: 0.9842
当我比较所有 kdj* 值时，我看不到与交易视图的标准 stoch rsi 和 3/3/14/14/close 参数在同一时间段内的值有很强的相似性。我已经多次调整 KDJ_WINDOW 和 KDJ_ParaM 值，几乎所有对我来说都有意义的 3、7 和 14 组合；我试过调用 _calc_kd 而没有调用，我试过在 _calc_kd 调用之间调整 KDJ_WINDODW 和 KDJ_ParaM；我试图只引用 ['kdjk'] 和 ['kdjd'];我曾尝试使用 https://www.programmersought.com/article/69583827158/ 中提到的 ['kdjk_N'] 列，但我无法密切复制交易视图值...
我错过了什么/做错了什么？
（注意 1：任何对交换的引用都是针对 ccxt 交换对象的）。 （注 2：我正在使用 stockstats ）
解决方法
我最终使用了以下手动计算： 我最终使用了以下手动计算：
stock_data['rsi1'] = stock_data['rsi_14']
stock_data['rsi_L14'] = stock_data['rsi1'].rolling(window=14).min()
stock_data['rsi_H14'] = stock_data['rsi1'].rolling(window=14).max()
stock_data['stoch'] = 100*((stock_data['rsi1'] - stock_data['rsi_L14']) / (stock_data['rsi_H14'] - stock_data['rsi_L14']) )
stock_data['my_k2'] = stock_data['stoch'].rolling(window=3).mean()
stock_data['my_d2'] = stock_data['my_k2'].rolling(window=3).mean()
r['kdjk'] = stock_data['my_k2'][499]
r['kdjd'] = stock_data['my_d2'][499]
我找到了 this pine 脚本，并使用了交易视图中的解释 explanation 和来自 this page 的解释来提出真正的算法。
我随后在交易视图中交叉引用了 xrp/usdt 的实时 1m 图表，并且值几乎完全对齐。
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