def plot_line_percent(stock_code,
buy_date,
start=6,
end=2,
win_status='green'):
"""
百分比方式绘图
:return:
"""
from abupy import code_to_symbol
stock_code_str = code_to_symbol(stock_code).value
kl_pd, df_req_start, df_req_end = load_kline_df(stock_code_str)
temp = kl_pd[kl_pd.date == buy_date]['key']
if kl_pd[kl_pd.date == buy_date]['key'].size == 0:
return
kl_pd_key = kl_pd[kl_pd.date == buy_date]['key'].values[0]
kl_pd_back = kl_pd.iloc[kl_pd_key - start:kl_pd_key + end, :] # 获得数据
kl_pd_back['key2'] = list(range(0, len(kl_pd_back)))
pre_close = kl_pd[kl_pd.key == (kl_pd_key -
1)]['close'].values[0] # 前一天收盘价
rate = 100 / pre_close
kl_pd_back['close2'] = kl_pd_back['close'].map(lambda x: x * rate)
plt.plot(kl_pd_back.key2,
kl_pd_back.close2,
label='p_change',
color=win_status)
def plot_result_volumn_sum(best_result_tuple, count=None):
"""
可视化绘制回测结果累计交易量
:param count: 要绘制的数量
"""
orders_pd = best_result_tuple.orders_pd[
0:count if count is not None else 1000]
for key, order in orders_pd.iterrows():
buy_date = order.buy_date
stock_code = order.symbol
win_status = 'red' if order.sell_type == 'win' else 'green'
from abupy import code_to_symbol
stock_code = code_to_symbol(stock_code).value
kl_pd, df_req_start, df_req_end = load_kline_df(stock_code)
kl_pd_key = kl_pd[kl_pd.date == buy_date]['key'].values[0]
kl_pd_back = kl_pd.iloc[kl_pd_key - 10:kl_pd_key + 1, :] # 获得数据
kl_pd_back['key2'] = list(range(0, len(kl_pd_back)))
kl_pd_back['volume_sum'] = kl_pd_back['volume'].cumsum()
pre_data = kl_pd_back[kl_pd.key == (kl_pd_key -
1)]['volume_sum'].values[0]
rate = 100 / pre_data
kl_pd_back['volume2'] = kl_pd_back['volume_sum'].map(
lambda x: 500 if ((x * rate) > 500) else (x * rate))
plt.plot(kl_pd_back.key2,
kl_pd_back.volume2,
label='p_change',
color=win_status)
# plt.show()
plt.show()
def plot_result_price(best_result_tuple, count=None):
"""
可视化绘制回测结果价格
:param orders: AbuOrder对象序列
:param kl_pd: 金融时间序列，pd.DataFrame对象
:return:
"""
orders_pd = best_result_tuple.orders_pd[
0:count if count is not None else 1000]
for key, order in orders_pd.iterrows():
buy_date = order.buy_date
stock_code = order.symbol
win_status = 'red' if order.sell_type == 'win' else 'green'
from abupy import code_to_symbol
stock_code = code_to_symbol(stock_code).value
kl_pd, df_req_start, df_req_end = load_kline_df(stock_code)
kl_pd_key = kl_pd[kl_pd.date == buy_date]['key'].values[0]
kl_pd_back = kl_pd.iloc[kl_pd_key - 30:kl_pd_key + 10, :] # 获得数据
kl_pd_back['key2'] = list(range(0, len(kl_pd_back)))
# buy_price = kl_pd[kl_pd.date == buy_date]['close'].values[0]
pre_close = kl_pd[kl_pd.key == (kl_pd_key - 1)]['close'].values[0]
rate = 100 / pre_close
kl_pd_back['close2'] = kl_pd_back['close'].map(lambda x: x * rate)
# plt.plot(kl_pd_back.key2, kl_pd_back.p_change, label='p_change',color = win_status)
plt.plot(kl_pd_back.key2,
kl_pd_back.close2,
label='p_change',
color=win_status)
# plt.show()
plt.show()
def plot_order_wave_myself(order, start=100, end=30):
"""
绘制周期图
:param order: AbuOrder对象序列
"""
stockCode = order.symbol
from abupy import code_to_symbol
sh_stock_code = code_to_symbol(stockCode).value
kl_pd, df_req_start, df_req_end = load_kline_df(sh_stock_code)
mask_date = kl_pd['date'] == order.buy_date
st_key = kl_pd[mask_date]['key']
if order.sell_type == 'keep':
