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BUG #520 ENH lowerbound guardrails MAINT refactor

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Kevin Johnson committed 2022-05-02 16:08:16 -07:00
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README.md
+6 -11
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@@ -96,7 +96,7 @@ Performance
-----------
* Pandas TA is fast, with or without **TA Lib** or **Numba** installed, but one is not penalized if they are installed.
* **TA Lib** computations are **enabled** by default. They can be disabled per indicator.
* The library includes a performance method, ```help(ta.performance)```, to check runtime indicator performance for a given _ohlcv_ DataFrame.
* The library includes a performance method, ```help(ta.speed_test)```, to check runtime indicator performance for a given _ohlcv_ DataFrame.
* Optionable **Multiprocessing** for a Pandas TA ```Study```.
* Check Indicator Speeds on your system with the [Indicator Speed Check Notebook](https://github.com/twopirllc/pandas-ta/tree/main/examples/Speed_Check.ipynb).
@@ -145,15 +145,10 @@ Pandas TA is used by Applications and Services like
<br/>
[Gamestonk Terminal](https://github.com/GamestonkTerminal/GamestonkTerminal)
[Open BB](https://openbb.co/) (previously Gamestonk Terminal)
-------------------
> Gamestonk Terminal is an awesome stock and crypto market terminal that has been developed for fun, while I saw my GME shares tanking. But hey, I like the stock 💎🙌.
<br/>
[MarketMaker Lite](https://github.com/MarketMakerLite)
-------------------
> Make the market you deserve. Market alerts, statistics and analytics, delivered through an innovative interface, made for retail investors.
> OpenBB is a leading open source investment analysis company.
We represent millions of investors who want to leverage state-of-the-art data science and machine learning technologies to make sense of raw unrefined data. Our mission is to make investment research effective, powerful and accessible to everyone.
<br/>
@@ -198,7 +193,7 @@ $ pip install pandas_ta[full]
Latest Version
--------------
Best choice! Version: *0.3.63b*
Best choice! Version: *0.3.64b*
* Includes all fixes and updates between **pypi** and what is covered in this README.
```sh
$ pip install -U git+https://github.com/twopirllc/pandas-ta
@@ -1305,7 +1300,7 @@ Back to [Contents](#contents)
# **Support**
Feeling generous, like the package or want to see it become more a mature package?
Like the package, want more indicators and features? Continued Support?
* Donations help cover data and API costs so platform indicators (like [TradingView](https://github.com/tradingview/)) are accurate.
* I appreciate **ALL** of those that have bought me Coffee/Beer/Wine et al. I greatly appreciate it! 😎
examples/PandasTA_Study_Examples.ipynb
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examples/Speed_Test.ipynb
+509 -507
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examples/TA_Analysis.ipynb
+286 -269
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@@ -48,7 +48,7 @@
"Pandas v1.3.0\n",
"mplfinance v0.12.7a17\n",
"\n",
"Pandas TA v0.3.54b0\n",
"Pandas TA v0.3.63b0\n",
"To install the Latest Version:\n",
"$ pip install -U git+https://github.com/twopirllc/pandas-ta\n",
"\n"
@@ -119,16 +119,26 @@
"output_type": "stream",
"text": [
"[!] Loading All: SPY, QQQ, AAPL, TSLA, BTC-USD\n",
"[i] Loaded SPY[D]: SPY_D.csv\n",
"[i] Analysis Time: 34.7742 ms (0.0348 s) for 5 columns (avg 6.9562 ms / col)\n",
"[i] Loaded QQQ[D]: QQQ_D.csv\n",
"[i] Analysis Time: 3.1180 ms (0.0031 s) for 5 columns (avg 0.6242 ms / col)\n",
"[i] Loaded AAPL[D]: AAPL_D.csv\n",
"[i] Analysis Time: 3.1966 ms (0.0032 s) for 5 columns (avg 0.6401 ms / col)\n",
"[i] Loaded TSLA[D]: TSLA_D.csv\n",
"[i] Analysis Time: 2.7987 ms (0.0028 s) for 5 columns (avg 0.5605 ms / col)\n",
"[i] Loaded BTC-USD[D]: BTC-USD_D.csv\n",
"[i] Analysis Time: 2.8410 ms (0.0028 s) for 5 columns (avg 0.5692 ms / col)\n"
"[+] Downloading[yahoo]: SPY[D]\n",
"[+] yf | SPY(7367, 7): 3196.9914 ms (3.1970 s)\n",
"[+] Saving: /Users/kj/av_data/SPY_D.csv\n",
"[i] Analysis Time: 34.3883 ms (0.0344 s) for 5 columns (avg 6.8791 ms / col)\n",
"[+] Downloading[yahoo]: QQQ[D]\n",
"[+] yf | QQQ(5825, 7): 4333.8306 ms (4.3338 s)\n",
"[+] Saving: /Users/kj/av_data/QQQ_D.csv\n",
"[i] Analysis Time: 3.3304 ms (0.0033 s) for 5 columns (avg 0.6667 ms / col)\n",
"[+] Downloading[yahoo]: AAPL[D]\n",
"[+] yf | AAPL(10434, 7): 4274.9140 ms (4.2749 s)\n",
"[+] Saving: /Users/kj/av_data/AAPL_D.csv\n",
"[i] Analysis Time: 3.6997 ms (0.0037 s) for 5 columns (avg 0.7406 ms / col)\n",
"[+] Downloading[yahoo]: TSLA[D]\n",
"[+] yf | TSLA(2981, 7): 3129.0620 ms (3.1291 s)\n",
"[+] Saving: /Users/kj/av_data/TSLA_D.csv\n",
"[i] Analysis Time: 3.0117 ms (0.0030 s) for 5 columns (avg 0.6030 ms / col)\n",
"[+] Downloading[yahoo]: BTC-USD[D]\n",
"[+] yf | BTC-USD(2784, 7): 3533.1249 ms (3.5331 s)\n",
"[+] Saving: /Users/kj/av_data/BTC-USD_D.csv\n",
"[i] Analysis Time: 3.6089 ms (0.0036 s) for 5 columns (avg 0.7226 ms / col)\n"
]
}
],
@@ -156,7 +166,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"QQQ (5801, 12)\n",
"QQQ (5825, 12)\n",
"Columns: Open, High, Low, Close, Volume, Dividends, Stock Splits, SMA_10, SMA_20, SMA_50, SMA_200, VOL_SMA_20\n"
]
}
@@ -228,74 +238,74 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-03-27</th>\n",
" <td>125.196966</td>\n",
" <td>126.519411</td>\n",
" <td>124.907379</td>\n",
" <td>126.297386</td>\n",
" <td>18472400</td>\n",
" <th>2017-05-01</th>\n",
" <td>131.702961</td>\n",
" <td>132.610327</td>\n",
" <td>131.645046</td>\n",
" <td>132.436569</td>\n",
" <td>24815500</td>\n",
" <td>0.0</td>\n",
" <td>126.491342</td>\n",
" <td>126.296040</td>\n",
" <td>123.630030</td>\n",
" <td>114.376488</td>\n",
" <td>19309820.00</td>\n",
" <td>129.452889</td>\n",
" <td>128.293103</td>\n",
" <td>127.098378</td>\n",
" <td>117.352839</td>\n",
" <td>20653180.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-28</th>\n",
" <td>126.297405</td>\n",
" <td>127.446093</td>\n",
" <td>126.017480</td>\n",
" <td>127.069633</td>\n",
" <td>23721900</td>\n",
" <th>2017-05-02</th>\n",
" <td>132.600685</td>\n",
" <td>132.716529</td>\n",
" <td>132.262846</td>\n",
" <td>132.658600</td>\n",
" <td>18345100</td>\n",
" <td>0.0</td>\n",
" <td>126.551437</td>\n",
" <td>126.387253</td>\n",
" <td>123.798677</td>\n",
" <td>114.491380</td>\n",
" <td>19665985.00</td>\n",
" <td>130.046537</td>\n",
" <td>128.540698</td>\n",
" <td>127.250689</td>\n",
" <td>117.480589</td>\n",
" <td>20385420.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-29</th>\n",
" <td>127.156459</td>\n",
" <td>127.706676</td>\n",
" <td>127.021320</td>\n",
" <td>127.658409</td>\n",
" <td>13710000</td>\n",
" <th>2017-05-03</th>\n",
" <td>132.359333</td>\n",
" <td>132.407600</td>\n",
" <td>131.876692</td>\n",
" <td>132.233856</td>\n",
" <td>23827600</td>\n",
" <td>0.0</td>\n",
" <td>126.592381</td>\n",
" <td>126.439514</td>\n",
" <td>123.986228</td>\n",
" <td>114.613563</td>\n",
" <td>19060795.00</td>\n",
" <td>130.577444</td>\n",
" <td>128.756921</td>\n",
" <td>127.382175</td>\n",
" <td>117.602671</td>\n",
" <td>20906800.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-30</th>\n",
" <td>127.610166</td>\n",
" <td>128.005935</td>\n",
" <td>127.494337</td>\n",
" <td>127.870796</td>\n",
" <td>16092400</td>\n",
" <th>2017-05-04</th>\n",
" <td>132.214573</td>\n",
" <td>132.658594</td>\n",
" <td>131.818804</td>\n",
" <td>132.282135</td>\n",
" <td>14630100</td>\n",
" <td>0.0</td>\n",
" <td>126.663233</td>\n",
" <td>126.534182</td>\n",
" <td>124.173210</td>\n",
" <td>114.736808</td>\n",
" <td>18867845.00</td>\n",
" <td>131.006993</td>\n",
" <td>129.001620</td>\n",
" <td>127.513663</td>\n",
" <td>117.726958</td>\n",
" <td>20037745.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-03-31</th>\n",
" <td>127.687432</td>\n",
" <td>128.131468</td>\n",
" <td>127.542646</td>\n",
" <td>127.783966</td>\n",
" <td>19841400</td>\n",
" <th>2017-05-05</th>\n",
" <td>132.619938</td>\n",
" <td>132.764725</td>\n",
" <td>132.127651</td>\n",
" <td>132.764725</td>\n",
" <td>19330700</td>\n",
" <td>0.0</td>\n",
" <td>126.729840</td>\n",
" <td>126.612949</td>\n",
" <td>124.359612</td>\n",
" <td>114.861100</td>\n",
" <td>19173780.00</td>\n",
" <td>131.487700</td>\n",
" <td>129.267070</td>\n",
" <td>127.664244</td>\n",
" <td>117.847383</td>\n",
" <td>20062670.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
@@ -312,74 +322,74 @@
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-03-21</th>\n",
" <td>350.200012</td>\n",
" <td>352.480011</td>\n",
" <td>345.579987</td>\n",
" <td>350.079987</td>\n",
" <td>73799100</td>\n",
" <th>2022-04-25</th>\n",
" <td>323.730011</td>\n",
" <td>329.899994</td>\n",
" <td>322.429993</td>\n",
" <td>329.579987</td>\n",
" <td>101755900</td>\n",
" <td>0.0</td>\n",
" <td>334.295990</td>\n",
" <td>336.622971</td>\n",
" <td>350.758813</td>\n",
" <td>367.121709</td>\n",
" <td>81985850.00</td>\n",
" <td>338.041000</td>\n",
" <td>349.745001</td>\n",
" <td>344.924885</td>\n",
" <td>367.586838</td>\n",
" <td>67700730.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-03-22</th>\n",
" <td>350.589996</td>\n",
" <td>357.850006</td>\n",
" <td>350.200012</td>\n",
" <td>356.959991</td>\n",
" <td>63345900</td>\n",
" <th>2022-04-26</th>\n",
" <td>327.470001</td>\n",
" <td>327.660004</td>\n",
" <td>316.859985</td>\n",
" <td>317.140015</td>\n",
" <td>105819600</td>\n",
" <td>0.0</td>\n",
" <td>337.696915</td>\n",
" <td>337.587843</td>\n",
" <td>350.310193</td>\n",
" <td>367.231533</td>\n",
" <td>80854790.00</td>\n",
" <td>335.666000</td>\n",
" <td>347.356502</td>\n",
" <td>344.335056</td>\n",
" <td>367.366986</td>\n",
" <td>70150015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-03-23</th>\n",
" <td>354.010010</td>\n",
" <td>357.660004</td>\n",
" <td>351.769989</td>\n",
" <td>351.829987</td>\n",
" <td>70615500</td>\n",
" <th>2022-04-27</th>\n",
" <td>317.239990</td>\n",
" <td>322.880005</td>\n",
" <td>315.000000</td>\n",
" <td>316.760010</td>\n",
" <td>111204200</td>\n",
" <td>0.0</td>\n",
" <td>339.422278</td>\n",
" <td>338.728680</td>\n",
" <td>349.753981</td>\n",
" <td>367.314911</td>\n",
" <td>80074795.00</td>\n",
" <td>333.397000</td>\n",
" <td>344.635002</td>\n",
" <td>343.729037</td>\n",
" <td>367.145234</td>\n",
" <td>72306575.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-03-24</th>\n",
" <td>353.799988</td>\n",
" <td>359.700012</td>\n",
" <td>351.589996</td>\n",
" <td>359.649994</td>\n",
" <td>53383700</td>\n",
" <th>2022-04-28</th>\n",
" <td>321.850006</td>\n",
" <td>329.890015</td>\n",
" <td>317.519989</td>\n",
" <td>328.010010</td>\n",
" <td>99450000</td>\n",
" <td>0.0</td>\n",
" <td>342.301181</td>\n",
" <td>339.707703</td>\n",
" <td>349.240109</td>\n",
" <td>367.436991</td>\n",
" <td>76213275.00</td>\n",
" <td>331.563000</td>\n",
" <td>342.681003</td>\n",
" <td>343.175432</td>\n",
" <td>366.976494</td>\n",
" <td>73841190.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-03-25</th>\n",
" <td>359.589996</td>\n",
" <td>360.660004</td>\n",
" <td>354.943787</td>\n",
" <td>355.645813</td>\n",
" <td>24863691</td>\n",
" <th>2022-04-29</th>\n",
" <td>323.700012</td>\n",
" <td>327.230011</td>\n",
" <td>312.600006</td>\n",
" <td>313.250000</td>\n",
" <td>91856700</td>\n",
" <td>0.0</td>\n",
" <td>345.465820</td>\n",
" <td>340.222841</td>\n",
" <td>348.615591</td>\n",
" <td>367.521584</td>\n",
" <td>73514354.55</td>\n",
" <td>329.045001</td>\n",
" <td>340.216502</td>\n",
" <td>342.328425</td>\n",
" <td>366.746658</td>\n",
" <td>75083455.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -387,47 +397,47 @@
"</div>"
],
"text/plain": [
" open high low close volume \\\n",
"Date \n",
"2017-03-27 125.196966 126.519411 124.907379 126.297386 18472400 \n",
"2017-03-28 126.297405 127.446093 126.017480 127.069633 23721900 \n",
"2017-03-29 127.156459 127.706676 127.021320 127.658409 13710000 \n",
"2017-03-30 127.610166 128.005935 127.494337 127.870796 16092400 \n",
"2017-03-31 127.687432 128.131468 127.542646 127.783966 19841400 \n",
"... ... ... ... ... ... \n",
"2022-03-21 350.200012 352.480011 345.579987 350.079987 73799100 \n",
"2022-03-22 350.589996 357.850006 350.200012 356.959991 63345900 \n",
"2022-03-23 354.010010 357.660004 351.769989 351.829987 70615500 \n",
"2022-03-24 353.799988 359.700012 351.589996 359.649994 53383700 \n",
"2022-03-25 359.589996 360.660004 354.943787 355.645813 24863691 \n",
" open high low close volume \\\n",
"Date \n",
"2017-05-01 131.702961 132.610327 131.645046 132.436569 24815500 \n",
"2017-05-02 132.600685 132.716529 132.262846 132.658600 18345100 \n",
"2017-05-03 132.359333 132.407600 131.876692 132.233856 23827600 \n",
"2017-05-04 132.214573 132.658594 131.818804 132.282135 14630100 \n",
"2017-05-05 132.619938 132.764725 132.127651 132.764725 19330700 \n",
"... ... ... ... ... ... \n",
"2022-04-25 323.730011 329.899994 322.429993 329.579987 101755900 \n",
"2022-04-26 327.470001 327.660004 316.859985 317.140015 105819600 \n",
"2022-04-27 317.239990 322.880005 315.000000 316.760010 111204200 \n",
"2022-04-28 321.850006 329.890015 317.519989 328.010010 99450000 \n",
"2022-04-29 323.700012 327.230011 312.600006 313.250000 91856700 \n",
"\n",
" stock splits sma_10 sma_20 sma_50 sma_200 \\\n",
"Date \n",
"2017-03-27 0.0 126.491342 126.296040 123.630030 114.376488 \n",
"2017-03-28 0.0 126.551437 126.387253 123.798677 114.491380 \n",
"2017-03-29 0.0 126.592381 126.439514 123.986228 114.613563 \n",
"2017-03-30 0.0 126.663233 126.534182 124.173210 114.736808 \n",
"2017-03-31 0.0 126.729840 126.612949 124.359612 114.861100 \n",
"2017-05-01 0.0 129.452889 128.293103 127.098378 117.352839 \n",
"2017-05-02 0.0 130.046537 128.540698 127.250689 117.480589 \n",
"2017-05-03 0.0 130.577444 128.756921 127.382175 117.602671 \n",
"2017-05-04 0.0 131.006993 129.001620 127.513663 117.726958 \n",
"2017-05-05 0.0 131.487700 129.267070 127.664244 117.847383 \n",
"... ... ... ... ... ... \n",
"2022-03-21 0.0 334.295990 336.622971 350.758813 367.121709 \n",
"2022-03-22 0.0 337.696915 337.587843 350.310193 367.231533 \n",
"2022-03-23 0.0 339.422278 338.728680 349.753981 367.314911 \n",
"2022-03-24 0.0 342.301181 339.707703 349.240109 367.436991 \n",
"2022-03-25 0.0 345.465820 340.222841 348.615591 367.521584 \n",
"2022-04-25 0.0 338.041000 349.745001 344.924885 367.586838 \n",
"2022-04-26 0.0 335.666000 347.356502 344.335056 367.366986 \n",
"2022-04-27 0.0 333.397000 344.635002 343.729037 367.145234 \n",
"2022-04-28 0.0 331.563000 342.681003 343.175432 366.976494 \n",
"2022-04-29 0.0 329.045001 340.216502 342.328425 366.746658 \n",
"\n",
" vol_sma_20 \n",
"Date \n",
"2017-03-27 19309820.00 \n",
"2017-03-28 19665985.00 \n",
"2017-03-29 19060795.00 \n",
"2017-03-30 18867845.00 \n",
"2017-03-31 19173780.00 \n",
"... ... \n",
"2022-03-21 81985850.00 \n",
"2022-03-22 80854790.00 \n",
"2022-03-23 80074795.00 \n",
"2022-03-24 76213275.00 \n",
"2022-03-25 73514354.55 \n",
" vol_sma_20 \n",
"Date \n",
"2017-05-01 20653180.0 \n",
"2017-05-02 20385420.0 \n",
"2017-05-03 20906800.0 \n",
"2017-05-04 20037745.0 \n",
"2017-05-05 20062670.0 \n",
"... ... \n",
"2022-04-25 67700730.0 \n",
"2022-04-26 70150015.0 \n",
"2022-04-27 72306575.0 \n",
"2022-04-28 73841190.0 \n",
"2022-04-29 75083455.0 \n",
"\n",
"[1260 rows x 11 columns]"
]
@@ -515,69 +525,69 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2022-03-21</th>\n",
" <th>2022-04-25</th>\n",
" <td>0.0</td>\n",
" <td>334.295990</td>\n",
" <td>336.622971</td>\n",
" <td>350.758813</td>\n",
" <td>367.121709</td>\n",
" <td>81985850.00</td>\n",
" <td>340.435699</td>\n",
" <td>340.087312</td>\n",
" <td>350.327677</td>\n",
" <td>-0.002780</td>\n",
" <td>338.041000</td>\n",
" <td>349.745001</td>\n",
" <td>344.924885</td>\n",
" <td>367.586838</td>\n",
" <td>67700730.0</td>\n",
" <td>336.456901</td>\n",
" <td>343.546748</td>\n",
" <td>348.865113</td>\n",
" <td>0.012846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-03-22</th>\n",
" <th>2022-04-26</th>\n",
" <td>0.0</td>\n",
" <td>337.696915</td>\n",
