def run():
codes = list(get_all_stock_codes())
codes.reverse()
codes = ['000725']
result_list = []
for code in tqdm(codes):
df = get_stock_k_line(code, date_start='2016-01-01')
code_dict = line_hammer_and_hang(code, df)
result_list.append(code_dict)
code_dict = shape_dark_cloud_cover_top(code, df)
result_list.append(code_dict)
code_dict = shape_devour(code, df)
result_list.append(code_dict)
code_dict = shape_morning_and_evening_star(code, df)
result_list.append(code_dict)
code_dict = shape_meteor_and_invertedhammer_star(code, df)
result_list.append(code_dict)
for result in result_list:
for code, d in result.items():
if d['up']:
print code, " up:", d['up']
for result in result_list:
for code, d in result.items():
if d['down']:
print code, " down:", d['down']
print 'end'
def run():
codes = list(get_all_stock_codes())
codes.reverse()
#codes = ['603999']
result_list = []
for code in tqdm(codes):
df = get_stock_k_line(code, date_start='2016-01-01')
#code_dict = line_hammer_and_hang(code, df)
#code_dict = shape_devour(code, df)
#code_dict = shape_morning_and_evening_star(code, df)
code_dict = shape_meteor_and_invertedhammer_star(code, df)
result_list.append(code_dict)
for result in result_list:
for code, d in result.items():
if d['up']:
print code, " up:", d['up']
for result in result_list:
for code, d in result.items():
if d['down']:
print code, " down:", d['down']
print 'end'
def fibonacci():
df_gem = ts.get_gem_classified() #创业板
for ix, row in df_gem.iterrows():
code = row['code']
df_price = dg.get_stock_k_line(code, date_start='2015-01-04')
print df_price.head()
returns = df_price['close'].pct_change()
returns[0] = 0
print returns
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
def tread_track_backtest(code, df=None):
df = get_stock_k_line(code, date_start='2015-12-30', date_end='2016-05-05')
if len(df) == 0:
return None
print df.head(2)
# 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]
#print df
# try:
# df = df.reindex(df.index[::-1])
# except Exception as e:
# print str(e)
# return
#print df
close_price = df['close'].get_values()
ma_short = pd.rolling_mean(df['close'], prama_ma_short)
ma_long = pd.rolling_mean(df['close'], 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")
# 判断极点
# for i in range(prama_ma_short+1, len(ma_short)-1):
# if ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[i+1]:
# print '极大值:', ma_short[i], 'pos:', i
#
# elif ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[i+1]:
# print '极小值:', ma_short[i], 'pos:', i
# 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]
xticklabels = df['date'].apply(lambda x: str(x)).get_values()
# print xticklabels[:5]
# print xticklabels[-5:]
#plt.plot(df['date'].get_values(), ma_short.get_values(), label=code, color="r", linewidth=1)
#plt.plot(df['date'].get_values(), ma_short.get_values(), color="r")
def wavlet_plt():
# trick to get the axes
fig, ax = plt.subplots()
xValues = close_price[-200:]
ax.plot(xValues, label=code, color="r", linewidth=1)
zValues = cwavelet.getWaveletData(xValues, 'db2', 2, 'sqtwolog')
zxValue = np.arange(0, len(zValues), 1)
#plt.figure(figsize=(16,8))
ax.plot(zxValue, zValues, color="b", linewidth=2)
ax.grid()
# make ticks and tick labels
xticks = range(0, len(xValues) + 1, 10)
#xticklabels=['2000-01-0'+str(n) for n in range(1,len(xValues)+1)]
xticklabels = df['date'].apply(lambda x: str(x)).get_values()
# set ticks and tick labels
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels, rotation=15)
#plt.legend()
plt.show()
min_index_pre = 0 #前一个极小值
max_index_pre = 0 #前一个极大值
keep_stop_up_price = 0
keep_stop_down_price = 0
stockPos = StockPosition(50000, code)
#for i in range(prama_ma_short+1, len(ma_short)-1):
for i in range(prama_ma_long - 1, len(ma_short) - 1):
# print prama_ma_long, len(ma_short)-1
# print ma_short[prama_ma_long]
# print ma_long[prama_ma_long]
#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