rv_pd = kl_pd[kl_pd.key >= st_key.values[0]] # 买入到卖出之间的数据
else:
mask_sell_date = kl_pd['date'] == order.sell_date # 卖出日期
st_sell_key = kl_pd[mask_sell_date]['key'] # 卖出日期key
rv_pd = kl_pd[kl_pd.key >= st_key.values[0]]
rv_pd = rv_pd[rv_pd.key < st_sell_key.values[0] + 1]
from abupy import ABuMarketDrawing
kl_pd = kl_pd[kl_pd.key >= (st_key.values[0] - start)]
kl_pd = kl_pd[kl_pd.key < (st_sell_key.values[0] + end)]
kl_pd.name = stockCode
benchmark = IndexSymbol.SH
bench_pd = ABuSymbolPd.make_kl_df(
benchmark,
data_mode=EMarketDataSplitMode.E_DATA_SPLIT_UNDO,
n_folds=None,
start=None,
end=None)
bench_mask_date = bench_pd['date'] == order.buy_date # 买入日期
bench_st_key = bench_pd[bench_mask_date]['key'] # 买入日期key
if order.sell_type == 'keep':
rv_pd = bench_pd[bench_pd.key >= bench_st_key.values[0]] # 买入到卖出之间的数据
else:
bench_mask_sell_date = bench_pd['date'] == order.sell_date # 卖出日期
bench_st_sell_key = bench_pd[bench_mask_sell_date]['key'] # 卖出日期key
rv_pd = bench_pd[bench_pd.key >= bench_st_key.values[0]]
rv_pd = rv_pd[rv_pd.key < bench_st_sell_key.values[0] + 1]
bench_pd = bench_pd[bench_pd.key >= (bench_st_key.values[0] - start)]
bench_pd = bench_pd[bench_pd.key < (bench_st_sell_key.values[0] + end)]
from abupy.UtilBu.AbuJiJinDataUtil import jiJinPlotWave
jiJinPlotWave(kl_pd=kl_pd,
bench_pd=bench_pd,
jiJinCodes=[stockCode],
windowBuy=18,
windowSell=18,
poly=50,
showWindowBuy=True,
showWindowSell=True,
keepDays=rv_pd)
def plot_order_limit_info_myself(order, index, ax_cnt=4, start=60, end=100):
"""
绘制单个订单卖出信息最近几天信息
:param order: AbuOrder对象序列
"""
stock_code = order.symbol
from abupy import code_to_symbol
sh_stock_code = code_to_symbol(stock_code).value
kl_pd, df_req_start, df_req_end = load_kline_df(sh_stock_code)
all_pd = kl_pd
fig_dims = (ax_cnt, 1)
mask_date = all_pd['date'] == order.buy_date
st_key = all_pd[mask_date]['key']
all_pd = all_pd[all_pd.key > st_key.values[0] - start]
all_pd = all_pd[all_pd.key < st_key.values[0] + end]
plt.subplot2grid(fig_dims, (index, 0))
# 绘制价格曲线
plt.plot(all_pd.index, all_pd['close'], label='close')
plot_order_share_myself(all_pd, st_key, order, order.symbol)
ABuEnv.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx #作用同上点击效果。腾讯数据源(美股，A股，港股)
ABuEnv.g_market_target = EMarketTargetType.E_MARKET_TARGET_CN
register_matplotlib_converters()
stock_code = '600988' #中欧医疗健康混合A
# stock_code = '162703' #广发小盘成长混合（LOF）
stock_name = 'xxx'
start_date = '2020-02-12' # 设置起始日期
end_date = '2020-02-20' # 设置终止日期
# stock_k = ts.get_hist_data(stock_code, start=start_date, end=end_date)
from abupy import code_to_symbol, IndexSymbol, EMarketDataSplitMode, EMarketDataFetchMode, EMarketSourceType
sh_stock_code = code_to_symbol(stock_code).value
stock_k, df_req_start, df_req_end = load_kline_df(sh_stock_code) #读取已存在的数据
kl_pd = ABuSymbolPd.make_kl_df(
stock_code,
data_mode=EMarketDataSplitMode.E_DATA_SPLIT_UNDO,
benchmark=None,
n_folds=1)
# temp1 = ts.get_hist_data('161005',start='2020-02-03',end='2020-02-22',ktype='D')
#ktype：数据类型，D=日k线 W=周 M=月 5=5分钟 15=15分钟 30=30分钟 60=60分钟，默认为D
kl_pd = get_and_store_jijin_data('519005')
kl_pd = kl_pd.tail(300)
kl_pd['date2'] = [
int(datetime.datetime.strptime(dateTemp, '%Y-%m-%d').strftime('%Y%m%d'))
for dateTemp in kl_pd.date
]
print('test')
if score_tuple_array != None:
scorer = WrsmScorer(score_tuple_array, weights=[1, 0, 0, 0])
test = scorer.score_pd
scorer_returns_max = scorer.fit_score()
best_result_tuple = score_tuple_array[scorer_returns_max.index[-1]]
orders_pd = best_result_tuple.orders_pd[0:4000]
key_value = 1000 * 10000
order_pds_win = None
order_pds_loss = None
order_pds = None
for key, order in orders_pd.iterrows():
# break
buy_date = order.buy_date
stock_code = order.symbol #股票代码
sh_stock_code = code_to_symbol(stock_code).value
kl_pd, df_req_start, df_req_end = load_kline_df(sh_stock_code)
kl_pd_key = kl_pd[kl_pd.date == buy_date]['key'].values[0]
kl_pd_yestaday = kl_pd.iloc[kl_pd_key - 1] # 获得买入前一天数据
yestadayDate = kl_pd_yestaday.date
timeArray = time.strptime(str(int(yestadayDate)), "%Y%m%d")
date_str = time.strftime("%Y-%m-%d", timeArray)
stock_detail_data = get_and_store_stock_detail_data(
stock_code, date_str)
if stock_detail_data is None:
continue
today_max_data = stock_detail_data[stock_detail_data.price ==
kl_pd_yestaday.close]
con_all = stock_detail_data.shape[0]
def plot_order_candle_myself(order, start=60, end=10):
"""
绘制订单蜡烛图
:param order: AbuOrder对象序列
"""
stock_code = order.symbol
from abupy import code_to_symbol
sh_stock_code = code_to_symbol(stock_code).value
all_pd, df_req_start, df_req_end = load_kline_df(sh_stock_code)
mask_date = all_pd['date'] == order.buy_date
st_key = all_pd[mask_date]['key']
if order.sell_type == 'keep':
rv_pd = all_pd[all_pd.key >= st_key.values[0]] # 买入到卖出之间的数据
st_sell_key = all_pd[mask_date]['key']
else:
mask_sell_date = all_pd['date'] == order.sell_date # 卖出日期
st_sell_key = all_pd[mask_sell_date]['key'] # 卖出日期key
rv_pd = all_pd[all_pd.key >= st_key.values[0]]
rv_pd = rv_pd[rv_pd.key < st_sell_key.values[0] + 1]
tempList = []
for k in rv_pd.index:
tempList.append(k)
if hasattr(order, 'bigWave2Close2'):
all_pd['bigWave2Close2'] = order.bigWave2Close2
# all_pd['ema10'] = get_EMA(all_pd.close, 10)
# all_pd['ema60'] = get_EMA(all_pd.close, 60)
# tempList.append(order.bigWave2Close2Date)
from abupy import ABuMarketDrawing
all_pd = all_pd[all_pd.key >= (st_key.values[0] - start)]
all_pd = all_pd[all_pd.key < (st_sell_key.values[0] + end)]
all_pd.name = stock_code
benchmark = IndexSymbol.SH
bench_pd = ABuSymbolPd.make_kl_df(
benchmark,
data_mode=EMarketDataSplitMode.E_DATA_SPLIT_UNDO,
n_folds=None,
start=None,
end=None)
bench_mask_date = bench_pd['date'] == order.buy_date # 买入日期
bench_st_key = bench_pd[bench_mask_date]['key'] # 买入日期key
# bench_pd = bench_pd[bench_pd.key > bench_st_key.values[0] - start]
# bench_pd = bench_pd[bench_pd.key < bench_st_key.values[0] + end]
if order.sell_type == 'keep':
rv_pd = bench_pd[bench_pd.key >= bench_st_key.values[0]] # 买入到卖出之间的数据
bench_st_sell_key = bench_st_key # 卖出日期key
else:
bench_mask_sell_date = bench_pd['date'] == order.sell_date # 卖出日期
bench_st_sell_key = bench_pd[bench_mask_sell_date]['key'] # 卖出日期key
rv_pd = bench_pd[bench_pd.key >= bench_st_key.values[0]]
rv_pd = rv_pd[rv_pd.key < bench_st_sell_key.values[0] + 1]
bench_pd = bench_pd[bench_pd.key >= (bench_st_key.values[0] - start)]
bench_pd = bench_pd[bench_pd.key <= (bench_st_sell_key.values[0] + end)]
ABuMarketDrawing.plot_candle_form_klpd(all_pd,
html_bk=False,
view_indexs=tempList,
bench=bench_pd,
order=order,
save=True)