" <td>337.587843</td>\n",
" <td>350.310193</td>\n",
" <td>367.231533</td>\n",
" <td>80854790.00</td>\n",
" <td>344.107764</td>\n",
" <td>341.621192</td>\n",
" <td>350.587768</td>\n",
" <td>0.019653</td>\n",
" <td>335.666000</td>\n",
" <td>347.356502</td>\n",
" <td>344.335056</td>\n",
" <td>367.366986</td>\n",
" <td>70150015.0</td>\n",
" <td>332.164260</td>\n",
" <td>341.146136</td>\n",
" <td>347.620992</td>\n",
" <td>-0.037745</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-03-23</th>\n",
" <th>2022-04-27</th>\n",
" <td>0.0</td>\n",
" <td>339.422278</td>\n",
" <td>338.728680</td>\n",
" <td>349.753981</td>\n",
" <td>367.314911</td>\n",
" <td>80074795.00</td>\n",
" <td>345.823813</td>\n",
" <td>342.549265</td>\n",
" <td>350.636483</td>\n",
" <td>-0.014371</td>\n",
" <td>333.397000</td>\n",
" <td>344.635002</td>\n",
" <td>343.729037</td>\n",
" <td>367.145234</td>\n",
" <td>72306575.0</td>\n",
" <td>328.741093</td>\n",
" <td>338.929215</td>\n",
" <td>346.410757</td>\n",
" <td>-0.001198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-03-24</th>\n",
" <th>2022-04-28</th>\n",
" <td>0.0</td>\n",
" <td>342.301181</td>\n",
" <td>339.707703</td>\n",
" <td>349.240109</td>\n",
" <td>367.436991</td>\n",
" <td>76213275.00</td>\n",
" <td>348.896298</td>\n",
" <td>344.103876</td>\n",
" <td>350.989954</td>\n",
" <td>0.022227</td>\n",
" <td>331.563000</td>\n",
" <td>342.681003</td>\n",
" <td>343.175432</td>\n",
" <td>366.976494</td>\n",
" <td>73841190.0</td>\n",
" <td>328.578630</td>\n",
" <td>337.936560</td>\n",
" <td>345.689159</td>\n",
" <td>0.035516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-03-25</th>\n",
" <th>2022-04-29</th>\n",
" <td>0.0</td>\n",
" <td>345.465820</td>\n",
" <td>340.222841</td>\n",
" <td>348.615591</td>\n",
" <td>367.521584</td>\n",
" <td>73514354.55</td>\n",
" <td>350.396190</td>\n",
" <td>345.153143</td>\n",
" <td>351.172536</td>\n",
" <td>-0.011134</td>\n",
" <td>329.045001</td>\n",
" <td>340.216502</td>\n",
" <td>342.328425</td>\n",
" <td>366.746658</td>\n",
" <td>75083455.0</td>\n",
" <td>325.172268</td>\n",
" <td>335.692328</td>\n",
" <td>344.417035</td>\n",
" <td>-0.044999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -586,19 +596,19 @@
"text/plain": [
" stock splits sma_10 sma_20 sma_50 sma_200 \\\n",
"Date \n",
"2022-03-21 0.0 334.295990 336.622971 350.758813 367.121709 \n",
"2022-03-22 0.0 337.696915 337.587843 350.310193 367.231533 \n",
"2022-03-23 0.0 339.422278 338.728680 349.753981 367.314911 \n",
"2022-03-24 0.0 342.301181 339.707703 349.240109 367.436991 \n",
"2022-03-25 0.0 345.465820 340.222841 348.615591 367.521584 \n",
"2022-04-25 0.0 338.041000 349.745001 344.924885 367.586838 \n",
"2022-04-26 0.0 335.666000 347.356502 344.335056 367.366986 \n",
"2022-04-27 0.0 333.397000 344.635002 343.729037 367.145234 \n",
"2022-04-28 0.0 331.563000 342.681003 343.175432 366.976494 \n",
"2022-04-29 0.0 329.045001 340.216502 342.328425 366.746658 \n",
"\n",
" vol_sma_20 EMA_8 EMA_21 EMA_50 PCTRET_1 \n",
"Date \n",
"2022-03-21 81985850.00 340.435699 340.087312 350.327677 -0.002780 \n",
"2022-03-22 80854790.00 344.107764 341.621192 350.587768 0.019653 \n",
"2022-03-23 80074795.00 345.823813 342.549265 350.636483 -0.014371 \n",
"2022-03-24 76213275.00 348.896298 344.103876 350.989954 0.022227 \n",
"2022-03-25 73514354.55 350.396190 345.153143 351.172536 -0.011134 "
" vol_sma_20 EMA_8 EMA_21 EMA_50 PCTRET_1 \n",
"Date \n",
"2022-04-25 67700730.0 336.456901 343.546748 348.865113 0.012846 \n",
"2022-04-26 70150015.0 332.164260 341.146136 347.620992 -0.037745 \n",
"2022-04-27 72306575.0 328.741093 338.929215 346.410757 -0.001198 \n",
"2022-04-28 73841190.0 328.578630 337.936560 345.689159 0.035516 \n",
"2022-04-29 75083455.0 325.172268 335.692328 344.417035 -0.044999 "
]
},
"execution_count": 7,
@@ -638,6 +648,13 @@
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tsignals\n"
]
},
{
"data": {
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@@ -674,35 +691,35 @@
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" <tr>\n",
" <th>2022-03-22</th>\n",
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" <th>2022-04-26</th>\n",
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" <td>0</td>\n",
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" <th>2022-03-24</th>\n",
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@@ -715,11 +732,11 @@
"text/plain": [
" TS_Trends TS_Trades TS_Entries TS_Exits\n",
"Date \n",
"2022-03-21 0 0 0 0\n",
"2022-03-22 0 0 0 0\n",
"2022-03-23 0 0 0 0\n",
"2022-03-24 0 0 0 0\n",
"2022-03-25 0 0 0 0"
"2022-04-25 1 0 0 0\n",
"2022-04-26 1 0 0 0\n",
"2022-04-27 1 0 0 0\n",
"2022-04-28 0 -1 0 1\n",
"2022-04-29 0 0 0 0"
]
},
"execution_count": 8,
@@ -751,7 +768,7 @@
"text": [
"\n",
"Trades Total | Round Trip:\t22 | 11\n",
"Trade Coverage: 71.67%\n"
"Trade Coverage: 71.19%\n"
]
},
{
@@ -788,28 +805,16 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-04-27</th>\n",
" <td>1</td>\n",
" <td>213.502670</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-01</th>\n",
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" <td>280.042267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-10-16</th>\n",
" <td>1</td>\n",
" <td>286.253387</td>\n",
" <td>286.253357</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2021-03-11</th>\n",
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" <td>NaN</td>\n",
" <td>316.124329</td>\n",
" <td>316.124359</td>\n",
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" <tr>\n",
" <th>2021-04-13</th>\n",
@@ -826,14 +831,14 @@
" <tr>\n",
" <th>2021-06-16</th>\n",
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" <td>356.483429</td>\n",
" <td>356.483398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-11-02</th>\n",
@@ -847,6 +852,18 @@
" <td>NaN</td>\n",
" <td>379.390961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-04-05</th>\n",
" <td>1</td>\n",
" <td>361.100006</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-04-28</th>\n",
" <td>-1</td>\n",
" <td>NaN</td>\n",
" <td>328.010010</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
@@ -854,16 +871,16 @@
"text/plain": [
" Signal Entry Exit\n",
"Date \n",
"2020-04-27 1 213.502670 NaN\n",
"2020-10-01 -1 NaN 280.042267\n",
"2020-10-16 1 286.253387 NaN\n",
"2021-03-11 -1 NaN 316.124329\n",
"2020-10-16 1 286.253357 NaN\n",
"2021-03-11 -1 NaN 316.124359\n",
"2021-04-13 1 338.976044 NaN\n",
"2021-05-26 -1 NaN 332.536896\n",
"2021-06-16 1 339.384094 NaN\n",
"2021-10-05 -1 NaN 356.483429\n",
"2021-06-16 1 339.384125 NaN\n",
"2021-10-05 -1 NaN 356.483398\n",
"2021-11-02 1 388.073944 NaN\n",
"2022-01-07 -1 NaN 379.390961"
"2022-01-07 -1 NaN 379.390961\n",
"2022-04-05 1 361.100006 NaN\n",
"2022-04-28 -1 NaN 328.010010"
]
},
"execution_count": 9,
@@ -961,7 +978,7 @@
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'\\nQQQ [D for 5y(1260 bars)]\\nLast OHLCV: (359.5900, 360.6600, 354.9438, 355.6458, 24863691), Change (%): 3.9442 (1.0969 %)\\nSunday March 27, 2022, NYSE: 12:50:00, Local: 16:50:00 PDT, Day 86/365 (24.00%)'}, xlabel='Date'>"
"<AxesSubplot:title={'center':'\\nQQQ [D for 5y(1260 bars)]\\nLast OHLCV: (323.7000, 327.2300, 312.6000, 313.2500, 91856700), Change (%): 10.4500 (3.2283 %)\\nSunday May 1, 2022, NYSE: 14:15:43, Local: 18:15:43 PDT, Day 121/365 (33.00%)'}, xlabel='Date'>"
]
},
"execution_count": 11,
@@ -970,7 +987,7 @@
},
{
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truncated
"text/plain": [
"<Figure size 1152x720 with 1 Axes>"
]
@@ -1013,7 +1030,7 @@
},
{
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truncated
"text/plain": [
"<Figure size 1152x61.2 with 1 Axes>"
]
@@ -1057,7 +1074,7 @@
},
{
"data": {
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truncated
"text/plain": [
"<Figure size 1152x108 with 1 Axes>"
]
@@ -1088,7 +1105,7 @@
{
"data": {
"text/plain": [
"<matplotlib.lines.Line2D at 0x13037ff10>"
"<matplotlib.lines.Line2D at 0x132855d00>"
]
},
"execution_count": 14,
@@ -1097,7 +1114,7 @@
},
{
"data": {
"image/png": 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truncated
"image/png": 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truncated
"text/plain": [
"<Figure size 1152x216 with 1 Axes>"
]
@@ -1128,7 +1145,7 @@
{
"data": {
"text/plain": [
"<matplotlib.lines.Line2D at 0x130402430>"
"<matplotlib.lines.Line2D at 0x13206da60>"
]
},
"execution_count": 15,
@@ -1137,7 +1154,7 @@
},
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<Figure size 1152x216 with 1 Axes>"
]
@@ -1167,7 +1184,7 @@
{
"data": {
"text/plain": [
"<matplotlib.lines.Line2D at 0x130470400>"
"<matplotlib.lines.Line2D at 0x133835ee0>"
]
},
"execution_count": 16,
@@ -1176,7 +1193,7 @@
},
{
"data": {
"image/png": 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truncated
"text/plain": [
"<Figure size 1152x216 with 1 Axes>"
]
examples/VectorBT_Backtest_with_Pandas_TA.ipynb
+309 -295
View File
@@ -34,7 +34,7 @@
"Pandas v1.3.0\n",
"vectorbt v0.23.1\n",
"\n",
"Pandas TA v0.3.54b0\n",
"Pandas TA v0.3.63b0\n",
"To install the Latest Version:\n",
"$ pip install -U git+https://github.com/twopirllc/pandas-ta\n",
"\n"
@@ -557,10 +557,10 @@
"output_type": "stream",
"text": [
"[i] Downloading: SPY, QQQ\n",
"[+] yf | SPY(7343, 7): 3510.6075 ms (3.5106 s)\n",
"[+] Friday March 25, 2022, NYSE: 4:33:29\n",
"[+] yf | QQQ(5801, 7): 2978.0290 ms (2.9780 s)\n",
"[+] Friday March 25, 2022, NYSE: 4:33:32\n",
"[+] yf | SPY(7367, 7): 3810.4675 ms (3.8105 s)\n",
"[+] Sunday May 1, 2022, NYSE: 14:17:10\n",
"[+] yf | QQQ(5825, 7): 3371.7531 ms (3.3718 s)\n",
"[+] Sunday May 1, 2022, NYSE: 14:17:14\n",
"[*] Download Complete\n",
"\n"
]
@@ -581,12 +581,12 @@
"output_type": "stream",
"text": [
"[i] Downloading: AAPL, TSLA, TWTR\n",
"[+] yf | AAPL(10410, 7): 3208.4925 ms (3.2085 s)\n",
"[+] Friday March 25, 2022, NYSE: 4:33:36\n",
"[+] yf | TSLA(2957, 7): 2836.7323 ms (2.8367 s)\n",
"[+] Friday March 25, 2022, NYSE: 4:33:38\n",
"[+] yf | TWTR(2110, 7): 2735.9980 ms (2.7360 s)\n",
"[+] Friday March 25, 2022, NYSE: 4:33:41\n",
"[+] yf | AAPL(10434, 7): 3390.6886 ms (3.3907 s)\n",
"[+] Sunday May 1, 2022, NYSE: 14:17:17\n",
"[+] yf | TSLA(2981, 7): 3219.1295 ms (3.2191 s)\n",
"[+] Sunday May 1, 2022, NYSE: 14:17:20\n",
"[+] yf | TWTR(2134, 7): 3016.2403 ms (3.0162 s)\n",
"[+] Sunday May 1, 2022, NYSE: 14:17:23\n",
"[*] Download Complete\n",
"\n"
]
@@ -738,50 +738,50 @@
" <tbody>\n",
" <tr>\n",
" <th>2005-01-03</th>\n",
" <td>86.905246</td>\n",
" <td>87.048233</td>\n",
" <td>85.718488</td>\n",
" <td>86.004456</td>\n",
" <td>86.905223</td>\n",
" <td>87.048210</td>\n",
" <td>85.718465</td>\n",
" <td>86.004433</td>\n",
" <td>55748000</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-04</th>\n",
" <td>86.118826</td>\n",
" <td>86.176020</td>\n",
" <td>84.674697</td>\n",
" <td>84.953514</td>\n",
" <td>86.118795</td>\n",
" <td>86.175989</td>\n",
" <td>84.674667</td>\n",
" <td>84.953484</td>\n",
" <td>69167600</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-05</th>\n",
" <td>84.889165</td>\n",
" <td>85.253774</td>\n",
" <td>84.360128</td>\n",
" <td>84.367279</td>\n",
" <td>84.889173</td>\n",
" <td>85.253781</td>\n",
" <td>84.360136</td>\n",
" <td>84.367287</td>\n",
" <td>65667300</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-06</th>\n",
" <td>84.674684</td>\n",
" <td>85.182274</td>\n",
" <td>84.545999</td>\n",
" <td>84.796219</td>\n",
" <td>84.674677</td>\n",
" <td>85.182267</td>\n",
" <td>84.545992</td>\n",
" <td>84.796211</td>\n",
" <td>47814700</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-07</th>\n",
" <td>85.053626</td>\n",
" <td>85.239506</td>\n",
" <td>84.453093</td>\n",
" <td>84.674721</td>\n",
" <td>85.053580</td>\n",
" <td>85.239459</td>\n",
" <td>84.453047</td>\n",
" <td>84.674675</td>\n",
" <td>55847700</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
@@ -798,50 +798,50 @@
" </tr>\n",
" <tr>\n",
" <th>2009-12-24</th>\n",
" <td>88.709423</td>\n",
" <td>89.033609</td>\n",
" <td>88.559186</td>\n",
" <td>88.938728</td>\n",
" <td>88.709400</td>\n",
" <td>89.033586</td>\n",
" <td>88.559164</td>\n",
" <td>88.938705</td>\n",
" <td>39677500</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-12-28</th>\n",
" <td>89.270837</td>\n",
" <td>89.341998</td>\n",
" <td>88.812225</td>\n",
" <td>89.128510</td>\n",
" <td>89.270845</td>\n",
" <td>89.342005</td>\n",
" <td>88.812233</td>\n",
" <td>89.128517</td>\n",
" <td>87508500</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-12-29</th>\n",
" <td>89.357798</td>\n",
" <td>89.373609</td>\n",
" <td>88.994073</td>\n",
" <td>89.001976</td>\n",
" <td>89.357828</td>\n",
" <td>89.373640</td>\n",
" <td>88.994104</td>\n",
" <td>89.002007</td>\n",
" <td>80572500</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-12-30</th>\n",
" <td>88.741037</td>\n",
" <td>89.073133</td>\n",
" <td>88.693591</td>\n",
" <td>88.970337</td>\n",
" <td>88.741052</td>\n",
" <td>89.073148</td>\n",
" <td>88.693606</td>\n",
" <td>88.970352</td>\n",
" <td>73138400</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-12-31</th>\n",
" <td>89.168030</td>\n",
" <td>89.191756</td>\n",
" <td>88.076856</td>\n",
" <td>88.116394</td>\n",
" <td>89.168022</td>\n",
" <td>89.191748</td>\n",
" <td>88.076849</td>\n",
" <td>88.116386</td>\n",
" <td>90637900</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
@@ -854,17 +854,17 @@
"text/plain": [
" Open High Low Close Volume Dividends \\\n",
"Date \n",
"2005-01-03 86.905246 87.048233 85.718488 86.004456 55748000 0.0 \n",
"2005-01-04 86.118826 86.176020 84.674697 84.953514 69167600 0.0 \n",
"2005-01-05 84.889165 85.253774 84.360128 84.367279 65667300 0.0 \n",
"2005-01-06 84.674684 85.182274 84.545999 84.796219 47814700 0.0 \n",
"2005-01-07 85.053626 85.239506 84.453093 84.674721 55847700 0.0 \n",
"2005-01-03 86.905223 87.048210 85.718465 86.004433 55748000 0.0 \n",
"2005-01-04 86.118795 86.175989 84.674667 84.953484 69167600 0.0 \n",
"2005-01-05 84.889173 85.253781 84.360136 84.367287 65667300 0.0 \n",
"2005-01-06 84.674677 85.182267 84.545992 84.796211 47814700 0.0 \n",
"2005-01-07 85.053580 85.239459 84.453047 84.674675 55847700 0.0 \n",
"... ... ... ... ... ... ... \n",
"2009-12-24 88.709423 89.033609 88.559186 88.938728 39677500 0.0 \n",
"2009-12-28 89.270837 89.341998 88.812225 89.128510 87508500 0.0 \n",
"2009-12-29 89.357798 89.373609 88.994073 89.001976 80572500 0.0 \n",
"2009-12-30 88.741037 89.073133 88.693591 88.970337 73138400 0.0 \n",
"2009-12-31 89.168030 89.191756 88.076856 88.116394 90637900 0.0 \n",
"2009-12-24 88.709400 89.033586 88.559164 88.938705 39677500 0.0 \n",
"2009-12-28 89.270845 89.342005 88.812233 89.128517 87508500 0.0 \n",
"2009-12-29 89.357828 89.373640 88.994104 89.002007 80572500 0.0 \n",
"2009-12-30 88.741052 89.073148 88.693606 88.970352 73138400 0.0 \n",
"2009-12-31 89.168022 89.191748 88.076849 88.116386 90637900 0.0 \n",
"\n",
" Stock Splits \n",
"Date \n",
@@ -943,8 +943,8 @@
" <th>2005-01-03</th>\n",
" <td>0.990526</td>\n",
" <td>0.995572</td>\n",
" <td>0.957193</td>\n",
" <td>0.967744</td>\n",
" <td>0.957192</td>\n",
" <td>0.967743</td>\n",
" <td>691992000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
@@ -963,8 +963,8 @@
" <th>2005-01-05</th>\n",
" <td>0.985632</td>\n",
" <td>0.997713</td>\n",
" <td>0.979364</td>\n",
" <td>0.986245</td>\n",
" <td>0.979363</td>\n",
" <td>0.986244</td>\n",
" <td>680433600</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
@@ -981,10 +981,10 @@
" </tr>\n",
" <tr>\n",
" <th>2005-01-07</th>\n",
" <td>0.993890</td>\n",
" <td>1.064686</td>\n",
" <td>0.993889</td>\n",
" <td>1.064685</td>\n",
" <td>0.990067</td>\n",
" <td>1.058875</td>\n",
" <td>1.058874</td>\n",
" <td>2227450400</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
@@ -1002,9 +1002,9 @@
" <tr>\n",
" <th>2009-12-24</th>\n",
" <td>6.224809</td>\n",
" <td>6.402181</td>\n",
" <td>6.218693</td>\n",
" <td>6.392700</td>\n",
" <td>6.402180</td>\n",
" <td>6.218692</td>\n",
" <td>6.392699</td>\n",
" <td>500889200</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
@@ -1021,29 +1021,29 @@
" </tr>\n",
" <tr>\n",
" <th>2009-12-29</th>\n",
" <td>6.502488</td>\n",
" <td>6.505240</td>\n",
" <td>6.383221</td>\n",