while (ma_short[index_post] == ma_short[i]
and index_post < len(ma_short) - 1):
index_post += 1
# 长均线保护策略:
# bLongMA_forbid_buy = ma_short[i] < ma_long[i] # 买入保护
# bLongMA_forbid_sale = not bLongMA_forbid_buy # 卖出保护
#
# if bLongMA_forbid_buy and signal == SIGNAL_BUY:
# signal = SIGNAL_SALE
# stockPos.sale(close_price[i], str(df.ix[i, 'date']))
# min_index_pre = 0
# max_index_pre = 0
# keep_stop_up_price = 0
# keep_stop_down_price = 0
# continue
# if bLongMA_forbid_buy and signal == SIGNAL_SALE:
# continue
# if len(stockPos.transaction_records): # 有交易以后才考虑此情况
# signal = SIGNAL_BUY
# stockPos.buy(close_price[i], str(df.ix[i, 'date']))
# #print '买入:', close_price[i], ' ', str(df.ix[i, 'date'])
# continue
# 高低点比较策略
if ma_short[i] > ma_short[
i - 1] and ma_short[i] > ma_short[index_post]: # 极大值
# <卖出> 如果当前高点比前一个高点的向下漂移项低，则空头开仓或持有空头；
# <买入> 如果当前高点比前一个高点的向上漂移项高，则多头开仓或持有多头；
if max_index_pre > 0 and i > max_index_pre:
if signal == SIGNAL_BUY and ma_short[i] < ma_short[
max_index_pre] - drift * (i - max_index_pre):
signal = SIGNAL_SALE
stockPos.sale(close_price[i], str(df.ix[i, 'date']))
min_index_pre = 0
max_index_pre = 0
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
elif signal == SIGNAL_SALE and ma_short[i] > ma_short[
max_index_pre] + drift * (i - max_index_pre):
signal = SIGNAL_BUY
stockPos.buy(close_price[i], str(df.ix[i, 'date']))
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
max_index_pre = i
elif ma_short[i] < ma_short[
i - 1] and ma_short[i] < ma_short[index_post]: # 极小值
# < 买入 > 如果当前低点比前一个低点的向上漂移项高，则多头开仓或持有多头；
# < 卖出 > 如果当前低点比前一个低点的向下漂移项低，则空头开仓或持有空头
if min_index_pre > 0 and i > min_index_pre:
if signal == SIGNAL_SALE and ma_short[i] > ma_short[
min_index_pre] + drift * (i - min_index_pre):
signal = SIGNAL_BUY
stockPos.buy(close_price[i], str(df.ix[i, 'date']))
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
elif signal == SIGNAL_BUY and ma_short[i] > ma_short[
min_index_pre] - drift * (i - min_index_pre):
signal = SIGNAL_SALE
stockPos.sale(close_price[i], str(df.ix[i, 'date']))
min_index_pre = 0
max_index_pre = 0
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
min_index_pre = i
# 高低点突破策略
# <卖出>如果滤波后的均线比前一个低点的向下漂移项低，则空头开仓或持有空头。
elif 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
stockPos.sale(close_price[i], str(df.ix[i, 'date']))
min_index_pre = 0
max_index_pre = 0
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
# <买入>如果滤波后的均线比前一个高点的向上漂移项高，则多头开仓或持有多头。
elif 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
stockPos.buy(close_price[i], str(df.ix[i, 'date']))
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
# 大波段保护策略
## 向上的大波段
if min_index_pre > 0 and close_price[i] >= ma_short[min_index_pre] * (
1 + param_big_band):
if close_price[i] * (1 - protect_big_band) > keep_stop_up_price:
keep_stop_up_price = close_price[i] * (1 - protect_big_band
) # 止盈位
keep_stop_up_index = i
if signal == SIGNAL_BUY and keep_stop_up_price > 0 and \
close_price[i] < keep_stop_up_price + drift*(i-keep_stop_up_index):
signal = SIGNAL_SALE
stockPos.sale(close_price[i], str(df.ix[i, 'date']))
min_index_pre = 0
max_index_pre = 0
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
## 向下的大波段
if max_index_pre > 0 and close_price[i] <= ma_short[max_index_pre] * (
1 - param_big_band):
if keep_stop_down_price == 0 or close_price[i] * (
1 + protect_big_band) < keep_stop_down_price:
keep_stop_down_price = close_price[i] * (1 + protect_big_band
) # 止损位
keep_stop_down_index = i
if signal == SIGNAL_SALE and keep_stop_down_price > 0 and \
close_price[i] > keep_stop_down_price + drift * (i + keep_stop_down_index):
signal = SIGNAL_BUY
keep_stop_up_price = 0
keep_stop_down_price = 0
stockPos.buy(close_price[i], str(df.ix[i, 'date']))
continue
stockPos.current_price = close_price[-1]
