def __init__(self, stockCsvPath = '', stockData='', df=''):
self.AVR_SHORT = 12
self.AVR_LONG = 40
if len(stockCsvPath) > 0:
#方式一
df_stockHistory = get_stock_k_line(stockData.code)
if len(df_stockHistory) == 0:
return
#df_stockHistory = pd.read_csv(stockCsvPath)
# 将数据按照交易日期从远到近排序
df_stockHistory.sort('date', inplace=True)
self.close_price = df_stockHistory['close'].get_values()
needWrite2Csv = False
try:
self.ma_12 = df_stockHistory['ma_12'].get_values()
self.ma_40 = df_stockHistory['ma_40'].get_values()
except Exception as e:
print '重新计算 ma'
self.ma_12 = pd.rolling_mean(self.close_price, self.AVR_SHORT)
self.ma_40 = pd.rolling_mean(self.close_price, self.AVR_LONG)
needWrite2Csv = True
df_stockHistory['ma_12'] = self.ma_12
df_stockHistory['ma_40'] = self.ma_40
try:
self.ema_12 = df_stockHistory['ema_12'].get_values()
self.ema_40 = df_stockHistory['ema_40'].get_values()
except Exception as e:
print '重新计算 ema'
self.ema_12 = pd.ewma(self.close_price, span=self.AVR_SHORT)
self.ema_40 = pd.ewma(self.close_price, span=self.AVR_LONG)
needWrite2Csv = True
df_stockHistory['ema_12'] = self.ema_12
df_stockHistory['ema_40'] = self.ema_40
if needWrite2Csv:
# 将数据按照交易日期从近到远排序
df_stockHistory.sort('date', ascending = False, inplace=True)
df_stockHistory.to_csv(stockCsvPath)
print '保存至'+stockCsvPath
self.close_price = np.append(self.close_price, float(stockData.current))
# 计算当前的ma
last_ma_12 = sum(self.close_price[-self.AVR_SHORT:])/self.AVR_SHORT;
last_ma_40 = sum(self.close_price[-self.AVR_LONG:])/self.AVR_LONG;
self.ma_12 = np.append(self.ema_12, last_ma_12)
self.ma_40 = np.append(self.ema_40, last_ma_40)
# 计算当前价的ema
last_ema_12 = self.ema_12[-2] * (self.AVR_SHORT-1)/(self.AVR_SHORT+1) + stockData.current * 2/(self.AVR_SHORT+1)
last_ema_40 = self.ema_40[-2] * (self.AVR_LONG-1)/(self.AVR_LONG+1) + stockData.current * 2/(self.AVR_LONG+1)
#self.close_price = np.append(self.close_price, float(stockData.current))
self.ema_12 = np.append(self.ema_12, last_ema_12)
self.ema_40 = np.append(self.ema_40, last_ema_40)
else:
#方式二
# 将数据按照交易日期从远到近排序
df.sort('date', inplace=True)
self.close_price = df['close'].get_values()
self.ma_12 = pd.rolling_mean(self.close_price, self.AVR_SHORT)
self.ma_40 = pd.rolling_mean(self.close_price, self.AVR_LONG)
self.ema_12 = pd.ewma(self.close_price, span=self.AVR_SHORT)
self.ema_40 = pd.ewma(self.close_price, span=self.AVR_LONG)
def tread_track_live_trading(code, price_now, df=None):
# 分析某个时间段的股票
#dateS = datetime.datetime.today().date() + datetime.timedelta(-100)
#date_start = dateS.strftime("%Y-%m-%d")
#df = df[df.index > date_start]
try:
df = get_stock_k_line(code)
df = df.reindex(df.index[::-1])
except Exception as e:
print str(e)
return
#print df
close_price = df['close'].get_values()
close_price = np.append(close_price, float(price_now))
ma_short = pd.rolling_mean(close_price, prama_ma_short)
ma_long = pd.rolling_mean(close_price, prama_ma_long)
#print ma_short
signal = SIGNAL_SALE
print '交易开始'
plt.figure(figsize=(16,8))
#plt.plot(range(len(ma_short)), ma_short.get_values(), label=code, color="b", linewidth=1)
plt.xlabel("Time")
plt.ylabel("Price")
# 过滤微小波动
extreIndex = -1 #极值点的索引
for i in range(prama_ma_short+1, len(ma_short)-1):
bMax = ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[i+1] # 极大值的条件
bMin = ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[i+1] # 极小值的条件
if bMax or bMin:
extreIndex = i
elif extreIndex > 0:
if ma_short[i] > ma_short[extreIndex]*(1-filter_range) and \