" <td>6.394536</td>\n",
" <td>6.502485</td>\n",
" <td>6.505237</td>\n",
" <td>6.383218</td>\n",
" <td>6.394533</td>\n",
" <td>445205600</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-12-30</th>\n",
" <td>6.386277</td>\n",
" <td>6.483220</td>\n",
" <td>6.370375</td>\n",
" <td>6.472210</td>\n",
" <td>6.386275</td>\n",
" <td>6.483218</td>\n",
" <td>6.370373</td>\n",
" <td>6.472208</td>\n",
" <td>412084400</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2009-12-31</th>\n",
" <td>6.517778</td>\n",
" <td>6.524506</td>\n",
" <td>6.439184</td>\n",
" <td>6.517777</td>\n",
" <td>6.524505</td>\n",
" <td>6.439183</td>\n",
" <td>6.444382</td>\n",
" <td>352410800</td>\n",
" <td>0.0</td>\n",
@@ -1057,17 +1057,17 @@
"text/plain": [
" Open High Low Close Volume Dividends \\\n",
"Date \n",
"2005-01-03 0.990526 0.995572 0.957193 0.967744 691992000 0.0 \n",
"2005-01-03 0.990526 0.995572 0.957192 0.967743 691992000 0.0 \n",
"2005-01-04 0.975388 1.001076 0.962850 0.977682 1096810400 0.0 \n",
"2005-01-05 0.985632 0.997713 0.979364 0.986245 680433600 0.0 \n",
"2005-01-05 0.985632 0.997713 0.979363 0.986244 680433600 0.0 \n",
"2005-01-06 0.988843 0.992513 0.968354 0.987009 705555200 0.0 \n",
"2005-01-07 0.993890 1.064686 0.990067 1.058875 2227450400 0.0 \n",
"2005-01-07 0.993889 1.064685 0.990067 1.058874 2227450400 0.0 \n",
"... ... ... ... ... ... ... \n",
"2009-12-24 6.224809 6.402181 6.218693 6.392700 500889200 0.0 \n",
"2009-12-24 6.224809 6.402180 6.218692 6.392699 500889200 0.0 \n",
"2009-12-28 6.474657 6.542852 6.410130 6.471292 644565600 0.0 \n",
"2009-12-29 6.502488 6.505240 6.383221 6.394536 445205600 0.0 \n",
"2009-12-30 6.386277 6.483220 6.370375 6.472210 412084400 0.0 \n",
"2009-12-31 6.517778 6.524506 6.439184 6.444382 352410800 0.0 \n",
"2009-12-29 6.502485 6.505237 6.383218 6.394533 445205600 0.0 \n",
"2009-12-30 6.386275 6.483218 6.370373 6.472208 412084400 0.0 \n",
"2009-12-31 6.517777 6.524505 6.439183 6.444382 352410800 0.0 \n",
"\n",
" Stock Splits \n",
"Date \n",
@@ -1157,7 +1157,7 @@
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@@ -1467,10 +1481,10 @@
"\n",
"Last 1 of 1 Trades\n",
" status direction size entry_price exit_price return \\\n",
"0 0 0 1156.938534 86.219467 88.116394 0.019501 \n",
"0 0 0 1156.938842 86.219444 88.116386 0.019501 \n",
"\n",
" pnl entry_fees exit_fees \n",
"0 1945.251775 249.376559 0.0 \n",
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@@ -1478,42 +1492,42 @@
{
"data": {
"text/plain": [
"Run Time Friday March 25, 2022, NYSE: 4:33:45\n",
"Mode TEST\n",
"Strategy Buy and Hold\n",
"Direction both\n",
"Symbol SPY\n",
"Fees [%] 0.25\n",
"Slippage [%] 0.25\n",
"Accumulate False\n",
"Start 2005-01-03 00:00:00\n",
"End 2009-12-31 00:00:00\n",
"Period 1259 days 00:00:00\n",
"Start Value 100000.0\n",
"End Value 101945.251775\n",
"Total Return [%] 1.945252\n",
"Benchmark Return [%] 2.455615\n",
"Max Gross Exposure [%] 100.0\n",
"Total Fees Paid 249.376559\n",
"Max Drawdown [%] 55.189449\n",
"Max Drawdown Duration 562 days 00:00:00\n",
"Total Trades 1\n",
"Total Closed Trades 0\n",
"Total Open Trades 1\n",
"Open Trade PnL 1945.251775\n",
"Sharpe Ratio 0.163325\n",
"Calmar Ratio 0.010149\n",
"Omega Ratio 1.028442\n",
"Sortino Ratio 0.231222\n",
"Annualized Return [%] 0.560101\n",
"Annualized Volatility [%] 28.894507\n",
"Skew 0.426795\n",
"Kurtosis 14.955978\n",
"Tail Ratio 0.881912\n",
"Common Sense Ratio 0.886852\n",
"Value at Risk -0.02229\n",
"Alpha -0.001443\n",
"Beta 1.000002\n",
"Run Time Sunday May 1, 2022, NYSE: 14:17:27\n",
"Mode TEST\n",
"Strategy Buy and Hold\n",
"Direction both\n",
"Symbol SPY\n",
"Fees [%] 0.25\n",
"Slippage [%] 0.25\n",
"Accumulate False\n",
"Start 2005-01-03 00:00:00\n",
"End 2009-12-31 00:00:00\n",
"Period 1259 days 00:00:00\n",
"Start Value 100000.0\n",
"End Value 101945.270079\n",
"Total Return [%] 1.94527\n",
"Benchmark Return [%] 2.455634\n",
"Max Gross Exposure [%] 100.0\n",
"Total Fees Paid 249.376559\n",
"Max Drawdown [%] 55.189427\n",
"Max Drawdown Duration 562 days 00:00:00\n",
"Total Trades 1\n",
"Total Closed Trades 0\n",
"Total Open Trades 1\n",
"Open Trade PnL 1945.270079\n",
"Sharpe Ratio 0.163325\n",
"Calmar Ratio 0.010149\n",
"Omega Ratio 1.028442\n",
"Sortino Ratio 0.231223\n",
"Annualized Return [%] 0.560106\n",
"Annualized Volatility [%] 28.894497\n",
"Skew 0.426797\n",
"Kurtosis 14.955984\n",
"Tail Ratio 0.881903\n",
"Common Sense Ratio 0.886842\n",
"Value at Risk -0.02229\n",
"Alpha -0.001443\n",
"Beta 1.000002\n",
"dtype: object"
]
},
@@ -1542,10 +1556,10 @@
"\n",
"Last 1 of 1 Trades\n",
" status direction size entry_price exit_price return \\\n",
"0 0 0 102818.423476 0.970163 6.444382 5.640077 \n",
"0 0 0 102818.493136 0.970162 6.444382 5.640081 \n",
"\n",
" pnl entry_fees exit_fees \n",
"0 562601.217125 249.376559 0.0 \n",
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"\n",
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]
@@ -1553,42 +1567,42 @@
{
"data": {
"text/plain": [
"Run Time Friday March 25, 2022, NYSE: 4:33:46\n",
"Mode TEST\n",
"Strategy Buy and Hold\n",
"Direction both\n",
"Symbol AAPL\n",
"Fees [%] 0.25\n",
"Slippage [%] 0.25\n",
"Accumulate False\n",
"Start 2005-01-03 00:00:00\n",
"End 2009-12-31 00:00:00\n",
"Period 1259 days 00:00:00\n",
"Start Value 100000.0\n",
"End Value 662601.217125\n",
"Total Return [%] 562.601217\n",
"Benchmark Return [%] 565.918364\n",
"Max Gross Exposure [%] 100.0\n",
"Total Fees Paid 249.376559\n",
"Max Drawdown [%] 60.86673\n",
"Max Drawdown Duration 456 days 00:00:00\n",
"Total Trades 1\n",
"Total Closed Trades 0\n",
"Total Open Trades 1\n",
"Open Trade PnL 562601.217125\n",
"Sharpe Ratio 1.329387\n",
"Calmar Ratio 1.199638\n",
"Omega Ratio 1.209774\n",
"Sortino Ratio 1.982726\n",
"Annualized Return [%] 73.018042\n",
"Annualized Volatility [%] 51.088429\n",
"Skew -0.037597\n",
"Kurtosis 3.435264\n",
"Tail Ratio 1.037885\n",
"Common Sense Ratio 1.795729\n",
"Value at Risk -0.041247\n",
"Alpha -0.00145\n",
"Beta 1.00001\n",
"Run Time Sunday May 1, 2022, NYSE: 14:17:28\n",
"Mode TEST\n",
"Strategy Buy and Hold\n",
"Direction both\n",
"Symbol AAPL\n",
"Fees [%] 0.25\n",
"Slippage [%] 0.25\n",
"Accumulate False\n",
"Start 2005-01-03 00:00:00\n",
"End 2009-12-31 00:00:00\n",
"Period 1259 days 00:00:00\n",
"Start Value 100000.0\n",
"End Value 662601.617014\n",
"Total Return [%] 562.601617\n",
"Benchmark Return [%] 565.918766\n",
"Max Gross Exposure [%] 100.0\n",
"Total Fees Paid 249.376559\n",
"Max Drawdown [%] 60.866735\n",
"Max Drawdown Duration 456 days 00:00:00\n",
"Total Trades 1\n",
"Total Closed Trades 0\n",
"Total Open Trades 1\n",
"Open Trade PnL 562601.617014\n",
"Sharpe Ratio 1.329387\n",
"Calmar Ratio 1.199638\n",
"Omega Ratio 1.209774\n",
"Sortino Ratio 1.982725\n",
"Annualized Return [%] 73.018072\n",
"Annualized Volatility [%] 51.088458\n",
"Skew -0.037599\n",
"Kurtosis 3.43526\n",
"Tail Ratio 1.03789\n",
"Common Sense Ratio 1.795738\n",
"Value at Risk -0.041247\n",
"Alpha -0.00145\n",
"Beta 1.00001\n",
"dtype: object"
]
},
@@ -1625,69 +1639,69 @@
"\n",
"Last 5 of 15 Trades\n",
" status direction size entry_price exit_price return \\\n",
"10 1 0 797.772784 114.865412 108.679654 -0.058718 \n",
"11 1 1 793.793867 108.679654 105.284153 0.026321 \n",
"12 1 0 840.908131 105.284153 101.710419 -0.038859 \n",
"13 1 1 836.714076 101.710419 63.710985 0.369538 \n",
"14 0 0 1828.142319 63.710985 88.116394 0.380564 \n",
"10 1 0 797.770311 114.865419 108.679631 -0.058718 \n",
"11 1 1 793.791407 108.679631 105.284168 0.026321 \n",
"12 1 0 840.904944 105.284168 101.710388 -0.038859 \n",
"13 1 1 836.710904 101.710388 63.711012 0.369538 \n",
"14 0 0 1828.133472 63.711012 88.116386 0.380564 \n",
"\n",
" pnl entry_fees exit_fees \n",
"10 -5380.674590 229.091248 216.754175 \n",
"11 2270.720055 215.673107 208.934787 \n",
"12 -3440.340619 221.335750 213.822795 \n",
"13 31448.634826 212.756347 133.269695 \n",
"14 44325.378834 291.181871 0.000000 \n",
"10 -5380.682196 229.090553 216.753458 \n",
"11 2270.682765 215.672393 208.934170 \n",
"12 -3440.366007 221.334944 213.821921 \n",
"13 31448.467750 212.755477 133.269246 \n",
"14 44325.101312 291.180584 0.000000 \n",
"\n"
]
},
{
"data": {
"text/plain": [
"Run Time Friday March 25, 2022, NYSE: 4:33:49\n",
"Mode TEST\n",
"Strategy Long Strategy\n",
"Direction both\n",
"Symbol SPY\n",
"Fees [%] 0.25\n",
"Slippage [%] 0.25\n",
"Accumulate False\n",
"Start 2005-01-03 00:00:00\n",
"End 2009-12-31 00:00:00\n",
"Period 1259 days 00:00:00\n",
"Start Value 100000.0\n",
"End Value 161089.308979\n",
"Total Return [%] 61.089309\n",
"Benchmark Return [%] 2.455615\n",
"Max Gross Exposure [%] 100.0\n",
"Total Fees Paid 6786.609621\n",
"Max Drawdown [%] 21.642405\n",
"Max Drawdown Duration 411 days 00:00:00\n",
"Total Trades 15\n",
"Total Closed Trades 14\n",
"Total Open Trades 1\n",
"Open Trade PnL 44325.378834\n",
"Win Rate [%] 35.714286\n",
"Best Trade [%] 36.953815\n",
"Worst Trade [%] -5.871759\n",
"Avg Winning Trade [%] 10.694648\n",
"Avg Losing Trade [%] -3.476746\n",
"Avg Winning Trade Duration 136 days 14:24:00\n",
"Avg Losing Trade Duration 37 days 18:40:00\n",
"Profit Factor 1.564796\n",
"Expectancy 1197.423582\n",
"Sharpe Ratio 0.762857\n",
"Calmar Ratio 0.684934\n",
"Omega Ratio 1.124146\n",
"Sortino Ratio 1.111308\n",
"Annualized Return [%] 14.823626\n",
"Annualized Volatility [%] 21.015085\n",
"Skew 0.013953\n",
"Kurtosis 4.710095\n",
"Tail Ratio 1.023207\n",
"Common Sense Ratio 1.174883\n",
"Value at Risk -0.017307\n",
"Alpha 0.191161\n",
"Beta -0.301323\n",
"Run Time Sunday May 1, 2022, NYSE: 14:17:31\n",
"Mode TEST\n",
"Strategy Long Strategy\n",
"Direction both\n",
"Symbol SPY\n",
"Fees [%] 0.25\n",
"Slippage [%] 0.25\n",
"Accumulate False\n",
"Start 2005-01-03 00:00:00\n",
"End 2009-12-31 00:00:00\n",
"Period 1259 days 00:00:00\n",
"Start Value 100000.0\n",
"End Value 161088.515438\n",
"Total Return [%] 61.088515\n",
"Benchmark Return [%] 2.455634\n",
"Max Gross Exposure [%] 100.0\n",
"Total Fees Paid 6786.593906\n",
"Max Drawdown [%] 21.642411\n",
"Max Drawdown Duration 411 days 00:00:00\n",
"Total Trades 15\n",
"Total Closed Trades 14\n",
"Total Open Trades 1\n",
"Open Trade PnL 44325.101312\n",
"Win Rate [%] 35.714286\n",
"Best Trade [%] 36.95377\n",
"Worst Trade [%] -5.871785\n",
"Avg Winning Trade [%] 10.694623\n",
"Avg Losing Trade [%] -3.476782\n",
"Avg Winning Trade Duration 136 days 14:24:00\n",
"Avg Losing Trade Duration 37 days 18:40:00\n",
"Profit Factor 1.564774\n",
"Expectancy 1197.386723\n",
"Sharpe Ratio 0.76285\n",
"Calmar Ratio 0.684927\n",
"Omega Ratio 1.124145\n",
"Sortino Ratio 1.111297\n",
"Annualized Return [%] 14.823462\n",
"Annualized Volatility [%] 21.015086\n",
"Skew 0.013949\n",
"Kurtosis 4.710068\n",
"Tail Ratio 1.023206\n",
"Common Sense Ratio 1.174881\n",
"Value at Risk -0.017307\n",
"Alpha 0.191159\n",
"Beta -0.301324\n",
"dtype: object"
]
},
@@ -1720,69 +1734,69 @@
"\n",
"Last 5 of 19 Trades\n",
" status direction size entry_price exit_price return \\\n",
"14 1 0 29355.575610 5.320026 4.917980 -0.080383 \n",
"15 1 1 29209.163761 4.917980 3.043387 0.377124 \n",
"16 1 0 64956.862336 3.043387 2.652390 -0.133153 \n",
"17 1 1 64632.887961 2.652390 3.112368 -0.178854 \n",
"18 0 0 45253.957674 3.112368 6.444382 1.068072 \n",
"14 1 0 29355.566042 5.320024 4.917979 -0.080383 \n",
"15 1 1 29209.154242 4.917979 3.043387 0.377124 \n",
"16 1 0 64956.830347 3.043387 2.652390 -0.133153 \n",
"17 1 1 64632.856130 2.652390 3.112366 -0.178853 \n",
"18 0 0 45253.946608 3.112366 6.444382 1.068073 \n",
"\n",
" pnl entry_fees exit_fees \n",
"14 -12553.649891 390.431047 360.925314 \n",
"15 54173.913870 359.125187 222.236997 \n",
"16 -26322.878669 494.222229 430.727407 \n",
"17 -30661.152430 428.579140 502.903315 \n",
"18 150434.715996 352.117414 0.000000 \n",
"14 -12553.631451 390.430815 360.925126 \n",
"15 54173.875496 359.125001 222.236907 \n",
"16 -26322.911779 494.221947 430.727040 \n",
"17 -30661.105740 428.578776 502.902836 \n",
"18 150434.722694 352.117165 0.000000 \n",
"\n"
]
},
{
"data": {
"text/plain": [
"Run Time Friday March 25, 2022, NYSE: 4:33:50\n",
"Mode TEST\n",
"Strategy Long Strategy\n",
"Direction both\n",
"Symbol AAPL\n",
"Fees [%] 0.25\n",
"Slippage [%] 0.25\n",
"Accumulate False\n",
"Start 2005-01-03 00:00:00\n",
"End 2009-12-31 00:00:00\n",
"Period 1259 days 00:00:00\n",
"Start Value 100000.0\n",
"End Value 291633.798892\n",
"Total Return [%] 191.633799\n",
"Benchmark Return [%] 565.918364\n",
"Max Gross Exposure [%] 100.0\n",
"Total Fees Paid 10631.095277\n",
"Max Drawdown [%] 46.10598\n",
"Max Drawdown Duration 368 days 00:00:00\n",
"Total Trades 19\n",
"Total Closed Trades 18\n",
"Total Open Trades 1\n",
"Open Trade PnL 150434.715996\n",
"Win Rate [%] 33.333333\n",
"Best Trade [%] 78.771745\n",
"Worst Trade [%] -17.88535\n",
"Avg Winning Trade [%] 34.908001\n",
"Avg Losing Trade [%] -10.451626\n",
"Avg Winning Trade Duration 124 days 12:00:00\n",
"Avg Losing Trade Duration 21 days 20:00:00\n",
"Profit Factor 1.281501\n",
"Expectancy 2288.837939\n",
"Sharpe Ratio 0.926133\n",
"Calmar Ratio 0.789131\n",
"Omega Ratio 1.141239\n",
"Sortino Ratio 1.339789\n",
"Annualized Return [%] 36.383667\n",
"Annualized Volatility [%] 43.942033\n",
"Skew -0.036485\n",
"Kurtosis 1.988366\n",
"Tail Ratio 0.968342\n",
"Common Sense Ratio 1.32066\n",
"Value at Risk -0.038211\n",
"Alpha 0.353839\n",
"Beta 0.152645\n",
"Run Time Sunday May 1, 2022, NYSE: 14:17:32\n",
"Mode TEST\n",
"Strategy Long Strategy\n",
"Direction both\n",
"Symbol AAPL\n",
"Fees [%] 0.25\n",
"Slippage [%] 0.25\n",
"Accumulate False\n",
"Start 2005-01-03 00:00:00\n",
"End 2009-12-31 00:00:00\n",
"Period 1259 days 00:00:00\n",
"Start Value 100000.0\n",
"End Value 291633.706004\n",
"Total Return [%] 191.633706\n",
"Benchmark Return [%] 565.918766\n",
"Max Gross Exposure [%] 100.0\n",
"Total Fees Paid 10631.091236\n",
"Max Drawdown [%] 46.106062\n",
"Max Drawdown Duration 368 days 00:00:00\n",
"Total Trades 19\n",
"Total Closed Trades 18\n",
"Total Open Trades 1\n",
"Open Trade PnL 150434.722694\n",
"Win Rate [%] 33.333333\n",
"Best Trade [%] 78.771666\n",
"Worst Trade [%] -17.885338\n",
"Avg Winning Trade [%] 34.907989\n",
"Avg Losing Trade [%] -10.451628\n",
"Avg Winning Trade Duration 124 days 12:00:00\n",
"Avg Losing Trade Duration 21 days 20:00:00\n",
"Profit Factor 1.2815\n",
"Expectancy 2288.832406\n",
"Sharpe Ratio 0.926132\n",
"Calmar Ratio 0.78913\n",
"Omega Ratio 1.141239\n",
"Sortino Ratio 1.339789\n",
"Annualized Return [%] 36.383655\n",
"Annualized Volatility [%] 43.942062\n",
"Skew -0.036486\n",
"Kurtosis 1.988356\n",
"Tail Ratio 0.968328\n",
"Common Sense Ratio 1.320641\n",
"Value at Risk -0.038211\n",
"Alpha 0.353838\n",
"Beta 0.152646\n",
"dtype: object"
]
},
@@ -1852,7 +1866,7 @@
"outputs": [
{
"data": {