stockPos.summary()
print '交易结束'
#plt.grid()
#plt.legend()
#plt.show()
return stockPos
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
def tread_track_backtest(code, df=None):
df = get_stock_k_line(code, date_start='2015-12-30', date_end='2016-05-05')
if len(df) == 0:
return None
print df.head(2)
# 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]
#print df
# try:
# df = df.reindex(df.index[::-1])
# except Exception as e:
# print str(e)
# return
#print df
close_price = df['close'].get_values()
ma_short = pd.rolling_mean(df['close'], prama_ma_short)
ma_long = pd.rolling_mean(df['close'], 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")
# 判断极点
# for i in range(prama_ma_short+1, len(ma_short)-1):
# if ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[i+1]:
# print '极大值:', ma_short[i], 'pos:', i
#
# elif ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[i+1]:
# print '极小值:', ma_short[i], 'pos:', i
# 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]
xticklabels = df['date'].apply(lambda x : str(x)).get_values()
# print xticklabels[:5]
# print xticklabels[-5:]
#plt.plot(df['date'].get_values(), ma_short.get_values(), label=code, color="r", linewidth=1)
#plt.plot(df['date'].get_values(), ma_short.get_values(), color="r")
def wavlet_plt():
# trick to get the axes
fig,ax = plt.subplots()
xValues = close_price[-200:]
ax.plot(xValues, label=code, color="r", linewidth=1)
zValues = cwavelet.getWaveletData(xValues, 'db2', 2, 'sqtwolog')
zxValue = np.arange(0,len(zValues),1)
#plt.figure(figsize=(16,8))
ax.plot(zxValue, zValues, color="b", linewidth=2)
ax.grid()
# make ticks and tick labels
xticks=range(0, len(xValues)+1,10)
#xticklabels=['2000-01-0'+str(n) for n in range(1,len(xValues)+1)]
xticklabels = df['date'].apply(lambda x : str(x)).get_values()
# set ticks and tick labels
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels,rotation=15)
#plt.legend()
plt.show()
min_index_pre = 0 #前一个极小值
max_index_pre = 0 #前一个极大值
keep_stop_up_price = 0
keep_stop_down_price = 0
stockPos = StockPosition(50000, code)
#for i in range(prama_ma_short+1, len(ma_short)-1):
for i in range(prama_ma_long-1, len(ma_short)-1):
# print prama_ma_long, len(ma_short)-1
# print ma_short[prama_ma_long]
# print ma_long[prama_ma_long]
#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
while (ma_short[index_post] == ma_short[i] and index_post < len(ma_short) - 1):
index_post += 1
# 长均线保护策略:
# bLongMA_forbid_buy = ma_short[i] < ma_long[i] # 买入保护
# bLongMA_forbid_sale = not bLongMA_forbid_buy # 卖出保护
#
# if bLongMA_forbid_buy and signal == SIGNAL_BUY:
# signal = SIGNAL_SALE
# stockPos.sale(close_price[i], str(df.ix[i, 'date']))
# min_index_pre = 0
# max_index_pre = 0
# keep_stop_up_price = 0
# keep_stop_down_price = 0
# continue
# if bLongMA_forbid_buy and signal == SIGNAL_SALE:
# continue
# if len(stockPos.transaction_records): # 有交易以后才考虑此情况
# signal = SIGNAL_BUY
# stockPos.buy(close_price[i], str(df.ix[i, 'date']))
# #print '买入:', close_price[i], ' ', str(df.ix[i, 'date'])
# continue
# 高低点比较策略
if ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[index_post]: # 极大值
# <卖出> 如果当前高点比前一个高点的向下漂移项低，则空头开仓或持有空头；
# <买入> 如果当前高点比前一个高点的向上漂移项高，则多头开仓或持有多头；
if max_index_pre > 0 and i > max_index_pre:
if signal == SIGNAL_BUY and ma_short[i] < ma_short[max_index_pre] - drift*(i-max_index_pre):
signal = SIGNAL_SALE
stockPos.sale(close_price[i], str(df.ix[i, 'date']))
min_index_pre = 0
max_index_pre = 0
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
elif signal == SIGNAL_SALE and ma_short[i] > ma_short[max_index_pre] + drift*(i-max_index_pre):
signal = SIGNAL_BUY
stockPos.buy(close_price[i], str(df.ix[i, 'date']))
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
max_index_pre = i
elif ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[index_post]: # 极小值