ma_short[i] < ma_short[extreIndex]*(1+filter_range):
ma_short[i] = ma_short[extreIndex]
#print i,ma_short[i]
#plt.plot(range(len(ma_short.get_values())), ma_short.get_values(), label=code, color="r", linewidth=1)
# 交易次数
count_sale = 0
count_buy = 0
min_index_pre = 0 #前一个极小值
max_index_pre = 0 #前一个极大值
# 止损位
keep_stop_price = 0
keep_stop_index = 0
# 止赢位
keep_win_price = 0
keep_win_index = 0
total = 0
price_buy = 0
price_init = 0
money_init = 50000
stock_money = money_init
stock_count = 0
#for i in range(prama_ma_short+1, len(ma_short)-1):
for i in range(len(ma_short)-30, len(ma_short)-1):
#滤波后的均线走平时（即处于滤波区间内），将其识别为一个点
index_post = i+1
try:
while(ma_short[index_post] == ma_short[i] and index_post < len(ma_short)-1):
index_post += 1
except Exception as e:
print str(e)
# 长均线保护策略
bLongMA_protect_close = True
try:
bLongMA_protect_close = ma_short[i] > ma_long[i] # 长均线保护是否关闭
except Exception as e:
print str(e)
#if bLongMA_protect_close == False:
#print "长均线保护打开", i
# 高低点比较策略
if ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[index_post]:
#print '极大值:', ma_short[i], 'pos:', i
if bLongMA_protect_close and max_index_pre > 0 and ma_short[i] < ma_short[max_index_pre] + drift*(i-max_index_pre) and signal == SIGNAL_BUY:
signal = SIGNAL_SALE
print '卖出:', close_price[i], 'pos:', i
count_sale += 1
total += close_price[i] - price_buy
stock_money += stock_count * close_price[i]
stock_count = 0
max_index_pre = i
elif ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[index_post]:
#print '极小值:', ma_short[i], 'pos:', i
if bLongMA_protect_close and min_index_pre > 0 and ma_short[i] > ma_short[min_index_pre] + drift*(i-min_index_pre) and signal == SIGNAL_SALE:
signal = SIGNAL_BUY
print '买入:', close_price[i], 'pos:', i
count_buy += 1
price_buy = close_price[i]
if price_init == 0:
price_init = price_buy
stock_count = (stock_money/100)/close_price[i]*100
stock_money = stock_money - stock_count*close_price[i]
min_index_pre = i
# 高低点突破策略
# 股价突破前一个低点加漂移项，则卖出
elif bLongMA_protect_close and signal == SIGNAL_BUY and min_index_pre > 0 and \
ma_short[i] < ma_short[min_index_pre] + drift*(i-min_index_pre):
signal = SIGNAL_SALE
print '卖出:', close_price[i], 'pos:', i
count_sale += 1
total += close_price[i] - price_buy
stock_money += stock_count * close_price[i]
stock_count = 0
# 股价突破前一个高点加漂移项，则买入
elif bLongMA_protect_close and signal == SIGNAL_SALE and max_index_pre > 0 and \
ma_short[i] > ma_short[max_index_pre] + drift*(i-max_index_pre):
signal = SIGNAL_BUY
print '买入:', close_price[i], 'pos:', i
count_buy += 1
price_buy = close_price[i]
stock_count = (stock_money/100)/close_price[i]*100
stock_money = stock_money - stock_count*close_price[i]
# 大波段保护策略
elif min_index_pre > 0 and ma_short[i] >= ma_short[min_index_pre]*(1+param_big_band):
keep_stop_price = ma_short[i] * (1-protect_big_band) #止损位
keep_stop_index = i
elif bLongMA_protect_close and signal == SIGNAL_BUY and keep_stop_price > 0 and \
ma_short[i] < keep_stop_price + drift*(i-keep_stop_index):
signal = SIGNAL_SALE
print '卖出:', close_price[i], 'pos:', i
count_sale += 1
total += close_price[i] - price_buy
stock_money += stock_count * close_price[i]
stock_count = 0
print "buy count:", count_buy
print "sale count:", count_sale
if stock_count > 0:
stock_money += stock_count * close_price[-1]
total += close_price[-1] - price_buy
print "每股盈利：", total, "收益率：", (stock_money-money_init)/money_init*100,"%\n"
print '交易结束'
plt.grid()
#plt.legend()
#plt.show()
return (stock_money-money_init)/money_init*100