"image/png": 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truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -1870,7 +1884,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -1888,7 +1902,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -1924,7 +1938,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAD6CAYAAAAyVW3pAAAgAElEQVR4XuydCbRdRZX+K5CQCTJAQhI0QcQwBBIhDCoEEQEVFKHVVgm23QRsW0TECRz+zdAODCIio6BE7dUGUFEREDUBGaNMARMIkDAmzBkkPDK/JP/1VdiXfevVOafOcOvcvPedtVySd2v8qk6d+tXeVdVrw4YNGwwfKkAFqAAVoAJUgApQASpABagAFaACVKBWBXoR0GvVn5lTASpABagAFaACVIAKUAEqQAWoABWwChDQ2RGoABWgAlSAClABKkAFqAAVoAJUgAq0gQIE9DZoBBaBClABKkAFqAAVoAJUgApQASpABagAAZ19gApQASpABagAFaACVIAKUAEqQAWoQBsoQEBvg0ZgEagAFaACVIAKUAEqQAWoABWgAlSAChDQ2QeoABWgAlSAClABKkAFqAAVoAJUgAq0gQIE9DZoBBaBClABKkAFqAAVoAJUgApQASpABagAAZ19gApQASpABagAFaACVIAKUAEqQAWoQBso0G0BfdWqVeall14yvXv3NltvvbXp379/7XJPnz7dlulTn/pU7WVhAagAFaACVIAKUAEqQAWoABWgAlSgvRTodoD+4IMPmh/84Afm73//e5PSgPQjjzzS/Nd//ZcFdjx77LGHWb58eSPcwIEDza677mqOOeYY86EPfci8+OKL5oADDjD4+2233WYGDx7clOZrr71mDj74YLN69Wpzyy23NNJNauITTjjBzJw506CMeZ8f/vCH5tJLL7XR/vSnP5kdd9wxbxKVhl+3bp3V+a1vfav52Mc+FpT2448/bg477LAu7TJ27Fhz9NFHmw9+8INB6biB7rjjDvO3v/3NfPrTnzYjR44slAYjUQEqQAWoABWgAlSAClABKkAF6lagWwE6rNPvf//7LXTvvffe5t3vfrcBSM6bN8/cfvvt9u+//vWvLZhrQP+3f/s309nZaV544QVz66232t9OPvlk8/nPf95cdNFF5sILLzTHHnus+eY3v9nUXt///vfNFVdcYf+O37OeooC+fv168653vcssXbrUZnHSSSeZL3zhC1nZtfT3NWvWmN122828973vNZdffnlQXvPnzzeHH364GT16tHnPe95j2+bZZ5+1bYMnVEc3M2mj3/72t2b8+PFBZWEgKkAFqAAVoAJUgApQASpABahAuynQrQD9y1/+srn++usNrM2wgOtn0aJF5pxzzrEgDbAUQO/bt6+5++67G0Fnz55tPvrRj9p/P/DAA2bzzTc373vf+6w1/aabbjJve9vb7G9PPPGE+cAHPmD//Yc//MH06dMns22LAvr9999vPvnJT5pPfOIT5pprrrGAe/PNN5tevXpl5lk2wIYNG7z5lAH0j3zkI7Yt5LnrrrvMf/zHf1jrN6zhSU9SWQjoZVuZ8akAFaACVIAKUAEqQAWoABVoBwW6FaDDHR0gDff2bbbZJlNfWNJdQEckWKgB44DhiRMnWpdyWKz3339/8/Of/9ymO2XKFAuTv/zlL82+++6bmRcCFAX0M8880/zf//2fufbaa81Pf/pTWzb894QJE5ryxSLET37yE1suuJO/5S1vseWfPHmyefvb327DPvLII+ayyy6ziw8dHR1m++23N5MmTTLwIhD3cHgToJ5//OMfzZw5c+yCwIEHHmiwALLVVltZTwTogXzg/r/PPvvYtLHPH94GSY9Y0F1AR3hpOyxGDBo0qJHEsmXLzAUXXGDuvPNO8/TTT5udd97ZLqDAnR2LJ7/61a/Mj3/8Y7Nw4ULrGTFkyBAbF3U+6KCDGl4P3/ve95qKBav/fffdZ8uLcuPMAtQJnhdYDIGnBRZrUN/vfve7BvHRt9A3EEes/vAI+MY3vmHDyROicVCHYSAqQAWoABWgAlSAClABKkAFepQC3QrQTzvtNHPVVVeZr371q+b444+3AJf2JAH6KaecYn73u9+ZadOmNeATAAvwB9zi+dznPmeOOOIIc/755wd3mCKADks1FgAAgIDCP//5zxYksUAAMJRn5cqVBuALMB83bpzdow7X/scee8z867/+qwXMJ5980m4BwIPFBoAwQB3ged5559k9+rBS/+d//qd19Qd8H3LIIRbE4V4P9/Hf/OY3ZsWKFRZikTYe5IcHZcRCQtKTBOjIc88997TxtQV9yZIl5qijjrLlwyIB2gseEng+85nPGLTTz372MwvoKB/CCCjjrAHsd4cLPh6cEaAfbGG48cYbjSwI4DwBlAGLFNBcthPgvAJ4WEBbLFbIAy2gJxYrRF/8FqJxcIdhQCpABagAFaACVIAKUAEqQAV6lALdCtAB0ABpPLAeA84Aj3BpF9d03bo+QNf72O+55x4zdOhQGwWwi0PMxMoMaIRVd8SIEcEdpgig43A6LDYg7pe+9CULx7CGAxzhGo5T6vGImzggG7Atz0MPPWTd8fF3WKIvueQSC+uASjzY3/6Xv/zFDB8+3Oy1117WOg8rMSzQ3/rWt8wWW2xh9+dj8QNWZcAwDsarysUdcP6LX/zCWqlhFf/v//7vRtnPOOMM66Fw7rnnWlCHSz+AGP8NazoO3EO501zc8wI6Mj/00EOtyz36zKuvvmr7kgA6ziX47Gc/a63uWECAFijTo48+aheEQjQO7jAMSAWoABWgAlSAClABKkAFqECPUqBbATpaDhAJQBULqLQmXJe//vWvN1y98Xc5LO6ss86y4IsDy2CRBXAJEOveALDF73iKHGhWBNDhDXDdddfZ/4mlWvba/+///q89PA6PLE7AVRx78N0T5xFGQBblOPHEE7375mGZhvUcFuftttuuUX3AMKz2ckBdGUDHIgfc6qEzrOqw+sMV/0c/+lFjAQQLB3Bnh1UcCwh6vz3qgYUGWOvf8Y53VAroOFjwyiuv7DIIANBhHXdP4JftEFgg2XbbbYM07lEjDCtLBagAFaACVIAKUAEqQAWoQLAC3Q7QUXOcDo59wLBqwoKMA9Vg8cajD3pzr1kT1WAthgXZdZHHfmiAPly/77333qCD4XRL5AV0sZYjP5xQLs+MGTMMTpD/+Mc/bi3PeNauXWuhVxYmAJpw2YalXKz8cEmXw/OQJsIA8GGRlnviYXHGfu6kR/IsA+hu2vAGwKKAvqseJ+qjfGmPuOVXaUGHW7xvH30SoMu2CpT/TW96k3X7z9I4+O1kQCpABagAFaACVIAKUAEqQAV6lALdEtDdFgRMfuc737H707/yla/Yu9DxiAUd+8oB46NGjbL/E7dxX09I2rce0mvyArq4myelDciG5bxfv342CNyxUZcbbrihsSCBvwNgceI8nueee84CKPayyx3wAGRogzvNZdFCwN/NG+7e2BNfBtDlkDgsKsAqjzpgQQSH4ckjd6Zjrzf2u/seWM9xyF2dgC5u+ALoIRqH9BWGoQJUgApQASpABagAFaACVKDnKdCtAB17pZPgGvvJjznmGLuPHPuEBdB9p7indYOYgI6FBFj/4Y4OGNcP/o5TyAHkOMjNfXCiOw5Ug/s+ygzXf/1g7zf2psOdGwe/iUs/rnKbNWuW+cc//mEGDBiQKIUAepJLuC+i75C4f/7zn+bDH/6wXVDA/nPsQ8eDU9UB53B9x2n6aY8Aur7jXsLDIwALEfoqPfyWdEhcXgu6D9Al7ySNe94wwxpTASpABagAFaACVIAKUAEqEKJAtwJ0WFSxNxynq2+22WZN9cdBY7iCDPvQjzvuuLYHdIArLNU4qAyWdPfBaeewPgtQwpUflnR9GB4s1LgCTfZ648A5XM0mB98hzblz59oD5HDYGQ6Aw/71Sy+91J7k/rWvfa0pW1jf4eYve+HHjh1rD1CbPn16SF+z+81xLZl7zZocwIdEsGAgru1wz8eeb1wth2ve9INFBFjPcZ0e9qLD+g7PAOihn0996lMWzqGXHPCHgwDRB+CO7p7iXhbQQzQOEouBqAAVoAJUgApQASpABagAFehxCnQrQAcw4hE3bPz7mWeeadyhjb/j+rQtt9yy7QEd1mAsNuCQOJwa7j4avgGruDIMFmjANqAcsP7Xv/7V/h3XsuEwM1iN4YoNKzkgG+AOPQDBOEl9v/32M7huDLCOvezvec977H8jL1jUcVAd9lzLSfk46RyHowGkcVI+AB5XnyU9afegwyNAth7g/nW0HRYd/uVf/sUmB+8HWNRffvllu/8fwC0H5wGy4QYPAAd4r1692uy+++72KrmLL77YHj6HA+dgqV+wYEGTRb5qQA/RuMeNMqwwFaACVIAKUAEqQAWoABWgAkEKdCtA//3vf2/dunFfuPvAkoorsoYNG9b4Ca7f7t3bWarFcnEX+AW4jhkzxlssca+G1RvWZOyzB6zrBzAOcIcrP4AWsKoPgYPrPE6FF9dyxIWFGYfQIbx+3vnOd9o9/LJ3H/vEYbUWCz/Sck851/FlX7m+N1z/fvnll9sT+AHagHS0De4e99ULXhK4Bg4WdDywsuPeeqkb4qDuWHDA4oS+Xx0LDLimDX+DXshH7kHPa0GH5R4WfPQ5nF8QqnFWP+PvVIAKUAEqQAWoABWgAlSAChRXAEY73AylD6Iunlq8mN0K0EU27EVfvHixtQLjADRcf+W6vMeT+I2c8h4SV6SMK1euNDgBHQ+A0dch4aaOPd8Aapzw3qdPH29W0PH555+3HRvQLIfRuYFxOB3SRFq4N70VD/akw0KP+qA9fWcNYM83AB0eEmh3efB3aIIy7rDDDon1qLLcoRpXmSfTogJUgApQASpABagAFaACVMBYAxqMf3hgqBWP5CVLllgP4T/96U+N7a/tple3BPR2E1nKEwPQ27XuLBcVoAJUgApQASpABagAFaACVKDVCsC4iKuksYUXh14DyB9++GFrSDznnHPsldzYStyuDwE9YssQ0COKzayoABWgAlSAClABKkAFqAAV6HEKwKMWNznNnj3bbvPFWVQ4l2vQoEH26um//OULine truncated
},
"metadata": {},
"output_type": "display_data"
@@ -1995,7 +2009,7 @@
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAH0CAYAAACuKActAAAgAElEQVR4XuydCZhdRZn339476SXphEASGoIhYemgk4RRE5fEUQLDkgcQJMo2EtQB3L9xHNeRmVFHxdFxdCSKwkgERYnAREBIdEyPSFAgjGQBEgKBJITs6SXpJen+nrfauqk+fe695956695T5/zP93yPmj71nqrfW/fO/Z3aKgYHBwcJFwiAAAiAAAiAAAiAAAiAAAiAAAiAQFkJVEDQy8ofDwcBEAABEAABEAABEAABEAABEAABRQCCjo4AAiAAAiAAAiAAAiAAAiAAAiAAAjEgAEGPQRJQBRAAARAAARAAARAAARAAARAAARCAoKMPgAAIgAAIgAAIgAAIgAAIgAAIgEAMCEDQY5AEVAEEQAAEQAAEQAAEQAAEQAAEQAAEIOjoAyAAAiAAAiAAAiAAAiAAAiAAAiAQAwIQ9BgkAVUAARAAARAAARAAARAAARAAARAAAQg6+gAIgAAIgAAIgAAIgAAIgAAIgAAIxIAABD0GSUAVQAAEQAAEQAAEQAAEQAAEQAAEQACCjj4AAiAAAiAAAiAAAiAAAiAAAiAAAjEgAEGPQRLCqvBf//VfdOKJJ9Lb3/72mNZwZLUOHTpEP/7xj+mUU06h+fPne1NvVBQEQAAEQAAEQAAEQAAEQAAE4kAgsYL+zW9+k7773e8qxr/61a/o5JNPzsr7wIED9Jd/+Zfq7xdeeCF9/etfH3Hvpk2b6Nxzzx327+PGjaM3vvGNtHjxYpo5c2bmb/zfm5qa6H//93+LzvH06dNp4cKF9I1vfCNnjJUrV9L1118f6Tk/+MEPnIrz7t27ae7cufTOd76TvvrVr0aqE24CARAAARAAARAAARAAARAAARAYIpBIQR8YGFCiuHfvXtXIj3zkI/ThD384a86XLVtGn/rUpzJ/X7NmDTU2Ng67f+PGjXTeeefRCSecQG9729uop6eHnnnmGXr66afVfT/60Y/oTW96k/rvLOh1dXX02GOPFd3Pogr62rVraenSpcOe84tf/EL9bxZl83rve99Lp59+etF1ylcQgp6PEP4OAiAAAiAAAiAAAiAAAiAAAtkJJFLQn3jiCXr3u99NixYtorvuuktJ9a9//WuqqKgIJcHi+sgjj9Dll19Od955J/Ho+wUXXBAq6MHRYZ7S/U//9E/02te+lrQYl1LQwxok8fxCPjSDg4OKbRRB1/cWEh/3ggAIgAAIgAAIgAAIgAAIgEAaCCRS0FmYWZx5ZJyndT/44IPqv7/uda8bkdNXX32V3vKWt6hR8U984hNKzPm/33LLLZEEnYVz1qxZ1N3dTevXr6eampqSjqAXKujMgaf833jjjfTiiy+qFxfbtm1T0/R51P6LX/wibdiwgV555RXVplNPPZUuu+wyes973qPaZl7Lly+n2267Tc0imDhxopruf999942Y4n748GHiNfUPPPCAupdfmPAa9f/3//6fWgqgrxUrVtAdd9yhns/X1KlTacGCBerFSX19fRo+j2gjCIAACIAACIAACIAACIBAigkkTtD7+vroDW94gxK/9vZ2euihh9T0dhbQT3/60yNSzdPD//mf/1mtO+f15yyELK48PZ3XmOtLT3EPW1/NYsrT6detW0e1tbWxFnRu5/e+9z1Vx6eeeirTvi9/+cuK21lnnaVku62tjaqqquj3v/+9EvX3v//99MlPfjJzP8fQa/Xf/OY305EjR2j16tXq7yYjfoHxgQ98gH77299SQ0ODis9r85kXzzq4++67qbKyUok9vyDhe3ipQG9vL/FMCH72//zP/1Bra2uKP6ZoOgiAAAiAAAiAAAiAAAiAQBoIJE7QV61aRe973/vohhtuoI9//ON08OBB+ou/+Asl2zyNvbq6elheWSZ5VJdlsLm5mf7zP/+T/v3f/53+5V/+RU2TzyfoLJss/yy0LJl8SUwxj7oGPayT5nq+FnQW4X/4h39QMszr5fn/8yj11q1b1Ui6vngDvbPPPlsJsxb67du3qxFwZsqCzSPifL388stq13lT0Hn2Au8BwKPgn/3sZ9ULDB5R/8d//Ef6+c9/TkuWLKF3vOMd9K53vUvF5xcqPHLOF+8K/5Of/IQuuuiiYS9L0vDBRBtBAARAAARAAARAAARAAATSRyBxgs6jsCzK/P9ZmvniqdQ8Hfv2229Xm8fpi0fKecSc/7/e8V3v1j579my1fj0o6Lzb+9VXX0379+9Xm8TxmnW+br31VnrrW9/qjaCzHJs7z5tdnzfA4xkDO3bsUCPdPD2duTz++OM0ZswYNQ2dp8hfc8019JnPfCZTNGwNOo+88+j5b37zG5o8eXLmXh6Z5xcbegM/nkLP8b///e/TX/3VX6Xvk4gWgwAIgAAIgAAIgAAIgAAIpJ5AogRdj5bz6LDesI0zzEeR3XTTTWot9Ze+9KVM0lnKeUO4j33sY8OOUGNZZDE1p1brKe7BHsPTwb/yla8QT/PWlw8j6Pfff786r9y8eJo6M/mP//iP0A/GH/7wB2ppaVGzC/hlR1CmwwSdR9R5ZD3bpXPCLwy07J900kn0+te/Xo2sc/lsm/ul/tMLACAAAiAAAiAAAiAAAiAAAokikChB19Ops2WIxZ3XSesNx/R682z38xRwni7PlxZ0FvG//du/VVPCeWr3hAkTRhT3VdC/9a1v0Xe+8x2aNm0aXXfddUrguX28Pp1nIGhB17MU+MXHlClTMu0PE3RmwevIzRcjJjCWcV77zhcvQeAp73otO/8br1PnmQzBDeoS9SlEY0AABEAABEAABEAABEAABEAgaeegs1TyruQf+tCH1GZj5sX/zlOob775ZrVRGW/oxmubWSDPOeecYffyFG+WVRZVln5T0MM2iQv2JF8F/dxzzx02lV23izeHu+eeezKCrtex8/pwnvKvrzBB56PunnzySfq///s/Gj16dKQPXX9/v1qPzs/hsubygUgBcBMIgAAIgAAIgAAIgIAVAV7OOTAwoGZP6tmMmzdvpp07d9KcOXOsYqMwCEgR4BnAvFdWVM+Qeq7LOIkZQd+3b58aiTWl2gSnN3NjCeUp3Dzlnado85RuHkkPXnrzOD0VPNcu7kkRdD3azVKsjz/r6Oiga6+9VgmzHkHXO67zUgDeAV9fvCSAd2w3X2LwEgJmzP/+93//98NQ8fFuvAkd7xXwy1/+Ur0oMUfK9Q77n//859W6f1wgAAIgAAIgAAIgAALuCLDs/PSnP1W/lXm5J1886HXFFVeo33F8vC7vK8S/0Xy4frhyH336v7ZQZcXI2jaMrqFffWEaTZ9cZ9UUXqLJSzWDF2+kzBtV57r4t/Of/vQn+uhHP2pVB8nC+1/+I63+3jyqqKwcEXZg4AjN/dt2GnvC0OxX20svceXNpPmY7CgXz+Rlr9BLdXkG7nvf+96Mp0SJEfd7EiPoOsGcNJ6CHrx4VJbXNfN06z/+8Y9KBvmLhz8Uo0aNGnG/lkMejecPTaGCzs+5+OKLQ/PPHTDsmebNrndxD1uDzhu28YwB3iCP13/ziDjLuP6C1oLOb6nmzZun/v3888+n008/XY108xc2X6agd3V1qVh8L58vz/+dc8Ej6hybd3O/6qqr1EwG3hWey5588slqN3k+w56fxVPpjznmmLh/llA/EAABEAABEAABEPCaAC815P//xS9+UW3ay7/DeGPfr371q+qYXN8E/T03vUj/t72GjmlpGpGXV3fspBvfPY7eM+/oscrFJI8FnWcbsIOY1/HHH6+WxOa62DceeOABdWpRXK7nVv4THX76Rpo2cWSNnt9BVHnGF+jUBTeKVJcH+9g3du3apY64zseLH8qO9OMf/5j4mGu+2DV44+/TTjttxGldIpUsQ5DECDq/OeE3KDyV/cQTTwxFyTuP8w7kfC76t7/97WEiGSzAO5jzruy8zpzFU+/uzseB8ZrsXJceic52jz7SLVcMV4L+b//2b+qLl78MzOPUuC6vvvoqXX/99erYOX0tXLhQfWh4XTi/2Bg7dqz6E+9gzzu0Mye++O0qLzHg+Jdeein967/+ayYGx+UZC/oYOv0Hnh71d3/3d0rOv/a1r6kd8fnFhr54fTrnzNyArwyfETwSBEAABEAABEAABBJPgGc18tJFHkjiEU3z4uWfvIdTUND5dzcvSeTfyVyWf7edeuqpqihL1I9+9CP1O5L3LOIBL978l4/R/cY3vkH//d//rQZnWNL493W+watiEnD511+ktTvqaMK45hHFt2/fQf94WYuIoA8ODg777Ws+jNvG7f7Vr35FW7ZsUWzZRfj3MS8F5UEs3nOJL/4tzL+lmRdLP7sND/jxrIYvfOELmRF5/Zudj4bO5j3F8OIyLOiD626kU48evpQJ9dx2osE2GUHnmbQ8eMfLaLmNehmyfhgvTWYWGzZsoNbWVvqbv/kbJeI8A5r9jJ3kkksuUaLOy3F5sJZPnnrhhRfUCyZ98Uxe9gueAcLP5M2u2WvYP5g/z66O25UYQY8bWNv62Ai6zbN5rdFLL72kzo/nY9G0kIfF1Pfyf/IXSVVVVc5H8/nnfIY638+73+vN+nQh/nLjt2h79uxRX9g8al4ZMr3Gpn0oCwIgAAIgAAIgAAJxIbDw7+4rS1WW/9uFI57LsyFZWB599NGsMxdNQdezS3nmKosWn/DDo6A8bZsHcjgWT5XnmZEcm38HXnnllfS5z32O1q9fr4SJ17bzUkaesXrhhSPrZAunVIK+Zs0a+uu//uth1eX28+9d/k3PS3A/+MEPqnXSfPwz73XFM3t5AItlUU/vPvPMM9WAFx9RzOI4a9Yset3rXqcG1/i3Mc9k4Iul86GHHhox+GXLq5SCfsstt6iZu3zyFs8+6Ovry5wkxS8yeM8wfrnBA38s3dyHWNJ59i7PWpgxY4biy8txWfBZ5NeuXavK8GwP/hvPAGGB54FAnhFy3nnnKaY8sMv7KXz84x8fdmqLine truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2013,7 +2027,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2031,7 +2045,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2067,7 +2081,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2164,7 +2178,7 @@
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAH0CAYAAACuKActAAAgAElEQVR4Xuydd3wU1d7Gn9ndFJJACi0UCU1BkKKoWECvFQuIAvZ+Abk2UCygqAiKiFwLyEUpviqCgiIKeKUoWLCAXhQElAiE0DskkEbavJ9zdmczu9kku7OzO5vkmX+A3Tm/c+Z7Zpf9zjnnd5S0U89VwYMESIAESIAESIAESIAESIAESIAESMBSAgoF3VL+rJwESIAESIAESIAESIAESIAESIAEJAEKOm8EEiABEiABEiABEiABEiABEiABEogAAhT0COgENoEESIAESIAESIAESIAESIAESIAEKOi8B0iABEiABEiABEiABEiABEiABEggAghQ0COgE9gEEiABEiABEiABEiABEiABEiABEqCg8x4gARIgARIgARIgARIgARIgARIggQggQEGPgE5gE0iABEiABEiABEiABEiABEiABEiAgs57gARIgARIgARIgARIgARIgARIgAQigAAFPQI6gU0gARIgARIgARIgARIgARIgARIgAQo67wESIAESIAESIAESIAESIAESIAESiAACNVbQExIS0KZNGxQWFmL3nt04cfyE5bjvvvdeNG3aBOPHvWR5WwJpQL8BA9Clc2dMmfImDh08FEhRnksCJEACJEACJEACJEACJEACJOAngRon6Ff26oVB9w1G3bp1PRCUlpZie0YGJox/Gbt27ZLvfblsKRRFcZ+nqiry8vLw9Vdf4e2pb6Htqafizf9MgXj93rvuxoEDBzxi1m/QALNmfyBjDBk02B23IvZzPvoQySkpuKbXVZV2T7NmzTDz3f/zqwv/+usvDB/2iF/nGj1pyltvoU2b1njyscexYcMGo2FYjgRIgARIgARIgARIgARIgARIoBICNUrQW7dpjSlTp0phzs3NRfrmzbDZ7Gh+SjPUr99Avv76q69h+bJlHoKesS0Djig76tath+TkZPne77/9jqdHjsT4Vyaga9eu2LplCx5+8CEPlK9PfgPt25+On378CS+MGVPljeavoAuJn/jqvz3iNW6cCofDjqNHjyI/P9/93q+//IJpb71dZd3BnEBBD4Yey5IACZAACZAACZAACZAACZCAfwRqlKC/N2sWGqc2xsLPF+LtqVM9CKS1bInnx4zB+++9h2+/+cYt6GJ0/Nqrrnafe+lll+GJEU/Kf99x2+0oKirEnI/mSjl+7tln8euaX+R7Z599Nl54aZycQn9T/wE4efJklcT9FXRfgbRrG/nEk1i/fn2VdZl5AgXdTJqMRQIkQAIkQAIkQAIkQAIkQAK+CdQoQV/85ZdSpO8bdB927dxRZZ+LKe7egi4KffDhbDRo0BCT35iEJV9+iYGDB2LAjTfh+PETuHnAABl33iefoF5iPbw1dSoWfb6wyrrECaESdLHefvo7M7Ft2zb8Z/Kb+NcDD6BN29bIzy/AfQMH4eFhQ3HxP/6BmJhYyaeoqBhHjxzBjOnT8OMPP3q0/ZQWaRg9ZjSaNGkCm80mZyLY7XbExsaWm+J++513ou/1fSHqF8exY8cw8ZVXsO63390xxYORJ558Aqe0aIHo6GgUFhXh0MGDmPvhh/j6q6/94saTSIAESIAESIAESIAESIAESKA2EKhRgj5t5nS0aJGGX3/9FWNHj0ZxcUmlfViRoP/f++9JQZ06ZQoWL1osY3z86Xy5rn3unI/kv2+5/VYc2H8A99x1l9/3SagEXayFn/3hHHm9NpsixVocYt29mB2gXU9eXi5ycnKlUMfFxcmHE48Oe0QuBRDHKaecgrdnTJflhUjnnMiR1xwV5ZDv69egPz92LLqf112+fvjwISQk1JUSL46H7n9APiyoW68uZn/4oRTzgoICZGdlIzklCdHRMdi9azcGDxzoNzueSAIkQAIkQAIkQAIkQAIkQAI1nUCNEvTrb7gBQ+7/l+wzIZg7tm9Heno6fvnlF/fUdH2H+hJ0/Tr2f95zL/bt3SuLnHf++Rg95nm39IuRaE1E/b1JQi3ooh1iZHzuR3Ox7vffUb9hAzmaLabjb8vIwLGjR91NfXb0aFxw4QVYuWIlJk6YIF+f+vZbaNW6NTZu2IAnH39CCrw4ZrzzDpqf0twt6B3O6IhXX3tNSvcD/7rfzeiRRx9Fr6uvcq/X73/jjRg0eBD2798vk+xpxyWXXIKOnc7AlMlv+ouO55EACZAACZAACZAACZAACZBAjSdQowRd9Najjz+Gyy+/3D2KrPWgmKot1qXrp1ULQRfHgvmfylHlZs2ao2OnjjKZ3KaNG/H48Mc8boApU/+DNm3bytd++uknvPB81Ynh9AFCLejZ2dm45cabKrxp27Vvj45nnIEmTVLR9tS2MsFd+uZ0PDJ0qCyjZbX3XiLgvQb9uTHP4/zzz5dT+8U0ee0Qifje+2CWnBY/4IZ+0B6YHM8+jqEPPVQuC36N/3TxAkmABEiABEiABEiABEiABEggAAI1TtDFtYvR7Z49L0KXM7uiXbt2aH5KC/maOPSJ3ry3WdO4rfr+e7zy8svlpsinpqbi3Vnvy5HlG67r61diuHAK+sGDB3H3HXeW6/5zup+Lp0eNck9B15+gZacXa8+nz5wuR8XFtekPb0HXlhJUdJ+JqfZ9rrkGMTExWLDwc/fDEvEAYXvGdin1InM+DxIgARIgARIgARIgARIgARIggTICNVLQvTs4tk4dTHpzklyfvnbt//DMU6PkKdoI+oxp01FcXIxtGduwZfPfKCouqvAeqWjduj83VahH0H0JepOmTfF/770rmydk/JtvvsHmzZvRqGEjjHhqpHs6erduZ+PF8eOwZ88eDLr3n5UKupZRXmxFd/jw4XKXLta6i33kxdGwUUOMeuZZOfNAe0giXp//ycd4Z8Y7/mDjOSRAAiRAAiRAAiRAAiRAAiRQKwjUKEGPjhJZwgt9dlyf667DAw89KKdZ33OnM7GbEdk2UkZrkBWCfvfd98iEdn/9+SeGP/Kom43Y213s8a6NoGuJ5k6cOCG3jdMf3iPoL098BV26dMGM6dPl8gB/D5HR/e5775HT4/Py8tD/+hv8LcrzSIAESIAEajmB1CapsNvs2Lt3rztHini4fEqLU/D5Z5/Vcjq8fKsIiCWSxSUlKMjPt6oJftf7j0sugfj9t/yr5dizazfycvMqHZTyO3A1P1EMIMXHJ0DM9ORRPQlUp8+hP4RrlKD/d+kSuS3a1Cn/kRnM9cfrkyah/entsfrn1RgzenStEfQnnnwSl15+mdw7Xeyhrh0imZ5YI64Junhd26busUeH489Nm+SpyUnJmPbODJnNXcvifuNNN+GfgwZCjJTfdvOtHlP9xfmXXH4ZFsyfD5EMLicv1yNBn5j2/tmihXKd/9VX9vLnHuU5JEACJEACtZSA+OH8+JNPoudFF7mXS4llZunpm/Ho0EcgEp526typ3IPlSMV1ssE/kNusHxQfDVRKC5GQ/gochQeDar6W2NU7yKsTJ1a5veltt9+OM888C0887pmDJ6gGBVlYSS5B9OUnAdUHNVVF4TcxUI86lzEaPR4a+jCu7d27XHH9b8aKYn/6+QLs3bMXDz/4EMzkp/0mE/WK37S7d+/CyMdH4FjWMUOX+e7776NR40YyYfC3336L/gMG4KM5H2LW++9jzIsvyN11ZkwryytkqJKKCqkqRpbuQ5PSgvJnqCp22+Mw0d4k6Cr1zPTBqvq92W/AAAy+bzC05NBiOeu0t97G6p9/DqpN4r7qddXVctlndTuGXZSOVsknfDb7YG4dTFh5OkpKnbtGBXOI7/iFX3whv9+HPvQwtvz9d5XhfH3O9J/DKgNUgxNqlKAvWb5MIhcZ3A/s34+dO3aiadMmaHbKKYiOipKvi9FzLZu5kdFwI2W0+8CKEfQLe1yIZ557To42ZG7fjkOHD8t1+YmJibJZekEf+fRTcr90sYZ848YNSIiPR+s2bdw/ivTbrM2Z+xFSUlJQWFiI//36P+Tk5uDUtm0hRsmzjh3D7bfeBu3hwP59+2XSvYKTBTj7nHPQuHFj/PHHHxjx+BPV4CPCJpIACZAACVhFQCy9Ouusbli0cBHmffgREuom4No+vXFt7z7yR291E/ScVg+iUYfL0CC5bjmkGRmZKPpzBupk/RIUbiHocXF18OrEVz3ipG/+C7l5eZXGfvqZUTirWzeZ6DVSDnuHIsRfbkedpnXKNalgXz5yvipByZ9RQTVXiNTV4n56+hmPOHv37pE70VR2iFkcx08cl2JhJj8hm//79VfMn/8JOpzeAXfdc4/8bTvon57LEP25cDF48uHHczH2+TH4+aef5O+6iy6+GJs2bcShg4cgfp/u3LULTz05wp9wAZ+jqCqmFG7BWa1blysrfp/+vj0TD8acGnBc7wKC2bp1v+Oz+Qvcb5WiVO5oVNkhZpF27tQJP6z6Qc4oED4hlmou/PzzoNqk3Vdiy+PqdrzR91dc0VmFa6dlj+Yv+V3ByC/ORH6xcxvmYI6+11+Pfz1wv3wItWbNGowd/XyV4Xx9zvSfwyoDVIMTapSgPzTsYfTo0dMtn3r+2zMyMP6ll7Fr5w73y0K2S0pKA3qyZbWg6yVZu5DklBR8OPcjVJQkTqw1F9OatEMTcDHNSfyHIp5YiSPKEYVX33gNp552mvvco0edT2pTUpLx+GOPYdOGjfLfYirJqGefRZeuXeRouHaIhyBfLV+GKZPexCWXXYqHHn4IcXHxHh+FnTt3YvjQYVX+UKgGnx82kQRIgARIIEQEtMSs36xciVdedm4Hqh3i/6CcnJxygn7n3XdhwI03Ijo6Wu4o8urEf0shEcdTo57GeRdcgCiHQ878mv/JfMz54APUS6yHcS+/jNatWsl8NL+sWYM3J0+G2IHE7COn9QNoesaVaJhSr1zov7dsQ+EmcwRd/L9ckch9+vln2PzXX+jQsaNM5ip2c3nu2WdkrphxL70k5S3fNV37rtvvwPiLine truncated