# < 买入 > 如果当前低点比前一个低点的向上漂移项高，则多头开仓或持有多头；
# < 卖出 > 如果当前低点比前一个低点的向下漂移项低，则空头开仓或持有空头
if min_index_pre > 0 and i > min_index_pre:
if signal == SIGNAL_SALE and ma_short[i] > ma_short[min_index_pre] + drift*(i-min_index_pre):
signal = SIGNAL_BUY
stockPos.buy(close_price[i], str(df.ix[i, 'date']))
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
elif signal == SIGNAL_BUY and ma_short[i] > ma_short[min_index_pre] - drift*(i-min_index_pre):
signal = SIGNAL_SALE
stockPos.sale(close_price[i], str(df.ix[i, 'date']))
min_index_pre = 0
max_index_pre = 0
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
min_index_pre = i
# 高低点突破策略
# <卖出>如果滤波后的均线比前一个低点的向下漂移项低，则空头开仓或持有空头。
elif 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
stockPos.sale(close_price[i], str(df.ix[i, 'date']))
min_index_pre = 0
max_index_pre = 0
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
# <买入>如果滤波后的均线比前一个高点的向上漂移项高，则多头开仓或持有多头。
elif 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
stockPos.buy(close_price[i], str(df.ix[i, 'date']))
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
# 大波段保护策略
## 向上的大波段
if min_index_pre > 0 and close_price[i] >= ma_short[min_index_pre] * (1 + param_big_band):
if close_price[i] * (1 - protect_big_band) > keep_stop_up_price:
keep_stop_up_price = close_price[i] * (1 - protect_big_band) # 止盈位
keep_stop_up_index = i
if signal == SIGNAL_BUY and keep_stop_up_price > 0 and \
close_price[i] < keep_stop_up_price + drift*(i-keep_stop_up_index):
signal = SIGNAL_SALE
stockPos.sale(close_price[i], str(df.ix[i, 'date']))
min_index_pre = 0
max_index_pre = 0
keep_stop_up_price = 0
keep_stop_down_price = 0
continue
## 向下的大波段
if max_index_pre > 0 and close_price[i] <= ma_short[max_index_pre] * (1 - param_big_band):
if keep_stop_down_price == 0 or close_price[i] * (1 + protect_big_band) < keep_stop_down_price:
keep_stop_down_price = close_price[i] * (1 + protect_big_band) # 止损位
keep_stop_down_index = i
if signal == SIGNAL_SALE and keep_stop_down_price > 0 and \
close_price[i] > keep_stop_down_price + drift * (i + keep_stop_down_index):
signal = SIGNAL_BUY
keep_stop_up_price = 0
keep_stop_down_price = 0
stockPos.buy(close_price[i], str(df.ix[i, 'date']))
continue
stockPos.current_price = close_price[-1]
stockPos.summary()
print '交易结束'
#plt.grid()
#plt.legend()
#plt.show()
return stockPos
def tread_track_backtest(code, df=None):
df = get_stock_k_line(code)
# 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]
#print df
# try:
# df = df.reindex(df.index[::-1])
# except Exception as e:
# print str(e)
# return
#print df
close_price = df['close'].get_values()
ma_short = pd.rolling_mean(df['close'], prama_ma_short)
ma_long = pd.rolling_mean(df['close'], 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")
# 判断极点
# for i in range(prama_ma_short+1, len(ma_short)-1):
# if ma_short[i] > ma_short[i-1] and ma_short[i] > ma_short[i+1]:
# print '极大值:', ma_short[i], 'pos:', i
#
# elif ma_short[i] < ma_short[i-1] and ma_short[i] < ma_short[i+1]:
# print '极小值:', ma_short[i], 'pos:', i
# 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(df.index, ma_short.get_values(), label=code, color="r", linewidth=1)
# trick to get the axes
fig,ax = plt.subplots()
xValues = close_price[-200:]
ax.plot(xValues, label=code, color="r", linewidth=1)
zValues = cwavelet.getWaveletData(xValues, 'db2', 2, 'sqtwolog')
zxValue = np.arange(0,len(zValues),1)
#plt.figure(figsize=(16,8))
ax.plot(zxValue, zValues, color="b", linewidth=2)
ax.grid()
# make ticks and tick labels
xticks=range(0, len(xValues)+1,10)
xticklabels=['2000-01-0'+str(n) for n in range(1,len(xValues)+1)]
# set ticks and tick labels
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels,rotation=15)
#plt.legend()
plt.show()
# 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)-180, 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
