fix ix to iloc

bobolvm · Sep 7, 2017 · a6b1b50 · a6b1b50
1 parent b5cef93
commit a6b1b50
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Showing 8 changed files with 167 additions and 32 deletions.
diff --git a/abupy/FactorBuyBu/ABuBuyFactorWrap.py b/abupy/FactorBuyBu/ABuBuyFactorWrap.py
@@ -57,7 +57,7 @@ def fit_month_wrapped(*args, **kwargs):
                 if benchmark_today.empty:
                     return 0
                 # 要拿大盘最近一个月的走势，准备切片的start，end
-                end_key = int(benchmark_today.ix[0].key)
+                end_key = int(benchmark_today.iloc[0].key)
                 start_key = end_key - 20
                 if start_key < 0:
                     return 0

diff --git a/abupy/FactorBuyBu/ABuFactorBuyDemo.py b/abupy/FactorBuyBu/ABuFactorBuyDemo.py
@@ -39,7 +39,7 @@ def fit_month(self, today):
         if benchmark_today.empty:
             return 0
         # 要拿大盘最近一个月的走势，准备切片的start，end
-        end_key = int(benchmark_today.ix[0].key)
+        end_key = int(benchmark_today.iloc[0].key)
         start_key = end_key - 20
         if start_key < 0:
             return False

diff --git a/abupy/MLBu/ABuMLPd.py b/abupy/MLBu/ABuMLPd.py
@@ -248,7 +248,7 @@ def btc_siblings_df(self, btc_raw):
         """
 
         # 将所有交易日以3个为一组，切割成多个子df，即每一个子df中有3个交易日的交易数据
-        btc_siblings = [btc_raw.ix[sib_ind * 3:(sib_ind + 1) * 3, :]
+        btc_siblings = [btc_raw.iloc[sib_ind * 3:(sib_ind + 1) * 3, :]
                         for sib_ind in np.arange(0, int(btc_raw.shape[0] / 3))]
 
         btc_df = pd.DataFrame()

diff --git a/abupy/TLineBu/ABuTLExecute.py b/abupy/TLineBu/ABuTLExecute.py
@@ -217,7 +217,7 @@ def calc_pair_speed(symbol, benchmark_symbol, resample=5, speed_key='close',
                     start=None, end=None, n_folds=2, show=False):
     """
     参数传递一组symbol对，获取symbol对的金融时间序列数据，根据speed_key获取曲线序列数据，
-    分别通过calc_kl_speed计算symbol对的趋势跟随速度
+    分别通过calc_kl_speed计算symbol对的趋势跟随速度，相关性＊敏感度＝敏感度置信度
     :param symbol: eg: 'AU0'
     :param benchmark_symbol: eg: 'XAU'
     :param resample: 计算速度数值的重采样周期默认5
@@ -226,16 +226,20 @@ def calc_pair_speed(symbol, benchmark_symbol, resample=5, speed_key='close',
     :param end: 获取金融时间序列的end时间
     :param n_folds: 获取金融时间序列的n_folds参数
     :param show: 是否可视化symbol对的趋势走势对比
-    :return: 参数symbol, benchmark_symbol所对应的趋势变化敏感速度数值
+    :return: 参数symbol, benchmark_symbol所对应的趋势变化敏感速度数值，以及相关性＊敏感度＝敏感度置信度
     """
     from ..TradeBu import AbuBenchmark
+    from ..SimilarBu import ABuCorrcoef, ECoreCorrType
+
     benchmark = AbuBenchmark(benchmark_symbol, start=start, end=end, n_folds=n_folds)
     benchmark_kl = benchmark.kl_pd
     kl = ABuSymbolPd.make_kl_df(symbol, benchmark=benchmark,
                                 data_mode=EMarketDataSplitMode.E_DATA_SPLIT_UNDO)
     # 通过calc_kl_speed计算趋势跟随速度
     kl_speed = calc_kl_speed(kl[speed_key], resample)
     benchmark_kl_speed = calc_kl_speed(benchmark_kl[speed_key], resample)
+    # 两个走势的SPERM相关性
+    corr = ABuCorrcoef.corr_xy(kl.close, benchmark_kl.close, ECoreCorrType.E_CORE_TYPE_SPERM)
 
     if show:
         # 可视化symbol对的趋势走势对比
@@ -246,8 +250,8 @@ def calc_pair_speed(symbol, benchmark_symbol, resample=5, speed_key='close',
         kl_resamp.plot(label='kl', style=['*--'])
         benchmark_kl_resamp.plot(label='benchmark', style=['^--'])
         plt.legend(loc='best')
-    # 返回参数symbol, benchmark_symbol所对应的趋势变化敏感速度数值
-    return kl_speed, benchmark_kl_speed
+    # 返回参数symbol, benchmark_symbol所对应的趋势变化敏感速度数值, 以及相关性＊敏感度＝敏感度置信度
+    return kl_speed, benchmark_kl_speed, corr
 
 
 def shift_distance_how(how):

diff --git a/abupy_lecture/12-机器学习与比特币示例(ABU量化使用文档).ipynb b/abupy_lecture/12-机器学习与比特币示例(ABU量化使用文档).ipynb
@@ -316,7 +316,7 @@
    "source": [
     "def btc_siblings_df(btc_raw):\n",
     "    # 将所有交易日以3个为一组，切割成多个子df，即每一个子df中有3个交易日的交易数据\n",
-    "    btc_siblings = [btc_raw.ix[sib_ind * 3:(sib_ind + 1) * 3, :]\n",
+    "    btc_siblings = [btc_raw.iloc[sib_ind * 3:(sib_ind + 1) * 3, :]\n",
     "                    for sib_ind in np.arange(0, int(btc_raw.shape[0] / 3))]\n",
     "\n",
     "    btc_df = pd.DataFrame()\n",

diff --git a/abupy_lecture/13-量化技术分析应用(ABU量化使用文档).ipynb b/abupy_lecture/13-量化技术分析应用(ABU量化使用文档).ipynb
@@ -498,7 +498,7 @@
     "                                                   sell_factors,\n",
     "                                                   choice_symbols=['usTSLA'])\n",
     "\n",
-    "order = abu_result_tuple.orders_pd.ix[0]"
+    "order = abu_result_tuple.orders_pd.iloc[0]"
    ]
   },
   {

diff --git a/abupy_lecture/25-期货市场内外盘低频统计套利.ipynb b/abupy_lecture/25-期货市场内外盘低频统计套利.ipynb
diff --git a/abupy_lecture/9-港股市场的回测(ABU量化使用文档).ipynb b/abupy_lecture/9-港股市场的回测(ABU量化使用文档).ipynb
@@ -593,7 +593,7 @@
     "        if benchmark_today.empty:\n",
     "            return 0\n",
     "        # 要拿大盘最近一个月的走势，准备切片的start，end\n",
-    "        end_key = int(benchmark_today.ix[0].key)\n",
+    "        end_key = int(benchmark_today.iloc[0].key)\n",
     "        start_key = end_key - 20\n",
     "        if start_key < 0:\n",
     "            return False\n",
@@ -834,7 +834,7 @@
     "                if benchmark_today.empty:\n",
     "                    return 0\n",
     "                # 要拿大盘最近一个月的走势，准备切片的start，end\n",
-    "                end_key = int(benchmark_today.ix[0].key)\n",
+    "                end_key = int(benchmark_today.iloc[0].key)\n",
     "                start_key = end_key - 20\n",
     "                if start_key < 0:\n",
     "                    return 0\n",