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAH0CAYAAACuKActAAAgAElEQVR4Xuydd3wU1d7Gn9ndFJJACi0UCU1BkKKoWECvFQuIAvZ+Abk2UCygqAiKiFwLyEUpviqCgiIKeKUoWLCAXhQElAiE0DskkEbavJ9zdmczu9kku7OzO5vkmX+A3Tm/c+Z7Zpf9zjnnd5S0U89VwYMESIAESIAESIAESIAESIAESIAESMBSAgoF3VL+rJwESIAESIAESIAESIAESIAESIAEJAEKOm8EEiABEiABEiABEiABEiABEiABEogAAhT0COgENoEESIAESIAESIAESIAESIAESIAEKOi8B0iABEiABEiABEiABEiABEiABEggAghQ0COgE9gEEiABEiABEiABEiABEiABEiABEqCg8x4gARIgARIgARIgARIgARIgARIggQggQEGPgE5gE0iABEiABEiABEiABEiABEiABEiAgs57gARIgARIgARIgARIgARIgARIgAQigAAFPQI6gU0gARIgARIgARIgARIgARIgARIgAQo67wESIAESIAESIAESIAESIAESIAESiAACNVbQExIS0KZNGxQWFmL3nt04cfyE5bjvvvdeNG3aBOPHvWR5WwJpQL8BA9Clc2dMmfImDh08FEhRnksCJEACJEACJEACJEACJEACJOAngRon6Ff26oVB9w1G3bp1PRCUlpZie0YGJox/Gbt27ZLvfblsKRRFcZ+nqiry8vLw9Vdf4e2pb6Htqafizf9MgXj93rvuxoEDBzxi1m/QALNmfyBjDBk02B23IvZzPvoQySkpuKbXVZV2T7NmzTDz3f/zqwv/+usvDB/2iF/nGj1pyltvoU2b1njyscexYcMGo2FYjgRIgARIgARIgARIgARIgARIoBICNUrQW7dpjSlTp0phzs3NRfrmzbDZ7Gh+SjPUr99Avv76q69h+bJlHoKesS0Djig76tath+TkZPne77/9jqdHjsT4Vyaga9eu2LplCx5+8CEPlK9PfgPt25+On378CS+MGVPljeavoAuJn/jqvz3iNW6cCofDjqNHjyI/P9/93q+//IJpb71dZd3BnEBBD4Yey5IACZAACZAACZAACZAACZCAfwRqlKC/N2sWGqc2xsLPF+LtqVM9CKS1bInnx4zB+++9h2+/+cYt6GJ0/Nqrrnafe+lll+GJEU/Kf99x2+0oKirEnI/mSjl+7tln8euaX+R7Z599Nl54aZycQn9T/wE4efJklcT9FXRfgbRrG/nEk1i/fn2VdZl5AgXdTJqMRQIkQAIkQAIkQAIkQAIkQAK+CdQoQV/85ZdSpO8bdB927dxRZZ+LKe7egi4KffDhbDRo0BCT35iEJV9+iYGDB2LAjTfh+PETuHnAABl33iefoF5iPbw1dSoWfb6wyrrECaESdLHefvo7M7Ft2zb8Z/Kb+NcDD6BN29bIzy/AfQMH4eFhQ3HxP/6BmJhYyaeoqBhHjxzBjOnT8OMPP3q0/ZQWaRg9ZjSaNGkCm80mZyLY7XbExsaWm+J++513ou/1fSHqF8exY8cw8ZVXsO63390xxYORJ558Aqe0aIHo6GgUFhXh0MGDmPvhh/j6q6/94saTSIAESIAESIAESIAESIAESKA2EKhRgj5t5nS0aJGGX3/9FWNHj0ZxcUmlfViRoP/f++9JQZ06ZQoWL1osY3z86Xy5rn3unI/kv2+5/VYc2H8A99x1l9/3SagEXayFn/3hHHm9NpsixVocYt29mB2gXU9eXi5ycnKlUMfFxcmHE48Oe0QuBRDHKaecgrdnTJflhUjnnMiR1xwV5ZDv69egPz92LLqf112+fvjwISQk1JUSL46H7n9APiyoW68uZn/4oRTzgoICZGdlIzklCdHRMdi9azcGDxzoNzueSAIkQAIkQAIkQAIkQAIkQAI1nUCNEvTrb7gBQ+7/l+wzIZg7tm9Heno6fvnlF/fUdH2H+hJ0/Tr2f95zL/bt3SuLnHf++Rg95nm39IuRaE1E/b1JQi3ooh1iZHzuR3Ox7vffUb9hAzmaLabjb8vIwLGjR91NfXb0aFxw4QVYuWIlJk6YIF+f+vZbaNW6NTZu2IAnH39CCrw4ZrzzDpqf0twt6B3O6IhXX3tNSvcD/7rfzeiRRx9Fr6uvcq/X73/jjRg0eBD2798vk+xpxyWXXIKOnc7AlMlv+ouO55EACZAACZAACZAACZAACZBAjSdQowRd9Najjz+Gyy+/3D2KrPWgmKot1qXrp1ULQRfHgvmfylHlZs2ao2OnjjKZ3KaNG/H48Mc8boApU/+DNm3bytd++uknvPB81Ynh9AFCLejZ2dm45cabKrxp27Vvj45nnIEmTVLR9tS2MsFd+uZ0PDJ0qCyjZbX3XiLgvQb9uTHP4/zzz5dT+8U0ee0Qifje+2CWnBY/4IZ+0B6YHM8+jqEPPVQuC36N/3TxAkmABEiABEiABEiABEiABEggAAI1TtDFtYvR7Z49L0KXM7uiXbt2aH5KC/maOPSJ3ry3WdO4rfr+e7zy8svlpsinpqbi3Vnvy5HlG67r61diuHAK+sGDB3H3HXeW6/5zup+Lp0eNck9B15+gZacXa8+nz5wuR8XFtekPb0HXlhJUdJ+JqfZ9rrkGMTExWLDwc/fDEvEAYXvGdin1InM+DxIgARIgARIgARIgARIgARIggTICNVLQvTs4tk4dTHpzklyfvnbt//DMU6PkKdoI+oxp01FcXIxtGduwZfPfKCouqvAeqWjduj83VahH0H0JepOmTfF/770rmydk/JtvvsHmzZvRqGEjjHhqpHs6erduZ+PF8eOwZ88eDLr3n5UKupZRXmxFd/jw4XKXLta6i33kxdGwUUOMeuZZOfNAe0giXp//ycd4Z8Y7/mDjOSRAAiRAAiRAAiRAAiRAAiRQKwjUKEGPjhJZwgt9dlyf667DAw89KKdZ33OnM7GbEdk2UkZrkBWCfvfd98iEdn/9+SeGP/Kom43Y213s8a6NoGuJ5k6cOCG3jdMf3iPoL098BV26dMGM6dPl8gB/D5HR/e5775HT4/Py8tD/+hv8LcrzSIAESIAEajmB1CapsNvs2Lt3rztHini4fEqLU/D5Z5/Vcjq8fKsIiCWSxSUlKMjPt6oJftf7j0sugfj9t/yr5dizazfycvMqHZTyO3A1P1EMIMXHJ0DM9ORRPQlUp8+hP4RrlKD/d+kSuS3a1Cn/kRnM9cfrkyah/entsfrn1RgzenStEfQnnnwSl15+mdw7Xeyhrh0imZ5YI64Junhd26busUeH489Nm+SpyUnJmPbODJnNXcvifuNNN+GfgwZCjJTfdvOtHlP9xfmXXH4ZFsyfD5EMLicv1yNBn5j2/tmihXKd/9VX9vLnHuU5JEACJEACtZSA+OH8+JNPoudFF7mXS4llZunpm/Ho0EcgEp526typ3IPlSMV1ssE/kNusHxQfDVRKC5GQ/gochQeDar6W2NU7yKsTJ1a5veltt9+OM888C0887pmDJ6gGBVlYSS5B9OUnAdUHNVVF4TcxUI86lzEaPR4a+jCu7d27XHH9b8aKYn/6+QLs3bMXDz/4EMzkp/0mE/WK37S7d+/CyMdH4FjWMUOX+e7776NR40YyYfC3336L/gMG4KM5H2LW++9jzIsvyN11ZkwryytkqJKKCqkqRpbuQ5PSgvJnqCp22+Mw0d4k6Cr1zPTBqvq92W/AAAy+bzC05NBiOeu0t97G6p9/DqpN4r7qddXVctlndTuGXZSOVsknfDb7YG4dTFh5OkpKnbtGBXOI7/iFX3whv9+HPvQwtvz9d5XhfH3O9J/DKgNUgxNqlKAvWb5MIhcZ3A/s34+dO3aiadMmaHbKKYiOipKvi9FzLZu5kdFwI2W0+8CKEfQLe1yIZ557To42ZG7fjkOHD8t1+YmJibJZekEf+fRTcr90sYZ848YNSIiPR+s2bdw/ivTbrM2Z+xFSUlJQWFiI//36P+Tk5uDUtm0hRsmzjh3D7bfeBu3hwP59+2XSvYKTBTj7nHPQuHFj/PHHHxjx+BPV4CPCJpIACZAACVhFQCy9Ouusbli0cBHmffgREuom4No+vXFt7z7yR291E/ScVg+iUYfL0CC5bjmkGRmZKPpzBupk/RIUbiHocXF18OrEVz3ipG/+C7l5eZXGfvqZUTirWzeZ6DVSDnuHIsRfbkedpnXKNalgXz5yvipByZ9RQTVXiNTV4n56+hmPOHv37pE70VR2iFkcx08cl2JhJj8hm//79VfMn/8JOpzeAXfdc4/8bTvon57LEP25cDF48uHHczH2+TH4+aef5O+6iy6+GJs2bcShg4cgfp/u3LULTz05wp9wAZ+jqCqmFG7BWa1blysrfp/+vj0TD8acGnBc7wKC2bp1v+Oz+Qvcb5WiVO5oVNkhZpF27tQJP6z6Qc4oED4hlmou/PzzoNqk3Vdiy+PqdrzR91dc0VmFa6dlj+Yv+V3ByC/ORH6xcxvmYI6+11+Pfz1wv3wItWbNGowd/XyV4Xx9zvSfwyoDVIMTapSgPzTsYfTo0dMtn3r+2zMyMP6ll7Fr5w73y0K2S0pKA3qyZbWg6yVZu5DklBR8OPcjVJQkTqw1F9OatEMTcDHNSfyHIp5YiSPKEYVX33gNp552mvvco0edT2pTUpLx+GOPYdOGjfLfYirJqGefRZeuXeRouHaIhyBfLV+GKZPexCWXXYqHHn4IcXHxHh+FnTt3YvjQYVX+UKgGnx82kQRIgARIIEQEtMSs36xciVdedm4Hqh3i/6CcnJxygn7n3XdhwI03Ijo6Wu4o8urEf0shEcdTo57GeRdcgCiHQ878mv/JfMz54APUS6yHcS+/jNatWsl8NL+sWYM3J0+G2IHE7COn9QNoesaVaJhSr1zov7dsQ+EmcwRd/L9ckch9+vln2PzXX+jQsaNM5ip2c3nu2WdkrphxL70k5S3fNV37rtvvwPiLine truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2182,7 +2196,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2200,7 +2214,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2236,7 +2250,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2272,7 +2286,7 @@
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAD6CAYAAAAyVW3pAAAgAElEQVR4XuydB3xV9fn/Pyd7QQYBAmGpKKhlONtqK2pbtVZFwFVnFan9t4pA3YNWEXG0/XUoLpyAWlAUV92zInUBggoqmAABwkhIyF7n//qek4QsyM19zrn3uTef83rxgiTn+3yf835uQt73u6zB+x9pgxcJkAAJkAAJkAAJkAAJkAAJkAAJkEBYCVgU9LDyZ+ckQAIkQAIkQAIkQAIkQAIkQAIk4BCgoPOFQAIkQAIkQAIkQAIkQAIkQAIkQAIKCFDQFRSBKZAACZAACZAACZAACZAACZAACZAABZ2vARIgARIgARIgARIgARIgARIgARJQQICCrqAITIEESIAESIAESIAESIAESIAESIAEKOh8DZAACZAACZAACZAACZAACZAACZCAAgIUdAVFYAokQAIkQAIkQAIkQAIkQAIkQAIkQEHna4AESIAESIAESIAESIAESIAESIAEFBCIWkFPS0vDfvvth5qaGmws2IhdpbvCjvuiiy9G//79MGvm7WHPRZLA7y+/HFlZWbjt1lslYdiWBEiABEiABEiABEiABEiABEigBYGoE/QTTjwRl/52Enr06NGq0A0NDfh+3TrcOesObNiwwfnaK6+9Csuymu+zbRsVFRV48403cP/s+zB0//3xr3vvgfn8xRdehMLCwlYxe2Vn44l5c50Yl106qTnunl5h8596EplZWTj5xJP2+iLMzc3FnEcfCeiF+vXXX2PalVMCutfcdM111yInJwfTpkwNuE3bG599fhFSUlLxyxNO3GuMI448ErfeNmOP99TV1ePUk08OOg82JAESIAESIAESIAESIAESIIFoIhBVgr7vfvvintmzHWEuLy/HmtWrERMTiwEDc9GrV7bz+f/769/w+muvtRL0dWvXIS4+Fj169ERmZqbztWWfL8MN112HWXfdidGjR+O7b7/FFX+4vFXt/++ff8fw4QdiyYdLMOOWWzp9XQQq6Ebi7/7rX1rF69s3B3FxsSgqKkJlZWXz1z75+GM8cN/9nfbddMMzzy1Camrncr23gIEK+g9/+EP8ecatMCJeWLilXcjKiop2TAN+EN5IAiRAAiRAAiRAAiRAAiRAAlFGIKoE/bEnnkDfnL5Y/Pxi3D97dqtSDR4yBH++5RY8/thjePedd5oF3YyO/+qkXzbfe/zPfoarr73G+fj8c89DbW0N5j/1tCPH02++GZ/872Pna4cffjhm3D7TmUJ/1oQzUF1d3elLI1BB7yhQ07Ndd/U1WLFixV77Mm9EmOfq6AqHoBcUFODSiy/plE/TDXvLv2WQQO8LuGPeSAIkQAIkQAIkQAIkQAIkQAJhJBBVgv7iK684Iv3bS3+LDevzO8Vqpri3FXTTaO6T85Cd3Rv//Ps/8J9XXsHESRNxxplnobR0F84+4wwn7r8XLkTP9J64b/ZsvPD84k77Mjf4LehXXX01jvrJ0UhOTkZtbR02rF+Pm268EcVFRU5+s++/D0P22ceZSVBcXNyc8223zsBXX36Jf97zLwwYOBCJiYmIiYlxYqxbuxZ//ctfW/Hs6gh6IILet29f/OmWPyN34EAkxMejpqYan37yGWbNvM0ZgW+64uPiceP0mzFy1Mjm59y4YT2m3zQd27dva77PrPc/+Ve/QlpaqvM5M+vgiy++wN/u/gvKysoCqhdvIgESIAESIAESIAESIAESIIFQEogqQX9gzoMYNGgwPvnkE9z6pz+1EruOoO5J0B95/DH069cPs++5By++8KLTdMGzzzjr2p+e/5Tz8Tnn/RqFWwrxmwsvDLhefgq6WStv1sybtfZbC7ciPSPdEdia2lqc/+tfO5vkNY3Cm4Srqqqa8/7zzdOdUfmXX/2PI++7dpWhqqoSmZlZiI+Pc+T2nDPOQk1tjdPGa0EfOGiw8+aBeXPFvClQtGOHMxPCXKu/Xo2pV17p/NvkNv/pp5xlCOaNFcO/T98+zpsJJsczxo13nt9sYnfqaac695g3J2Lj4tCzZ0+n/R+nTnPejOBFAiRAAiRAAiRAAiRAAiRAAtoIRJWgnz5uHC77f79zGBsxzf/+e6xZswYff/xx89T0lgXoSNBbrmO/5DcXY/OmTU6TH/34x84Ib9NorpHJy//f77F27dqAa+qXoB/xwyNx64wZjnRPuuTS5pHkpjXyHy/9H/40fbqT596muB8zZgyWLvmoWcSN+M59cr6zY7uR+P/9739BCboR5ZZvCDQBO3vCmaitq8V9D9zvjOy/8vLLuOef/3LEOjMjE4/Nn+uMpv/ut5chPy8Pky67DOMnjEd+fj6mXTkVFRXlMCPqsx+4HwMGDsBT85/EE48/jmeffw4pKSm46fob8dlnnzrdmdkOU6ZNw8MPPgQzos+LBEiABEiABEiABEiABEiABLQRiCpBN3CnXvVH/PznP3dGVVteZtM4sy79zTfebP60EXRzLXrmWZhj2XJzB+DgEQc7I61frlqFq6b9sVWMe2bfi/2GDnU+t2TJEsz4c+cbw7UM4Jeg3zrzNhxxxBFY8O9/49GHd+/+bnaZn/fkfEeOx502tlNBNzcYbocdcTj2H7o/+vTpgyOOPAJZvXph3tx5mD93blCCbhqZke2WV0ODjfGnjUV9Q70zcm9Gzs84fRwa7N3T2WfdeSd+MGKEs6P+4uefx5P/ftoZPf/jtGn4ZvXq5nDjxk/AJZdOxPLly3H9Ndc2vwkx74m5mD9vnrbvOeZDAiRAAiRAAiRAAiRAAiRAAh0SiDpBN09pRrd/+tNjMOqQ0Rg2bBgGDBzkfM5cLTd6a3vMWhOhD95/H3fdcUe7KfLmeLJHn3jcGeE1whvIxnChEPSHHn7YGUG+8bob8Pnnn7Uq9OKXX3JGoZuORNvbCPpvLrkYZ551Vrs3N0zAptFp828vp7gfMGwY/vGvf+7129MsMzDLDV54+WVnyv2eLnN83m8nXuq8SXPCCSc4t5mZFFu3FMLUdO4TT+xx8zz+fCABEiABEiABEiABEiABEiCBcBOISkFvCzUpORn/+Nc/nPXpZsqzmfpsrqYR9IceeBB1dXVYu24tvl39jTPtek/XntatB1JIv0bQm9aWd7S+evFLLyAhIdE5e928sbAnQZ9w5pm4dNKlzj0fffSRsyTg22++wZlnn4Uxxx7rm6A3nZVu1pD/94P/dojx7TffdEbHm95QeeP1Nzq8z+T74gsvOF8zz3P2OWc7+wY0XaaPX591dpffWAmktryHBEiABEiABEiABEiABEiABKQEokrQE+ITmtdPtwVz6mmn4feX/wGFhYX4zQXuxm7ByHYwbZpy8UvQzZnpZir47HvubRZU06eZNWBGnWvr6jD2V6c4aexJ0Jum7z/26KP491NPN+ObMnUqTvzlSb4JullasHDRs8568gmnj9/r67lp5P6cM89CSUlJQK99syP9CSeeADM7ICUlFY/MeRgLFywIqC1vIgESIAESIAESIAESIAESIIFQEogqQTdrmc2xaEZU2655/r9//APDDxyOpR8txS1/+lNUCfp555+P8y+8AFu2bMHFF17U/PppOh5u44aNmDRxovN58yaBWVP+h//3/7Bu7brmex+b+wTMUWcP3v8Anlu0yPm82YDt7//6J8zGeX5NcTf9NG3qZs6of/pJd5f8puukX/4SX375lXPM21/+9lcc/IMfYPXqrzF18pRW9x144IHo3acP3n/vPfzu97/How8/3Gqk/IabbsRPjzkG//3gA8yccVsov8fYFwmQAAmQAAmQAAmQAAmQAAkERCCqBP0/r7/mPLRZd1y4ZQvW569H//79dp+tXVvrjJ43nQsezGh4MG2aKuHXCLrZ1M6MjJudy7ds3oKlSz9yzjM//PDDna5bTn2/bdZMHHbY4c5Z4J99+qmzEdz9992P08edjuOOP97ZrG3N16vRgAYMHz7MmR5vLj8F/bjjjsM111/n9JNndt5fvQa9+/TG0KH7O7uvz5p5uyPeZtO7J+bNddbIm3Pcl332OeIT4rH//gcgp1+Os8789ttmOjMjzG77ZqO/td99h4GGxZFHOJv/XTbpslZnugf0XcKbSIAESIAESIAESIAESIAESCAEBKJK0C+/8gr85Cc/RXp6ejt0369bh1m339FKzozI1dc34NSTTw4YdbgF/Zo/XoWVK1e2y9cIqpnqnp3du/lrRrb//re/4e233mr+nJHcGbfNcI41M8JqLrNx3vLPluG+hx5Abm5u870VFRXOkW1m7f6T8+dj7uNPOF8LdJO4puPfWo7g7wn0cT87HpdfcbkzDb3lZc46v/mmm5vrZkbzb/7Tn2A27Gt57dq1Cw898ADM+nQzW2LY8GHNz2fuM2/aPP7oY1j0zDMB15o3kgAJkAAJkAAJkAAJkAAJkEAoCUSVoDeBM2vRBw0ZjP79+2PTpk1Yt3ZtuynvoYTc1JdkBD3QfI2AH3zwwSjYuHGvZ7SbjfOGDxuG/Lx8FO8sbg6/3377YdCgQdhYUOBsEhfqy6xJHz58OHaVlWHdd2v3uGGfmX5/wIHDEB8fj++++daZEdDyMm8+DBo82NndftPGAufs9LbLHkL9bOyPBEiABEiABEiABEiABEiABPZGICoFXWvJQyHoWp+deZEACZAACZAACZAACZAACZDAHgnEJwEjTwD6DQMSkoH85cAXrwO1VZ5AM0tnS0tKW8Xq6HOedCYIQkEXwOtqUwp6V4nxfhIgARIgARIgARIgARIggW5B4EdnA/2HtX7U/BXAZ4vFjz94yBDMvv8+/OXuu/Hf9z5AXX0d7rj7LgwYMBDnnXOOOL6XASjoXtLsJJbZnC09I73VmvAQds+uSIAESIAESIAESIAESIAESCA0BMZPl/dj20DjvlkBB1t0a4e3TrvqKvzihF84X7Nt2/lzw7XXYcWKFQGHDsWNFPRQUGYfJEACJEACJEACJEACJEACJNCLine truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2307,7 +2321,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2325,7 +2339,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2343,7 +2357,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2361,7 +2375,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
@@ -2379,7 +2393,7 @@
"outputs": [
{
"data": {
"image/png": "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 truncated
"image/png": "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 truncated
},
"metadata": {},
"output_type": "display_data"
examples/example.ipynb
+87 -86
View File
@@ -20,7 +20,7 @@
"output_type": "stream",
"text": [
"\n",
"Pandas TA v0.3.54b0\n",
"Pandas TA v0.3.63b0\n",
"To install the Latest Version:\n",
"$ pip install -U git+https://github.com/twopirllc/pandas-ta\n",
"\n",
@@ -64,15 +64,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Pandas TA - Technical Analysis Indicators - v0.3.54b0\n",
"Pandas TA - Technical Analysis Indicators - v0.3.63b0\n",
"\n",
"Indicators and Utilities [147]:\n",
" aberration, accbands, ad, adosc, adx, alligator, alma, amat, ao, aobv, apo, aroon, atr, atrts, bbands, bias, bop, brar, cci, cdl_pattern, cdl_z, cfo, cg, chop, cksp, cmf, cmo, coppock, cti, cube, decay, decreasing, dema, dm, donchian, dpo, ebsw, efi, ema, entropy, eom, er, eri, fisher, fwma, ha, hilo, hl2, hlc3, hma, hwc, hwma, ichimoku, ifisher, increasing, inertia, jma, kama, kc, kdj, kst, kurtosis, kvo, linreg, log_return, long_run, macd, mad, massi, mcgd, median, mfi, midpoint, midprice, mom, natr, nvi, obv, ohlc4, pdist, percent_return, pgo, ppo, psar, psl, pvi, pvo, pvol, pvr, pvt, pwma, qqe, qstick, quantile, reflex, remap, rma, roc, rsi, rsx, rvgi, rvi, short_run, sinwma, skew, slope, sma, smi, smma, squeeze, squeeze_pro, ssf, ssf3, stc, stdev, stoch, stochf, stochrsi, supertrend, swma, t3, td_seq, tema, thermo, tos_stdevall, trendflex, trima, trix, true_range, tsi, tsignals, ttm_trend, ui, uo, variance, vhf, vidya, vortex, vwap, vwma, wb_tsv, wcp, willr, wma, xsignals, zlma, zscore\n",
"Indicators and Utilities [148]:\n",
" aberration, accbands, ad, adosc, adx, alligator, alma, amat, ao, aobv, apo, aroon, atr, atrts, bbands, bias, bop, brar, cci, cdl_pattern, cdl_z, cfo, cg, chop, cksp, cmf, cmo, coppock, cti, cube, decay, decreasing, dema, dm, donchian, dpo, ebsw, efi, ema, entropy, eom, er, eri, fisher, fwma, ha, hilo, hl2, hlc3, hma, hwc, hwma, ichimoku, ifisher, increasing, inertia, jma, kama, kc, kdj, kst, kurtosis, kvo, linreg, log_return, long_run, macd, mad, mama, massi, mcgd, median, mfi, midpoint, midprice, mom, natr, nvi, obv, ohlc4, pdist, percent_return, pgo, ppo, psar, psl, pvi, pvo, pvol, pvr, pvt, pwma, qqe, qstick, quantile, reflex, remap, rma, roc, rsi, rsx, rvgi, rvi, short_run, sinwma, skew, slope, sma, smi, smma, squeeze, squeeze_pro, ssf, ssf3, stc, stdev, stoch, stochf, stochrsi, supertrend, swma, t3, td_seq, tema, thermo, tos_stdevall, trendflex, trima, trix, true_range, tsi, tsignals, ttm_trend, ui, uo, variance, vhf, vidya, vortex, vwap, vwma, wb_tsv, wcp, willr, wma, xsignals, zlma, zscore\n",
"\n",
"Candle Patterns [62]:\n",
" 2crows, 3blackcrows, 3inside, 3linestrike, 3outside, 3starsinsouth, 3whitesoldiers, abandonedbaby, advanceblock, belthold, breakaway, closingmarubozu, concealbabyswall, counterattack, darkcloudcover, doji, dojistar, dragonflydoji, engulfing, eveningdojistar, eveningstar, gapsidesidewhite, gravestonedoji, hammer, hangingman, harami, haramicross, highwave, hikkake, hikkakemod, homingpigeon, identical3crows, inneck, inside, invertedhammer, kicking, kickingbylength, ladderbottom, longleggeddoji, longline, marubozu, matchinglow, mathold, morningdojistar, morningstar, onneck, piercing, rickshawman, risefall3methods, separatinglines, shootingstar, shortline, spinningtop, stalledpattern, sticksandwich, takuri, tasukigap, thrusting, tristar, unique3river, upsidegap2crows, xsidegap3methods\n",
"\n",
"Total Candles, Indicators and Utilities: 209\n"
"Total Candles, Indicators and Utilities: 210\n"
]
}
],
@@ -207,30 +207,30 @@
"==== Market Information =====================================================\n",
"Market | Exchange | Symbol | Category US | PCX | SPY | Large Blend\n",
"\n",
"NAV | Yield 419.57 | 1.3000%\n",
"NAV | Yield 412.07 | 1.3000%\n",
"\n",
"\n",
"\n",
"==== Price Information =====================================================\n",
"Open High Low | Close 451.1600 451.1600 448.4300 | 449.2300\n",
"HL2 | HLC3 | OHLC4 | C - OHLC4 450.7050, 450.2133, 450.4500, -1.2200\n",
"Change (%) -1.2600 (-0.2797%)\n",
"Bid | Ask | Spread 449.94 x 1100 | 449.92 x 1100 | -0.0200\n",
"Open High Low | Close 423.5900 423.5900 411.2100 | 412.0000\n",
"HL2 | HLC3 | OHLC4 | C - OHLC4 418.5400, 416.3600, 418.1675, -6.1675\n",
"Change (%) -15.8100 (-3.6956%)\n",
"Bid | Ask | Spread 413.35 x 1400 | 413.45 x 4000 | 0.1000\n",
"Volume | Market | Avg Vol (10Day) \n",
" 24,409,187 | 24,409,187 | 113,048,470 (95,835,200)\n",
" 145,491,088 | 145,491,088 | 105,112,352 (102,313,060)\n",
"\n",
"52Wk Range (% from 52Wk Low) 390.29 - 479.98 : 89.6900 (15.1016%)\n",
"SMA 50 | SMA 200 440.5880 | 446.4628\n",
"Avg. Return 3Yr | 5Yr 16.8800% | 13.9900%\n",
"52Wk Range (% from 52Wk Low) 404 - 479.98 : 75.9800 (1.9802%)\n",
"SMA 50 | SMA 200 437.1890 | 448.1483\n",
"Avg. Return 3Yr | 5Yr 15.2500% | 14.4100%\n",
"\n",
"==== Dividends / Splits =====================================================\n",
"Trailing Annual Dividend Rate | Yield 5.563 | 1.2349%\n",
"Trailing Annual Dividend Rate | Yield 5.662 | 1.3235%\n",
"\n",
"\n",
"Stock Splits (Last 5 of 118):\n",
"Date 2022-03-18 2021-12-17 2021-09-17 2021-06-18 2021-03-19\n",
"Ratio 1.366 1.633 1.428 1.376 1.278\n",
"[+] yf | SPY(7343, 7): 3078.9023 ms (3.0789 s)\n"
"[+] yf | SPY(7367, 7): 3189.1786 ms (3.1892 s)\n"
]
}
],
@@ -260,7 +260,7 @@
"output_type": "stream",
"text": [
"\n",
"From 2021-03-29 00:00:00 to 2022-03-25 00:00:00\n"
"From 2021-05-03 00:00:00 to 2022-04-29 00:00:00\n"
]
},
{
@@ -305,52 +305,52 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2021-03-29</th>\n",
" <td>389.277324</td>\n",
" <td>391.596807</td>\n",
" <td>387.707979</td>\n",
" <td>390.639404</td>\n",
" <td>108107600</td>\n",
" <th>2021-05-03</th>\n",
" <td>413.982163</td>\n",
" <td>414.386841</td>\n",
" <td>412.245043</td>\n",
" <td>412.768158</td>\n",
" <td>68128300</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-30</th>\n",
" <td>389.297056</td>\n",
" <td>390.313677</td>\n",
" <td>387.915216</td>\n",
" <td>389.603027</td>\n",
" <td>76262200</td>\n",
" <th>2021-05-04</th>\n",
" <td>410.665847</td>\n",
" <td>411.188962</td>\n",
" <td>406.323003</td>\n",
" <td>410.221680</td>\n",
" <td>101591200</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-03-31</th>\n",
" <td>390.205119</td>\n",
" <td>392.830573</td>\n",
" <td>390.175510</td>\n",
" <td>391.182251</td>\n",
" <td>112734200</td>\n",
" <th>2021-05-05</th>\n",
" <td>411.958809</td>\n",
" <td>412.205562</td>\n",
" <td>409.757763</td>\n",
" <td>410.349976</td>\n",
" <td>60162200</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-01</th>\n",
" <td>393.225360</td>\n",
" <td>395.465895</td>\n",
" <td>393.008216</td>\n",
" <td>395.406647</td>\n",
" <td>99682900</td>\n",
" <th>2021-05-06</th>\n",
" <td>410.428955</td>\n",
" <td>413.765058</td>\n",
" <td>408.306886</td>\n",
" <td>413.626892</td>\n",
" <td>74321400</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-04-05</th>\n",
" <td>398.219613</td>\n",
" <td>401.654423</td>\n",
" <td>398.140665</td>\n",
" <td>401.081940</td>\n",
" <td>91684800</td>\n",
" <th>2021-05-07</th>\n",
" <td>414.436221</td>\n",
" <td>417.328158</td>\n",
" <td>413.715692</td>\n",
" <td>416.637238</td>\n",
" <td>67733800</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
@@ -361,19 +361,19 @@
"text/plain": [
" Open High Low Close Volume \\\n",
"Date \n",
"2021-03-29 389.277324 391.596807 387.707979 390.639404 108107600 \n",
"2021-03-30 389.297056 390.313677 387.915216 389.603027 76262200 \n",
"2021-03-31 390.205119 392.830573 390.175510 391.182251 112734200 \n",
"2021-04-01 393.225360 395.465895 393.008216 395.406647 99682900 \n",
"2021-04-05 398.219613 401.654423 398.140665 401.081940 91684800 \n",
"2021-05-03 413.982163 414.386841 412.245043 412.768158 68128300 \n",
"2021-05-04 410.665847 411.188962 406.323003 410.221680 101591200 \n",
"2021-05-05 411.958809 412.205562 409.757763 410.349976 60162200 \n",
"2021-05-06 410.428955 413.765058 408.306886 413.626892 74321400 \n",
"2021-05-07 414.436221 417.328158 413.715692 416.637238 67733800 \n",
"\n",
" Dividends Stock Splits \n",
"Date \n",
"2021-03-29 0.0 0 \n",
"2021-03-30 0.0 0 \n",
"2021-03-31 0.0 0 \n",
"2021-04-01 0.0 0 \n",
"2021-04-05 0.0 0 "
"2021-05-03 0.0 0 \n",
"2021-05-04 0.0 0 \n",
"2021-05-05 0.0 0 \n",
"2021-05-06 0.0 0 \n",
"2021-05-07 0.0 0 "
]
},
"execution_count": 7,
@@ -833,7 +833,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"[i] Loaded SPY(7343, 34)\n",
"[i] Loaded SPY(7367, 34)\n",
"[+] Study: Common Price and Volume SMAs\n",
"[i] Indicator arguments: {'append': True}\n",
"[i] No mulitproccessing (cores = 0).\n"
@@ -843,7 +843,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"[i] Progress: 100%|███████████████████| 5/5 [00:00<00:00, 792.48it/s]"
"[i] Progress: 100%|███████████████████████████| 5/5 [00:00<00:00, 774.71it/s]"
]
},
{
@@ -852,7 +852,8 @@
"text": [
"[i] Total indicators: 5\n",
"[i] Columns added: 5\n",
"[i] Last Run: Friday March 25, 2022, NYSE: 4:35:36, Local: 8:35:36 PDT, Day 84/365 (23.00%)\n"
"[i] Last Run: Sunday May 1, 2022, NYSE: 14:14:53, Local: 18:14:53 PDT, Day 121/365 (33.00%)\n",
"tsignals\n"
]
},
{
@@ -864,7 +865,7 @@
},
{
"data": {
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truncated
"image/png": 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truncated
"text/plain": [
"<Figure size 1200x1000 with 12 Axes>"
]
@@ -872,16 +873,6 @@
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<__main__.Chart at 0x1341a8430>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
@@ -889,10 +880,20 @@
"\n",
"Pandas v: 1.3.0 [pip install pandas] https://github.com/pandas-dev/pandas\n",
"Data from AlphaVantage v: 1.0.19 [pip install alphaVantage-api] http://www.alphavantage.co https://github.com/twopirllc/AlphaVantageAPI\n",
"Technical Analysis with Pandas TA v: 0.3.54b0 [pip install pandas_ta] https://github.com/twopirllc/pandas-ta\n",
"Technical Analysis with Pandas TA v: 0.3.63b0 [pip install pandas_ta] https://github.com/twopirllc/pandas-ta\n",
"Charts by Matplotlib Finance v: 0.12.7a17 [pip install mplfinance] https://github.com/matplotlib/mplfinance\n",
"\n"
]
},
{
"data": {
"text/plain": [
"<__main__.Chart at 0x131e9c0a0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
@@ -960,7 +961,7 @@
{
"data": {
"text/plain": [
"<AxesSubplot:title={'center':'SPY: CUMLOGRET_1 from 2021-03-29 00:00:00 to 2022-03-25 00:00:00 (252)'}, xlabel='Date'>"
"<AxesSubplot:title={'center':'SPY: CUMLOGRET_1 from 2021-05-03 00:00:00 to 2022-04-29 00:00:00 (252)'}, xlabel='Date'>"
]
},
"execution_count": 12,
@@ -969,7 +970,7 @@
},
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<Figure size 1600x325 with 1 Axes>"
]
@@ -1001,7 +1002,7 @@
{
"data": {
"text/plain": [
"<matplotlib.lines.Line2D at 0x13654fdf0>"
"<matplotlib.lines.Line2D at 0x13420fa00>"
]
},
"execution_count": 13,
@@ -1010,7 +1011,7 @@
},
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<Figure size 1600x325 with 1 Axes>"
]
@@ -1040,7 +1041,7 @@
{
"data": {
"text/plain": [
"<matplotlib.lines.Line2D at 0x1366212e0>"
"<matplotlib.lines.Line2D at 0x1342ae1c0>"
]
},
"execution_count": 14,
@@ -1049,7 +1050,7 @@
},
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<Figure size 1600x325 with 1 Axes>"
]
@@ -1073,7 +1074,7 @@
{
"data": {
"text/plain": [
"<matplotlib.lines.Line2D at 0x1364e3ac0>"
"<matplotlib.lines.Line2D at 0x1342935b0>"
]
},
"execution_count": 15,
@@ -1082,7 +1083,7 @@
},
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<Figure size 1600x325 with 1 Axes>"
]
@@ -1123,7 +1124,7 @@
"outputs": [
{
"data": {
"image/png": 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truncated
"image/png": 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truncated
"text/plain": [
"<Figure size 1200x1000 with 8 Axes>"
]
@@ -1138,7 +1139,7 @@
"\n",
"Pandas v: 1.3.0 [pip install pandas] https://github.com/pandas-dev/pandas\n",
"Data from AlphaVantage v: 1.0.19 [pip install alphaVantage-api] http://www.alphavantage.co https://github.com/twopirllc/AlphaVantageAPI\n",
"Technical Analysis with Pandas TA v: 0.3.54b0 [pip install pandas_ta] https://github.com/twopirllc/pandas-ta\n",
"Technical Analysis with Pandas TA v: 0.3.63b0 [pip install pandas_ta] https://github.com/twopirllc/pandas-ta\n",
"Charts by Matplotlib Finance v: 0.12.7a17 [pip install mplfinance] https://github.com/matplotlib/mplfinance\n",
"\n"
]
@@ -1146,7 +1147,7 @@
{
"data": {
"text/plain": [
"<__main__.Chart at 0x1367127f0>"
"<__main__.Chart at 0x1341d29a0>"
]
},
"execution_count": 17,
@@ -1175,7 +1176,7 @@
"outputs": [
{
"data": {
"image/png": 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truncated
"image/png": 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truncated
"text/plain": [
"<Figure size 1200x1000 with 8 Axes>"
]
@@ -1190,7 +1191,7 @@
"\n",
"Pandas v: 1.3.0 [pip install pandas] https://github.com/pandas-dev/pandas\n",
"Data from AlphaVantage v: 1.0.19 [pip install alphaVantage-api] http://www.alphavantage.co https://github.com/twopirllc/AlphaVantageAPI\n",
"Technical Analysis with Pandas TA v: 0.3.54b0 [pip install pandas_ta] https://github.com/twopirllc/pandas-ta\n",
"Technical Analysis with Pandas TA v: 0.3.63b0 [pip install pandas_ta] https://github.com/twopirllc/pandas-ta\n",
"Charts by Matplotlib Finance v: 0.12.7a17 [pip install mplfinance] https://github.com/matplotlib/mplfinance\n",
"\n"
]
@@ -1198,7 +1199,7 @@
{
"data": {
"text/plain": [
"<__main__.Chart at 0x1367000d0>"
"<__main__.Chart at 0x1340473d0>"
]
},
"execution_count": 18,
pandas_ta/candles/cdl_pattern.py
+14 -12
View File
@@ -7,18 +7,20 @@ from pandas_ta.candles import cdl_doji, cdl_inside
ALL_PATTERNS = [
"2crows", "3blackcrows", "3inside", "3linestrike", "3outside", "3starsinsouth",
"3whitesoldiers", "abandonedbaby", "advanceblock", "belthold", "breakaway",
"closingmarubozu", "concealbabyswall", "counterattack", "darkcloudcover", "doji",
"dojistar", "dragonflydoji", "engulfing", "eveningdojistar", "eveningstar",
"gapsidesidewhite", "gravestonedoji", "hammer", "hangingman", "harami",
"haramicross", "highwave", "hikkake", "hikkakemod", "homingpigeon",
"identical3crows", "inneck", "inside", "invertedhammer", "kicking", "kickingbylength",
"ladderbottom", "longleggeddoji", "longline", "marubozu", "matchinglow", "mathold",
"morningdojistar", "morningstar", "onneck", "piercing", "rickshawman",
"risefall3methods", "separatinglines", "shootingstar", "shortline", "spinningtop",
"stalledpattern", "sticksandwich", "takuri", "tasukigap", "thrusting", "tristar",
"unique3river", "upsidegap2crows", "xsidegap3methods"
"2crows", "3blackcrows", "3inside", "3linestrike", "3outside",
"3starsinsouth", "3whitesoldiers", "abandonedbaby", "advanceblock",
"belthold", "breakaway", "closingmarubozu", "concealbabyswall",
"counterattack", "darkcloudcover", "doji", "dojistar", "dragonflydoji",
"engulfing", "eveningdojistar", "eveningstar", "gapsidesidewhite",
"gravestonedoji", "hammer", "hangingman", "harami", "haramicross",
"highwave", "hikkake", "hikkakemod", "homingpigeon", "identical3crows",
"inneck", "inside", "invertedhammer", "kicking", "kickingbylength",
"ladderbottom", "longleggeddoji", "longline", "marubozu", "matchinglow",
"mathold", "morningdojistar", "morningstar", "onneck", "piercing",
"rickshawman", "risefall3methods", "separatinglines", "shootingstar",
"shortline", "spinningtop", "stalledpattern", "sticksandwich", "takuri",
"tasukigap", "thrusting", "tristar", "unique3river", "upsidegap2crows",
"xsidegap3methods"
]
pandas_ta/cycles/reflex.py
+4 -2
View File
@@ -13,7 +13,8 @@ except ImportError:
@njit
def np_reflex(
x: Array, n: Int, k: Int, alpha: IntFloat, pi: IntFloat, sqrt2: IntFloat
x: Array, n: Int, k: Int,
alpha: IntFloat, pi: IntFloat, sqrt2: IntFloat
):
m, ratio = x.size, 2 * sqrt2 / k
a = exp(-pi * ratio)
@@ -88,7 +89,8 @@ def reflex(
# Validate
length = v_pos_default(length, 20)
smooth = v_pos_default(smooth, 20)
close = v_series(close, max(length, smooth))
_length = max(length, smooth) + 1
close = v_series(close, _length)
if close is None:
return
pandas_ta/momentum/bop.py
+7 -1
View File
@@ -2,7 +2,13 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.utils import non_zero_range, v_offset, v_scalar, v_series, v_talib
from pandas_ta.utils import (
non_zero_range,
v_offset,
v_scalar,
v_series,
v_talib
)
def bop(
pandas_ta/momentum/brar.py
+8 -2
View File
@@ -1,8 +1,14 @@
# -*- coding: utf-8 -*-
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import non_zero_range, v_drift, v_offset
from pandas_ta.utils import v_pos_default, v_scalar, v_series
from pandas_ta.utils import (
non_zero_range,
v_drift,
v_offset,
v_pos_default,
v_scalar,
v_series
)
def brar(
pandas_ta/momentum/cfo.py
+7 -1
View File
@@ -2,7 +2,13 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap import linreg
from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_scalar, v_series
from pandas_ta.utils import (
v_drift,
v_offset,
v_pos_default,
v_scalar,
v_series
)
def cfo(
pandas_ta/momentum/cmo.py
+9 -3
View File
@@ -3,8 +3,14 @@ from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.overlap import rma
from pandas_ta.utils import v_drift, v_offset, v_pos_default
from pandas_ta.utils import v_scalar, v_series, v_talib
from pandas_ta.utils import (
v_drift,
v_offset,
v_pos_default,
v_scalar,
v_series,
v_talib
)
@@ -42,7 +48,7 @@ def cmo(
"""
# Validate
length = v_pos_default(length, 14)
close = v_series(close, length)
close = v_series(close, length + 1)
if close is None:
return
pandas_ta/momentum/coppock.py
+3 -1
View File
@@ -1,4 +1,5 @@
# -*- coding: utf-8 -*-
# from numpy import isnan
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.overlap import wma
@@ -39,7 +40,8 @@ def coppock(
length = v_pos_default(length, 10)
fast = v_pos_default(fast, 11)
slow = v_pos_default(slow, 14)
close = v_series(close, max(length, fast, slow))
_length = length + fast + slow
close = v_series(close, _length)
if close is None:
return
pandas_ta/momentum/dm.py
+9 -2
View File
@@ -3,8 +3,15 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.maps import Imports
from pandas_ta.utils import v_drift, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_series, v_talib, zero
from pandas_ta.utils import (
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_series,
v_talib,
zero
)
def dm(
pandas_ta/momentum/er.py
+8 -2
View File
@@ -1,7 +1,13 @@
# -*- coding: utf-8 -*-
from pandas import DataFrame, concat, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import signals, v_drift, v_offset, v_pos_default, v_series
from pandas_ta.utils import (
signals,
v_drift,
v_offset,
v_pos_default,
v_series
)
def er(
@@ -34,7 +40,7 @@ def er(
"""
# Validate
length = v_pos_default(length, 10)
close = v_series(close, length)
close = v_series(close, length + 1)
if close is None:
return
pandas_ta/momentum/inertia.py
+10 -3
View File
@@ -2,8 +2,15 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap import linreg
from pandas_ta.utils import v_bool, v_drift, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_scalar, v_series
from pandas_ta.utils import (
v_bool,
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_scalar,
v_series
)
from pandas_ta.volatility import rvi
@@ -48,7 +55,7 @@ def inertia(
# Validate
length = v_pos_default(length, 20)
rvi_length = v_pos_default(rvi_length, 14)
_length = max(length, rvi_length)
_length = 2 * max(length, rvi_length) - min(length, rvi_length) // 2 - 1
close = v_series(close, _length)
if close is None:
pandas_ta/momentum/kdj.py
+8 -3
View File
@@ -1,8 +1,13 @@
# -*- coding: utf-8 -*-
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import non_zero_range, rma_pandas, v_offset
from pandas_ta.utils import v_pos_default, v_series
from pandas_ta.utils import (
non_zero_range,
rma_pandas,
v_offset,
v_pos_default,
v_series
)
def kdj(
@@ -40,7 +45,7 @@ def kdj(
# Validate
length = v_pos_default(length, 9)
signal = v_pos_default(signal, 3)
_length = max(length, signal)
_length = length + signal + 1
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
pandas_ta/momentum/kst.py
+3 -1
View File
@@ -53,7 +53,9 @@ def kst(
sma4 = int(sma4) if sma4 and sma4 > 0 else 15
signal = v_pos_default(signal, 9)
_length = max(roc1, roc2, roc3, roc4, sma1, sma2, sma3, sma4, signal)
_rmax = max(roc1, roc2, roc3, roc4)
_smax = max(sma1, sma2, sma3, sma4)
_length = _rmax + _smax
close = v_series(close, _length)
if close is None:
pandas_ta/momentum/macd.py
+9 -3
View File
@@ -3,8 +3,13 @@ from pandas import concat, DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.overlap import ema
from pandas_ta.utils import signals, v_offset, v_mamode
from pandas_ta.utils import v_pos_default, v_series, v_talib
from pandas_ta.utils import (
signals,
v_offset,
v_pos_default,
v_series,
v_talib
)
def macd(
@@ -47,7 +52,8 @@ def macd(
signal = v_pos_default(signal, 9)
if slow < fast:
fast, slow = slow, fast
close = v_series(close, slow + signal)
_length = slow + signal - 1
close = v_series(close, _length)
if close is None:
return
pandas_ta/momentum/mom.py
+1 -1
View File
@@ -33,7 +33,7 @@ def mom(
"""
# Validate
length = v_pos_default(length, 10)
close = v_series(close, length)
close = v_series(close, length + 1)
if close is None:
return
pandas_ta/momentum/pgo.py
+4 -3
View File
@@ -37,9 +37,10 @@ def pgo(
"""
# Validate
length = v_pos_default(length, 14)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
_length = 2 * length
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return
pandas_ta/momentum/ppo.py
+11 -3
View File
@@ -3,8 +3,15 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.maps import Imports
from pandas_ta.utils import tal_ma, v_mamode, v_offset, v_pos_default
from pandas_ta.utils import v_scalar, v_series, v_talib
from pandas_ta.utils import (
tal_ma,
v_mamode,
v_offset,
v_pos_default,
v_scalar,
v_series,
v_talib
)
def ppo(
@@ -43,7 +50,8 @@ def ppo(
signal = v_pos_default(signal, 9)
if slow < fast:
fast, slow = slow, fast
close = v_series(close, max(fast, slow, signal))
_length = max(fast, slow, signal)
close = v_series(close, _length)
if close is None:
return
pandas_ta/momentum/psl.py
+7 -1
View File
@@ -2,7 +2,13 @@
from numpy import sign
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_scalar, v_series
from pandas_ta.utils import (
v_drift,
v_offset,
v_pos_default,
v_scalar,
v_series
)
def psl(
pandas_ta/momentum/qqe.py
+11 -4
View File
@@ -3,8 +3,14 @@ from numpy import isnan, maximum, minimum, nan
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.utils import v_drift, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_scalar, v_series
from pandas_ta.utils import (
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_scalar,
v_series
)
from .rsi import rsi
@@ -53,7 +59,8 @@ def qqe(
length = v_pos_default(length, 14)
smooth = v_pos_default(smooth, 5)
wilders_length = 2 * length - 1
close = v_series(close, smooth + wilders_length)
_length = wilders_length + smooth
close = v_series(close, _length)
if close is None:
return
@@ -80,7 +87,7 @@ def qqe(
return # Emergency Break
dar = factor * ma("ema", smoothed_rsi_tr_ma, length=wilders_length)
if all(isnan(dar)):
return # Emergency Break
return # Emergency Break
# Create the Upper and Lower Bands around RSI MA.
upperband = rsi_ma + dar
pandas_ta/momentum/roc.py
+8 -3
View File
@@ -2,8 +2,13 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.utils import v_offset, v_pos_default, v_scalar
from pandas_ta.utils import v_series, v_talib
from pandas_ta.utils import (
v_offset,
v_pos_default,
v_scalar,
v_series,
v_talib
)
from .mom import mom
@@ -39,7 +44,7 @@ def roc(
"""
# Validate
length = v_pos_default(length, 10)
close = v_series(close, length)
close = v_series(close, length + 1)
if close is None:
return
pandas_ta/momentum/rsi.py
+10 -3
View File
@@ -3,8 +3,15 @@ from pandas import DataFrame, concat, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.overlap import rma
from pandas_ta.utils import signals, v_drift, v_offset, v_pos_default
from pandas_ta.utils import v_scalar, v_series, v_talib
from pandas_ta.utils import (
signals,
v_drift,
v_offset,
v_pos_default,
v_scalar,
v_series,
v_talib
)
def rsi(
@@ -39,7 +46,7 @@ def rsi(
"""
# Validate
length = v_pos_default(length, 14)
close = v_series(close, length)
close = v_series(close, length + 1)
if close is None:
return
pandas_ta/momentum/rsx.py
+8 -2
View File
@@ -2,7 +2,13 @@
from numpy import nan
from pandas_ta._typing import DictLike, Int
from pandas import concat, DataFrame, Series
from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_series, signals
from pandas_ta.utils import (
signals,
v_drift,
v_offset,
v_pos_default,
v_series
)
def rsx(
@@ -53,7 +59,7 @@ def rsx(
f40, f48, f50, f58, f60, f68, f70, f78 = 0, 0, 0, 0, 0, 0, 0, 0
f80, f88, f90 = 0, 0, 0
result = [nan for _ in range(0, length - 1)] + [0]
result = [nan for _ in range(0, length - 1)] + [50]
for i in range(length, m):
if f90 == 0:
f90 = 1.0
pandas_ta/momentum/rvgi.py
+1 -1
View File
@@ -39,7 +39,7 @@ def rvgi(
# Validate
length = v_pos_default(length, 14)
swma_length = v_pos_default(swma_length, 4)
_length = max(length, swma_length)
_length = length + swma_length - 1
open_ = v_series(open_, _length)
high = v_series(high, _length)
low = v_series(low, _length)
pandas_ta/momentum/slope.py
+5 -4
View File
@@ -30,9 +30,10 @@ def slope(
Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 1
as_angle (value, optional): Converts slope to an angle. Default: False
to_degrees (value, optional): Converts slope angle to degrees.
Default: False
as_angle (value, optional): Converts slope to an angle in radians
per np.arctan(). Default: False
to_degrees (value, optional): If as_angle=True, it converts the slope
angle to degrees. Default: False
offset (int): How many periods to offset the result. Default: 0
Kwargs:
@@ -44,7 +45,7 @@ def slope(
"""
# Validate
length = v_pos_default(length, 1)
close = v_series(close, length)
close = v_series(close, length + 1)
if close is None:
return
pandas_ta/momentum/smi.py
+8 -1
View File
@@ -1,4 +1,5 @@
# -*- coding: utf-8 -*-
from numpy import isnan
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import v_offset, v_pos_default, v_scalar, v_series
@@ -47,7 +48,8 @@ def smi(
signal = v_pos_default(signal, 5)
if slow < fast:
fast, slow = slow, fast
close = v_series(close, max(fast, slow, signal))
_length = slow + signal + 1
close = v_series(close, _length)
if close is None:
return
@@ -57,8 +59,13 @@ def smi(
# Calculate
tsi_df = tsi(close, fast=fast, slow=slow, signal=signal, scalar=scalar)
if tsi_df is None:
return # Emergency Break
smi = tsi_df.iloc[:, 0]
signalma = tsi_df.iloc[:, 1]
if all(isnan(signalma)):
return # Emergency Break
osc = smi - signalma
# Offset
pandas_ta/momentum/squeeze.py
+28 -6
View File
@@ -4,8 +4,15 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap import ema, linreg, sma
from pandas_ta.trend import decreasing, increasing
from pandas_ta.utils import simplify_columns, unsigned_differences, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series
from pandas_ta.utils import (
simplify_columns,
unsigned_differences,
v_bool,
v_mamode,
v_offset,
v_pos_default,
v_series
)
from pandas_ta.volatility import bbands, kc
from .mom import mom
@@ -16,6 +23,7 @@ def squeeze(
kc_length: Int = None, kc_scalar: IntFloat = None,
mom_length: Int = None, mom_smooth: Int = None,
use_tr: bool = None, mamode: str = None,
prenan: bool = None,
offset: Int = None, **kwargs: DictLike
) -> DataFrame:
"""Squeeze (SQZ)
@@ -44,6 +52,8 @@ def squeeze(
mom_length (int): Momentum Period. Default: 12
mom_smooth (int): Smoothing Period of Momentum. Default: 6
mamode (str): Only "ema" or "sma". Default: "sma"
prenan (bool): If True, sets nan for all columns up the first
valid squeeze value. Default: False
offset (int): How many periods to offset the result. Default: 0
Kwargs:
@@ -67,7 +77,7 @@ def squeeze(
kc_length = v_pos_default(kc_length, 20)
mom_length = v_pos_default(mom_length, 12)
mom_smooth = v_pos_default(mom_smooth, 6)
_length = max(bb_length, kc_length, mom_length, mom_smooth)
_length = max(bb_length, kc_length, mom_length, mom_smooth) + 1
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
@@ -78,6 +88,7 @@ def squeeze(
bb_std = v_pos_default(bb_std, 2.0)
kc_scalar = v_pos_default(kc_scalar, 1.5)
mamode = v_mamode(mamode, "sma")
prenan = v_bool(prenan, False)
offset = v_offset(offset)
use_tr = kwargs.pop("tr", True)
@@ -140,11 +151,22 @@ def squeeze(
_props += "_LB" if lazybear else ""
squeeze.name = f"SQZ{_props}"
if asint:
squeeze_on = squeeze_on.astype(int)
squeeze_off = squeeze_off.astype(int)
no_squeeze = no_squeeze.astype(int)
if prenan:
nanlength = max(bb_length, kc_length) - 2
squeeze_on[:nanlength] = nan
squeeze_off[:nanlength] = nan
no_squeeze[:nanlength] = nan
data = {
squeeze.name: squeeze,
f"SQZ_ON": squeeze_on.astype(int) if asint else squeeze_on,
f"SQZ_OFF": squeeze_off.astype(int) if asint else squeeze_off,
f"SQZ_NO": no_squeeze.astype(int) if asint else no_squeeze,
f"SQZ_ON": squeeze_on,
f"SQZ_OFF": squeeze_off,
f"SQZ_NO": no_squeeze,
}
df = DataFrame(data)
df.name = squeeze.name
pandas_ta/momentum/squeeze_pro.py
+36 -9
View File
@@ -4,11 +4,19 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.momentum import mom
# from pandas_ta.overlap import ema, sma
from pandas_ta.trend import decreasing, increasing
from pandas_ta.utils import (
simplify_columns,
unsigned_differences,
v_bool,
v_mamode,
v_offset,
v_pos_default,
v_scalar,
v_series
)
from pandas_ta.volatility import bbands, kc
from pandas_ta.utils import simplify_columns, unsigned_differences, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_scalar, v_series
def squeeze_pro(
high: Series, low: Series, close: Series,
@@ -17,6 +25,7 @@ def squeeze_pro(
kc_scalar_normal: IntFloat = None, kc_scalar_narrow: IntFloat = None,
mom_length: Int = None, mom_smooth: Int = None,
use_tr: bool = None, mamode: str = None,
prenan: bool = None,
offset: Int = None, **kwargs: DictLike
) -> DataFrame:
"""Squeeze PRO(SQZPRO)
@@ -50,6 +59,8 @@ def squeeze_pro(
mom_length (int): Momentum Period. Default: 12
mom_smooth (int): Smoothing Period of Momentum. Default: 6
mamode (str): Only "ema" or "sma". Default: "sma"
prenan (bool): If True, sets nan for all columns up the first
valid squeeze value. Default: False
offset (int): How many periods to offset the result. Default: 0
Kwargs:
@@ -72,7 +83,7 @@ def squeeze_pro(
kc_length = v_pos_default(kc_length, 20)
mom_length = v_pos_default(mom_length, 12)
mom_smooth = v_pos_default(mom_smooth, 6)
_length = max(bb_length, kc_length, mom_length, mom_smooth)
_length = max(bb_length, kc_length, mom_length, mom_smooth) + 1
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
@@ -83,6 +94,7 @@ def squeeze_pro(
kc_scalar_narrow = v_scalar(kc_scalar_narrow, 1)
kc_scalar_normal = v_scalar(kc_scalar_normal, 1.5)
kc_scalar_wide = v_scalar(kc_scalar_wide, 2)
prenan = v_bool(prenan, False)
valid_kc_scaler = kc_scalar_wide > kc_scalar_normal \
and kc_scalar_normal > kc_scalar_narrow
@@ -157,13 +169,28 @@ def squeeze_pro(
_props += f"_{bb_length}_{bb_std}_{kc_length}_{kc_scalar_wide}_{kc_scalar_normal}_{kc_scalar_narrow}"
squeeze.name = f"SQZPRO{_props}"
if asint:
squeeze_on_wide = squeeze_on_wide.astype(int)
squeeze_on_narrow = squeeze_on_narrow.astype(int)
squeeze_on_normal = squeeze_on_normal.astype(int)
squeeze_off_wide = squeeze_off_wide.astype(int)
no_squeeze = no_squeeze.astype(int)
if prenan:
nanlength = max(bb_length, kc_length) - 2
squeeze_on_wide[:nanlength] = nan
squeeze_on_narrow[:nanlength] = nan
squeeze_on_normal[:nanlength] = nan
squeeze_off_wide[:nanlength] = nan
no_squeeze[:nanlength] = nan
data = {
squeeze.name: squeeze,
f"SQZPRO_ON_WIDE": squeeze_on_wide.astype(int) if asint else squeeze_on_wide,
f"SQZPRO_ON_NORMAL": squeeze_on_normal.astype(int) if asint else squeeze_on_normal,
f"SQZPRO_ON_NARROW": squeeze_on_narrow.astype(int) if asint else squeeze_on_narrow,
f"SQZPRO_OFF": squeeze_off_wide.astype(int) if asint else squeeze_off_wide,
f"SQZPRO_NO": no_squeeze.astype(int) if asint else no_squeeze,
f"SQZPRO_ON_WIDE": squeeze_on_wide,
f"SQZPRO_ON_NORMAL": squeeze_on_normal,
f"SQZPRO_ON_NARROW": squeeze_on_narrow,
f"SQZPRO_OFF": squeeze_off_wide,
f"SQZPRO_NO": no_squeeze,
}
df = DataFrame(data)
df.name = squeeze.name
pandas_ta/momentum/stc.py
+16 -3
View File
@@ -1,9 +1,14 @@
# -*- coding: utf-8 -*-
from numpy import nan
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap import ema
from pandas_ta.utils import non_zero_range, v_offset
from pandas_ta.utils import v_pos_default, v_series
from pandas_ta.utils import (
non_zero_range,
v_offset,
v_pos_default,
v_series
)
def stc(
@@ -106,10 +111,14 @@ def stc(
xmacd = fastma - slowma
pff, pf = schaff_tc(close, xmacd, tclength, factor)
pf[:_length - 1] = nan
stc = Series(pff, index=close.index)
macd = Series(xmacd, index=close.index)
stoch = Series(pf, index=close.index)
stc.iloc[:_length - 1] = nan
# Offset
if offset != 0:
stc = stc.shift(offset)
@@ -133,7 +142,11 @@ def stc(
stoch.name = f"STCstoch{_props}"
stc.category = macd.category = stoch.category = "momentum"
data = {stc.name: stc, macd.name: macd, stoch.name: stoch}
data = {
stc.name: stc,
macd.name: macd,
stoch.name: stoch
}
df = DataFrame(data)
df.name = f"STC{_props}"
df.category = stc.category
pandas_ta/momentum/stoch.py
+9 -2
View File
@@ -3,8 +3,15 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.maps import Imports
from pandas_ta.utils import non_zero_range, tal_ma, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
from pandas_ta.utils import (
non_zero_range,
tal_ma,
v_mamode,
v_offset,
v_pos_default,
v_series,
v_talib
)
def stoch(
pandas_ta/momentum/stochf.py
+10 -3
View File
@@ -3,8 +3,15 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.maps import Imports
from pandas_ta.utils import non_zero_range, tal_ma, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
from pandas_ta.utils import (
non_zero_range,
tal_ma,
v_mamode,
v_offset,
v_pos_default,
v_series,
v_talib
)
def stochf(
@@ -44,7 +51,7 @@ def stochf(
# Validate
k = v_pos_default(k, 14)
d = v_pos_default(d, 3)
_length = max(k, d)
_length = k + d - 1
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
pandas_ta/momentum/stochrsi.py
+9 -3
View File
@@ -3,8 +3,13 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.momentum import rsi
from pandas_ta.utils import non_zero_range, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series
from pandas_ta.utils import (
non_zero_range,
v_mamode,
v_offset,
v_pos_default,
v_series
)
def stochrsi(
@@ -49,7 +54,8 @@ def stochrsi(
rsi_length = v_pos_default(rsi_length, 14)
k = v_pos_default(k, 3)
d = v_pos_default(d, 3)
close = v_series(close, length + rsi_length + k + d)
_length = length + rsi_length + 2
close = v_series(close, _length)
if close is None:
return
pandas_ta/momentum/td_seq.py
+1 -1
View File
@@ -32,7 +32,7 @@ def td_seq(
pd.DataFrame: New feature generated.
"""
# Validate
close = v_series(close)
close = v_series(close, 5)
if close is None:
return
pandas_ta/momentum/trix.py
+18 -7
View File
@@ -1,9 +1,15 @@
# -*- coding: utf-8 -*-
from numpy import isnan
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap.ema import ema
from pandas_ta.utils import v_drift, v_offset, v_pos_default
from pandas_ta.utils import v_scalar, v_series
from pandas_ta.utils import (
v_drift,
v_offset,
v_pos_default,
v_scalar,
v_series
)
def trix(
@@ -35,26 +41,31 @@ def trix(
"""
# Validate
length = v_pos_default(length, 30)
_length = 3 * length - 2
signal = v_pos_default(signal, 9)
if length < signal:
length, signal = signal, length
_length = 3 * length - 1
close = v_series(close, _length)
if close is None:
return
signal = v_pos_default(signal, 9)
scalar = v_scalar(scalar, 100)
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
ema1 = ema(close=close, length=length, **kwargs)
# if all(isnan(ema1)): return # Emergency Break
if all(isnan(ema1)):
return # Emergency Break
ema2 = ema(close=ema1, length=length, **kwargs)
# if all(isnan(ema2)): return # Emergency Break
if all(isnan(ema2)):
return # Emergency Break
ema3 = ema(close=ema2, length=length, **kwargs)
# if all(isnan(ema3)): return # Emergency Break
if all(isnan(ema3)):
return # Emergency Break
trix = scalar * ema3.pct_change(drift)
trix_signal = trix.rolling(signal).mean()
pandas_ta/momentum/tsi.py
+21 -4
View File
@@ -1,10 +1,17 @@
# -*- coding: utf-8 -*-
from numpy import isnan
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.overlap import ema
from pandas_ta.utils import v_drift, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_scalar, v_series
from pandas_ta.utils import (
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_scalar,
v_series
)
def tsi(
@@ -43,14 +50,18 @@ def tsi(
# Validate
fast = v_pos_default(fast, 13)
slow = v_pos_default(slow, 25)
close = v_series(close, max(fast, slow))
signal = v_pos_default(signal, 13)
if slow < fast:
fast, slow = slow, fast
_length = slow + signal + 1
close = v_series(close, _length)
if "length" in kwargs:
kwargs.pop("length")
if close is None:
return
signal = v_pos_default(signal, 13)
scalar = v_scalar(scalar, 100)
mamode = v_mamode(mamode, "ema")
drift = v_drift(drift)
@@ -59,13 +70,19 @@ def tsi(
# Calculate
diff = close.diff(drift)
slow_ema = ema(close=diff, length=slow, **kwargs)
if all(isnan(slow_ema)):
return # Emergency Break
fast_slow_ema = ema(close=slow_ema, length=fast, **kwargs)
abs_diff = diff.abs()
abs_slow_ema = ema(close=abs_diff, length=slow, **kwargs)
if all(isnan(abs_slow_ema)):
return # Emergency Break
abs_fast_slow_ema = ema(close=abs_slow_ema, length=fast, **kwargs)
tsi = scalar * fast_slow_ema / abs_fast_slow_ema
if all(isnan(tsi)):
return # Emergency Break
tsi_signal = ma(mamode, tsi, length=signal)
# Offset
pandas_ta/momentum/uo.py
+8 -2
View File
@@ -2,7 +2,13 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_series, v_talib
from pandas_ta.utils import (
v_drift,
v_offset,
v_pos_default,
v_series,
v_talib
)
def uo(
@@ -46,7 +52,7 @@ def uo(
fast = v_pos_default(fast, 7)
medium = v_pos_default(medium, 14)
slow = v_pos_default(slow, 28)
_length = max(fast, medium, slow)
_length = max(fast, medium, slow) + 1
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
pandas_ta/overlap/alma.py
+3 -2
View File
@@ -1,6 +1,6 @@
# -*- coding: utf-8 -*-
from numpy import append, arange, array, exp, floor, nan, tensordot
from numpy.version import version as npVersion
from numpy.version import version as np_version
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas import Series
from pandas_ta.utils import strided_window, v_offset, v_pos_default, v_series
@@ -46,6 +46,7 @@ def alma(
return
sigma = v_pos_default(sigma, 6.0)
if isinstance(dist_offset, float) and 0 <= dist_offset <= 1:
offset_ = float(dist_offset)
else:
@@ -60,7 +61,7 @@ def alma(
weights = exp(-0.5 * ((sigma / length) * (x - k)) ** 2)
weights /= weights.sum()
if npVersion >= "1.20.0":
if np_version >= "1.20.0":
from numpy.lib.stride_tricks import sliding_window_view
window = sliding_window_view(np_close, length)
else:
pandas_ta/overlap/ema.py
+8 -2
View File
@@ -3,7 +3,13 @@ from numpy import nan
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series, v_talib
from pandas_ta.utils import (
v_bool,
v_offset,
v_pos_default,
v_series,
v_talib
)
try:
from numba import njit
@@ -71,7 +77,7 @@ def ema(
adjust = kwargs.setdefault("adjust", False)
# Calculate
if Imports["talib"] and mode_tal:
if Imports["talib"] and mode_tal and length > 1:
from talib import EMA
ema = EMA(close, length)
else:
pandas_ta/overlap/fwma.py
+8 -2
View File
@@ -1,8 +1,14 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import fibonacci, v_ascending, v_offset
from pandas_ta.utils import v_pos_default, v_series, weights
from pandas_ta.utils import (
fibonacci,
v_ascending,
v_offset,
v_pos_default,
v_series,
weights
)
def fwma(
pandas_ta/overlap/hilo.py
+1 -1
View File
@@ -49,7 +49,7 @@ def hilo(
# Validate
high_length = v_pos_default(high_length, 13)
low_length = v_pos_default(low_length, 21)
_length = max(high_length, low_length)
_length = max(high_length, low_length) + 1
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
pandas_ta/overlap/hma.py
+1 -1
View File
@@ -32,7 +32,7 @@ def hma(
"""
# Validate
length = v_pos_default(length, 10)
close = v_series(close, length)
close = v_series(close, length + 2)
if close is None:
return
pandas_ta/overlap/kama.py
+8 -2
View File
@@ -3,8 +3,14 @@ from numpy import nan
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.utils import non_zero_range, v_drift, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series
from pandas_ta.utils import (
non_zero_range,
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_series
)
def kama(
pandas_ta/overlap/linreg.py
+10 -6
View File
@@ -1,12 +1,16 @@
# -*- coding: utf-8 -*-
from numpy import arctan, nan, pi, zeros_like
from numpy.version import version
from numpy.version import version as np_version
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import strided_window, v_offset, v_pos_default
from pandas_ta.utils import v_series, v_talib
from pandas_ta.utils import (
strided_window,
v_offset,
v_pos_default,
v_series,
v_talib
)
def linreg(
close: Series, length: Int = None, talib: bool = None,
@@ -111,7 +115,7 @@ def linreg(
return m * length + b if not tsf else m * (length - 1) + b
if version >= "1.20.0":
if np_version >= "1.20.0":
from numpy.lib.stride_tricks import sliding_window_view
linreg_ = [
linear_regression(_) for _ in sliding_window_view(
@@ -137,7 +141,7 @@ def linreg(
linreg.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Category
linreg.name = f"LR"
linreg.name = f"LINREG"
if slope:
linreg.name += "m"
if intercept:
pandas_ta/overlap/mama.py
+6 -2
View File
@@ -1,5 +1,5 @@
# -*- coding: utf-8 -*-
from numpy import arctan, nan, zeros_like
from numpy import arctan, isnan, nan, zeros_like
from pandas import DataFrame, Series
from pandas_ta._typing import Array, DictLike, Int, IntFloat
from pandas_ta.maps import Imports
@@ -14,7 +14,8 @@ except ImportError:
@njit
def np_mama(
x: Array, fastlimit: IntFloat, slowlimit: IntFloat, prenan: Int
x: Array, fastlimit: IntFloat, slowlimit: IntFloat,
prenan: Int
):
"""Ehler's Mother of Adaptive Moving Averages
http://traders.com/documentation/feedbk_docs/2014/01/traderstips.html
@@ -155,6 +156,9 @@ def mama(
else:
mama, fama = np_mama(np_close, fastlimit, slowlimit, prenan)
if all(isnan(mama)) or all(isnan(fama)):
return # Emergency Break
# Name and Category
_props = f"_{fastlimit}_{slowlimit}"
df = DataFrame({
pandas_ta/overlap/pwma.py
+9 -2
View File
@@ -1,8 +1,15 @@
# -*- coding: utf-8 -*-
# from numpy.version import version as np_version
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import pascals_triangle, v_offset
from pandas_ta.utils import v_ascending, v_pos_default, v_series, weights
from pandas_ta.utils import (
pascals_triangle,
v_offset,
v_ascending,
v_pos_default,
v_series,
weights
)
def pwma(
pandas_ta/overlap/sma.py
+7 -2
View File
@@ -3,8 +3,13 @@ from numpy import convolve, ndarray, ones
from pandas import Series
from pandas_ta._typing import Array, DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import np_prepend, v_offset, v_pos_default
from pandas_ta.utils import v_series, v_talib
from pandas_ta.utils import (
np_prepend,
v_offset,
v_pos_default,
v_series,
v_talib
)
try:
pandas_ta/overlap/smma.py
+7 -2
View File
@@ -3,8 +3,13 @@ from numpy import nan
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.utils import v_mamode, v_offset, v_pos_default
from pandas_ta.utils import v_series, v_talib
from pandas_ta.utils import (
v_mamode,
v_offset,
v_pos_default,
v_series,
v_talib
)
def smma(
pandas_ta/overlap/supertrend.py
+3 -3
View File
@@ -40,9 +40,9 @@ def supertrend(
"""
# Validate
length = v_pos_default(length, 7)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
high = v_series(high, length + 1)
low = v_series(low, length + 1)
close = v_series(close, length + 1)
if high is None or low is None or close is None:
return
pandas_ta/overlap/swma.py
+7 -2
View File
@@ -1,8 +1,13 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import symmetric_triangle, v_offset, v_pos_default
from pandas_ta.utils import v_series, weights
from pandas_ta.utils import (
symmetric_triangle,
v_offset,
v_pos_default,
v_series,
weights
)
def swma(
pandas_ta/overlap/t3.py
+2 -1
View File
@@ -1,4 +1,5 @@
# -*- coding: utf-8 -*-
from numpy import isnan
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
@@ -37,7 +38,7 @@ def t3(
"""
# Validate
length = v_pos_default(length, 10)
close = v_series(close, length)
close = v_series(close, 5 * (length + 1))
if close is None:
return
pandas_ta/overlap/tema.py
+1 -1
View File
@@ -35,7 +35,7 @@ def tema(
"""
# Validate
length = v_pos_default(length, 10)
close = v_series(close, length)
close = v_series(close, 3 * length)
if close is None:
return
pandas_ta/overlap/vidya.py
+1 -1
View File
@@ -41,7 +41,7 @@ def vidya(
"""
# Validate
length = v_pos_default(length, 14)
close = v_series(close, length)
close = v_series(close, length + 1)
if close is None:
return
pandas_ta/overlap/wma.py
+7 -2
View File
@@ -3,8 +3,13 @@ from numpy import arange, dot
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import v_ascending, v_offset, v_pos_default
from pandas_ta.utils import v_series, v_talib
from pandas_ta.utils import (
v_ascending,
v_offset,
v_pos_default,
v_series,
v_talib
)
def wma(
pandas_ta/overlap/zlma.py
+4
View File
@@ -1,7 +1,9 @@
# -*- coding: utf-8 -*-
from numpy import isnan
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series
from .dema import dema
from .ema import ema
from .fwma import fwma
@@ -104,6 +106,8 @@ def zlma(
kwargs.update({"length": length})
zlma = _ma(mamode, **kwargs)
if zlma is None or all(isnan(zlma)):
return # Emergency Break
# Offset
if offset != 0:
pandas_ta/performance/log_return.py
+1 -1
View File
@@ -33,7 +33,7 @@ def log_return(
"""
# Validate
length = v_pos_default(length, 1)
close = v_series(close, length)
close = v_series(close, length + 1)
if close is None:
return
pandas_ta/performance/percent_return.py
+3 -2
View File
@@ -20,7 +20,8 @@ def percent_return(
Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 20
cumulative (bool): If True, returns the cumulative returns. Default: False
cumulative (bool): If True, returns the cumulative returns.
Default: False
offset (int): How many periods to offset the result. Default: 0
Kwargs:
@@ -32,7 +33,7 @@ def percent_return(
"""
# Validate
length = v_pos_default(length, 1)
close = v_series(close, length)
close = v_series(close, length + 1)
if close is None:
return
pandas_ta/statistics/entropy.py
+1 -1
View File
@@ -33,7 +33,7 @@ def entropy(
"""
# Validate
length = v_pos_default(length, 10)
close = v_series(close, length)
close = v_series(close, 2 * length - 1)
if close is None:
return
pandas_ta/statistics/median.py
+1 -1
View File
@@ -1,6 +1,6 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import v_offset, v_pos_default, v_series
pandas_ta/statistics/tos_stdevall.py
+1 -1
View File
@@ -46,7 +46,7 @@ def tos_stdevall(
close = close.iloc[-length:]
_props = f"{_props}_{length}"
close = v_series(close, length)
close = v_series(close, 2)
if close is None:
return
pandas_ta/transform/ifisher.py
+3 -1
View File
@@ -1,5 +1,5 @@
# -*- coding: utf-8 -*-
from numpy import exp, logical_and, max, min
from numpy import exp, isnan, logical_and, max, min
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import v_int, v_offset, v_scalar, v_series
@@ -60,6 +60,8 @@ def ifisher(
if not all(is_remapped):
np_max, np_min = max(np_close), min(np_close)
close_map = remap(close, from_min=np_min, from_max=np_max, to_min=-1, to_max=1)
if close_map is None or all(isnan(close_map.values)):
return # Emergency Break
np_close = close_map.values
amped = exp(amp * np_close)
result = (amped - 1) / (amped + 1)
pandas_ta/trend/adx.py
+16 -3
View File
@@ -1,9 +1,17 @@
# -*- coding: utf-8 -*-
from numpy import isnan
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.utils import v_drift, v_mamode, v_offset, v_pos_default
from pandas_ta.utils import v_scalar, v_series, zero
from pandas_ta.utils import (
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_scalar,
v_series,
zero
)
from pandas_ta.volatility import atr
@@ -58,7 +66,12 @@ def adx(
offset = v_offset(offset)
# Calculate
atr_ = atr(high=high, low=low, close=close, length=length)
atr_ = atr(
high=high, low=low, close=close,
length=length, prenan=kwargs.pop("prenan", True)
)
if atr_ is None or all(isnan(atr_)):
return
up = high - high.shift(drift) # high.diff(drift)
dn = low.shift(drift) - low # low.diff(-drift).shift(drift)
pandas_ta/trend/amat.py
+1 -1
View File
@@ -1,6 +1,6 @@
# -*- coding: utf-8 -*-
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series
from .long_run import long_run
pandas_ta/trend/aroon.py
+11 -5
View File
@@ -2,9 +2,15 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.utils import v_offset, v_pos_default
from pandas_ta.utils import v_scalar, v_series, v_talib
from pandas_ta.utils import recent_maximum_index, recent_minimum_index
from pandas_ta.utils import (
recent_maximum_index,
recent_minimum_index,
v_offset,
v_pos_default,
v_scalar,
v_series,
v_talib
)
def aroon(
@@ -37,8 +43,8 @@ def aroon(
"""
# Validate
length = v_pos_default(length, 14)
high = v_series(high, length)
low = v_series(low, length)
high = v_series(high, length + 1)
low = v_series(low, length + 1)
if high is None or low is None:
return
pandas_ta/trend/chop.py
+11 -5
View File
@@ -2,8 +2,14 @@
from numpy import log, log10
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import v_bool, v_drift, v_offset
from pandas_ta.utils import v_pos_default, v_scalar, v_series
from pandas_ta.utils import (
v_bool,
v_drift,
v_offset,
v_pos_default,
v_scalar,
v_series,
)
from pandas_ta.volatility import atr
@@ -45,9 +51,9 @@ def chop(
"""
# Validate
length = v_pos_default(length, 14)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
high = v_series(high, length + 1)
low = v_series(low, length + 1)
close = v_series(close, length + 1)
if high is None or low is None or close is None:
return
pandas_ta/trend/cksp.py
+11 -3
View File
@@ -1,8 +1,14 @@
# -*- coding: utf-8 -*-
from numpy import isnan
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import v_mamode, v_offset, v_pos_default
from pandas_ta.utils import v_series, v_tradingview
from pandas_ta.utils import (
v_mamode,
v_offset,
v_pos_default,
v_series,
v_tradingview
)
from pandas_ta.volatility import atr
@@ -53,7 +59,7 @@ def cksp(
# TODO: clean up x and q
x = float(x) if isinstance(x, float) and x > 0 else 1 if tvmode is True else 3
q = int(q) if isinstance(q, float) and q > 0 else 9 if tvmode is True else 20
_length = max(p, q, x)
_length = p + q
high = v_series(high, _length)
low = v_series(low, _length)
@@ -67,6 +73,8 @@ def cksp(
# Calculate
atr_ = atr(high=high, low=low, close=close, length=p, mamode=mamode)
if atr_ is None or all(isnan(atr_)):
return
long_stop_ = high.rolling(p).max() - x * atr_
long_stop = long_stop_.rolling(q).max()
pandas_ta/trend/decreasing.py
+8 -2
View File
@@ -1,8 +1,14 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import is_percent, v_bool, v_drift, v_offset
from pandas_ta.utils import v_pos_default, v_series
from pandas_ta.utils import (
is_percent,
v_bool,
v_drift,
v_offset,
v_pos_default,
v_series
)
def decreasing(
pandas_ta/trend/dpo.py
+1 -1
View File
@@ -35,7 +35,7 @@ def dpo(
"""
# Validate
length = v_pos_default(length, 20)
close = v_series(close, length)
close = v_series(close, length + 1)
if close is None:
return
pandas_ta/trend/increasing.py
+9 -2
View File
@@ -1,8 +1,15 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import is_percent, v_bool, v_drift
from pandas_ta.utils import v_offset, v_pos_default, v_series
from pandas_ta.utils import (
is_percent,
v_bool,
v_drift,
v_offset,
v_pos_default,
v_series
)
def increasing(
close: Series, length: Int = None, strict: bool = None,
pandas_ta/trend/qstick.py
+7 -2
View File
@@ -2,8 +2,13 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.utils import non_zero_range, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_series
from pandas_ta.utils import (
non_zero_range,
v_mamode,
v_offset,
v_pos_default,
v_series
)
def qstick(
pandas_ta/trend/trendflex.py
+3 -2
View File
@@ -13,7 +13,8 @@ except ImportError:
@njit
def np_trendflex(
x: Array, n: Int, k: Int, alpha: IntFloat, pi: IntFloat, sqrt2: IntFloat
x: Array, n: Int, k: Int,
alpha: IntFloat, pi: IntFloat, sqrt2: IntFloat
):
"""Ehler's Trendflex
http://traders.com/Documentation/FEEDbk_docs/2020/02/TradersTips.html"""
@@ -88,7 +89,7 @@ def trendflex(
# Validate
length = v_pos_default(length, 20)
smooth = v_pos_default(smooth, 20)
close = v_series(close, max(length, smooth))
close = v_series(close, max(length, smooth) + 1)
if close is None:
return
pandas_ta/trend/vhf.py
+7 -2
View File
@@ -2,8 +2,13 @@
from numpy import inf, fabs, nan
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import non_zero_range, v_drift, v_offset
from pandas_ta.utils import v_pos_default, v_series
from pandas_ta.utils import (
non_zero_range,
v_drift,
v_offset,
v_pos_default,
v_series
)
def vhf(
pandas_ta/utils/_math.py
+9 -1
View File
@@ -8,7 +8,15 @@ from numpy import all, append, array, corrcoef, dot, exp, fabs
from numpy import log, nan, ndarray, ones, seterr, sign, sqrt, sum, triu
from pandas import DataFrame, Series
from pandas_ta._typing import Array, DictLike, Float, Int, IntFloat, List, Optional
from pandas_ta._typing import (
Array,
DictLike,
Float,
Int,
IntFloat,
List,
Optional
)
from pandas_ta.maps import Imports
from pandas_ta.utils._validate import v_series
pandas_ta/utils/_validate.py
+12 -6
View File
@@ -3,7 +3,13 @@ from functools import partial
from pandas import DataFrame, Series
from pandas.api.types import is_datetime64_any_dtype
from pandas_ta._typing import (
Float, Int, IntFloat, List, MaybeSeriesFrame, Optional, SeriesFrame
Float,
Int,
IntFloat,
List,
MaybeSeriesFrame,
Optional,
SeriesFrame
)
@@ -99,7 +105,7 @@ def v_offset(var: Int) -> Int:
def v_pos_default(
var: IntFloat, default: IntFloat = 0, strict: bool = True, complement: bool = False
) -> IntFloat:
return partial(v_lowerbound, bound=0)\
return partial(v_lowerbound, bound=0) \
(var=var, default=default, strict=strict, complement=complement)
def v_scalar(var: IntFloat, default: Optional[IntFloat] = 1) -> Float:
@@ -111,10 +117,10 @@ def v_scalar(var: IntFloat, default: Optional[IntFloat] = 1) -> Float:
def v_series(series: Series, length: Optional[IntFloat] = 0) -> Optional[Series]:
"""Returns None if the Pandas Series does not meet the minimum length
required for the indicator."""
if isinstance(series, Series) and series.empty and series.size >= length:
print("[X] Requires a Pandas Series or DataFrame.")
return None
return series
if series is not None and isinstance(series, Series):
if series.size >= v_pos_default(length, 0):
return series
return None
def v_talib(var: bool) -> bool:
"""Returns True by default"""
pandas_ta/volatility/aberration.py
+1 -1
View File
@@ -49,7 +49,7 @@ def aberration(
# Validate
length = v_pos_default(length, 5)
atr_length = v_pos_default(atr_length, 15)
_length = max(atr_length, length)
_length = max(atr_length, length) + 1
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
pandas_ta/volatility/accbands.py
+8 -2
View File
@@ -2,8 +2,14 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.utils import non_zero_range, v_drift, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series
from pandas_ta.utils import (
non_zero_range,
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_series
)
def accbands(
pandas_ta/volatility/atr.py
+9 -6
View File
@@ -53,9 +53,10 @@ def atr(
"""
# Validate
length = v_pos_default(length, 14)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
_length = length + 1
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return
@@ -75,9 +76,11 @@ def atr(
high=high, low=low, close=close,
talib=mode_tal, prenan=prenan, drift=drift
)
sma_nth = tr[0:length].mean()
tr[:length - 1] = nan
tr.iloc[length - 1] = sma_nth
presma = kwargs.pop("presma", True)
if presma:
sma_nth = tr[0:length].mean()
tr[:length - 1] = nan
tr.iloc[length - 1] = sma_nth
atr = ma(mamode, tr, length=length, talib=mode_tal)
percent = kwargs.pop("percent", False)
pandas_ta/volatility/atrts.py
+13 -7
View File
@@ -2,10 +2,16 @@
from numpy import nan, uintc, zeros_like
from pandas import Series
from pandas_ta._typing import Array, DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.ma import ma as _ma
from pandas_ta.maps import Imports
from pandas_ta.utils import v_drift, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_series, v_talib
from pandas_ta.utils import (
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_series,
v_talib
)
from pandas_ta.volatility import atr
@@ -16,7 +22,7 @@ except ImportError:
@njit
def np_atrts(x: Array, ma_: Array, atr_: Array, length: Int, ma_length: Int):
def np_atrts(x: Array, ma: Array, atr_: Array, length: Int, ma_length: Int):
m = x.size
k = max(length, ma_length)
@@ -24,7 +30,7 @@ def np_atrts(x: Array, ma_: Array, atr_: Array, length: Int, ma_length: Int):
up = zeros_like(x, dtype=uintc)
dn = zeros_like(x, dtype=uintc)
expn = x > ma_
expn = x > ma
up[expn], dn[~expn] = 1, 1
up[:k], dn[:k] = 0, 0
result[:k] = nan
@@ -90,7 +96,7 @@ def atrts(
# Validate
length = v_pos_default(length, 14)
ma_length = v_pos_default(ma_length, 20)
_length = max(length, ma_length)
_length = length + ma_length
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
@@ -116,7 +122,7 @@ def atrts(
)
atr_ *= multiplier
ma_ = ma(mamode, close, length=ma_length, talib=mode_tal)
ma_ = _ma(mamode, close, length=ma_length, talib=mode_tal)
np_close, np_ma, np_atr = close.values, ma_.values, atr_.values
np_atrts_, _, _ = np_atrts(np_close, np_ma, np_atr, length, ma_length)
pandas_ta/volatility/bbands.py
+9 -2
View File
@@ -4,8 +4,15 @@ from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.maps import Imports
from pandas_ta.statistics import stdev
from pandas_ta.utils import non_zero_range, tal_ma, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
from pandas_ta.utils import (
non_zero_range,
tal_ma,
v_mamode,
v_offset,
v_pos_default,
v_series,
v_talib
)
def bbands(
pandas_ta/volatility/kc.py
+12 -5
View File
@@ -2,8 +2,14 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.utils import high_low_range, v_bool, v_offset
from pandas_ta.utils import v_mamode, v_pos_default, v_series
from pandas_ta.utils import (
high_low_range,
v_bool,
v_mamode,
v_offset,
v_pos_default,
v_series
)
from .true_range import true_range
@@ -42,9 +48,10 @@ def kc(
"""
# Validate
length = v_pos_default(length, 20)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
_length = length + 1
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return
pandas_ta/volatility/massi.py
+8 -1
View File
@@ -1,4 +1,5 @@
# -*- coding: utf-8 -*-
from numpy import isnan
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.overlap import ema
@@ -37,7 +38,7 @@ def massi(
slow = v_pos_default(slow, 25)
if slow < fast:
fast, slow = slow, fast
_length = max(fast, slow)
_length = 2 * max(fast, slow) - min(fast, slow)
high = v_series(high, _length)
low = v_series(low, _length)
@@ -51,10 +52,16 @@ def massi(
# Calculate
high_low_range = non_zero_range(high, low)
hl_ema1 = ema(close=high_low_range, length=fast, **kwargs)
if all(isnan(hl_ema1)):
return # Emergency Break
hl_ema2 = ema(close=hl_ema1, length=fast, **kwargs)
if all(isnan(hl_ema2)):
return # Emergency Break
hl_ratio = hl_ema1 / hl_ema2
massi = hl_ratio.rolling(slow, min_periods=slow).sum()
if all(isnan(massi)):
return # Emergency Break
# Offset
if offset != 0:
pandas_ta/volatility/natr.py
+13 -5
View File
@@ -2,8 +2,15 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.utils import v_drift, v_mamode, v_offset, v_pos_default
from pandas_ta.utils import v_scalar, v_series, v_talib
from pandas_ta.utils import (
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_scalar,
v_series,
v_talib
)
from pandas_ta.volatility import atr
@@ -40,9 +47,10 @@ def natr(
"""
# Validate
length = v_pos_default(length, 14)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
_length = length + 1
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return
pandas_ta/volatility/pdist.py
+8 -1
View File
@@ -1,4 +1,5 @@
# -*- coding: utf-8 -*-
from numpy import isnan
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import non_zero_range, v_drift, v_offset, v_series
@@ -32,18 +33,24 @@ def pdist(
pd.Series: New feature generated.
"""
# Validate
drift = v_drift(drift)
open_ = v_series(open_)
high = v_series(high)
low = v_series(low)
close = v_series(close)
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
pdist = 2 * non_zero_range(high, low)
if all(isnan(pdist)):
return # Emergency Break
pdist += non_zero_range(open_, close.shift(drift)).abs()
pdist -= non_zero_range(close, open_).abs()
if all(isnan(pdist)):
return # Emergency Break
# Offset
if offset != 0:
pdist = pdist.shift(offset)
pandas_ta/volatility/rvi.py
+14 -3
View File
@@ -1,10 +1,18 @@
# -*- coding: utf-8 -*-
from numpy import isnan
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.statistics import stdev
from pandas_ta.utils import unsigned_differences, v_bool, v_drift
from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series
from pandas_ta.utils import (
unsigned_differences,
v_bool,
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_series
)
def _rvi(source, length, scalar, mode, drift):
@@ -62,7 +70,7 @@ def rvi(
"""
# Validate
length = v_pos_default(length, 14)
close = v_series(close, length)
close = v_series(close, length + 2)
if close is None:
return
@@ -94,6 +102,9 @@ def rvi(
else:
rvi = _rvi(close, length, scalar, mamode, drift)
if all(isnan(rvi)):
return # Emergency Break
# Offset
if offset != 0:
rvi = rvi.shift(offset)
pandas_ta/volatility/thermo.py
+10 -4
View File
@@ -2,8 +2,14 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.utils import v_bool, v_drift, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series
from pandas_ta.utils import (
v_bool,
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_series
)
def thermo(
@@ -40,8 +46,8 @@ def thermo(
"""
# Validate
length = v_pos_default(length, 20)
high = v_series(high, length)
low = v_series(low, length)
high = v_series(high, length + 1)
low = v_series(low, length + 1)
if high is None or low is None:
return
pandas_ta/volatility/true_range.py
+14 -4
View File
@@ -1,10 +1,16 @@
# -*- coding: utf-8 -*-
from numpy import nan
from numpy import isnan, nan
from pandas import concat, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import non_zero_range, v_bool, v_drift
from pandas_ta.utils import v_offset, v_series, v_talib
from pandas_ta.utils import (
non_zero_range,
v_bool,
v_drift,
v_offset,
v_series,
v_talib
)
def true_range(
@@ -39,12 +45,13 @@ def true_range(
pd.Series: New feature
"""
# Validate
drift = v_drift(drift)
high = v_series(high)
low = v_series(low)
close = v_series(close)
mode_tal = v_talib(talib)
prenan = v_bool(prenan, False)
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
@@ -60,6 +67,9 @@ def true_range(
if prenan:
true_range.iloc[:drift] = nan
if all(isnan(true_range)):
return # Emergency Break
# Offset
if offset != 0:
true_range = true_range.shift(offset)
pandas_ta/volatility/ui.py
+4 -3
View File
@@ -40,7 +40,7 @@ def ui(
# Validate
length = v_pos_default(length, 14)
scalar = v_pos_default(scalar, 100)
close = v_series(close, length)
close = v_series(close, 2 * length - 1)
if close is None:
return
@@ -56,9 +56,10 @@ def ui(
everget = kwargs.pop("everget", False)
if everget:
# Everget uses SMA instead of SUM for calculation
ui = (sma(d2, length) / length).apply(sqrt)
_ui = sma(d2, length)
else:
ui = (d2.rolling(length).sum() / length).apply(sqrt)
_ui = d2.rolling(length).sum()
ui = sqrt(_ui / length)
# Offset
if offset != 0:
pandas_ta/volume/aobv.py
+1 -1
View File
@@ -50,7 +50,7 @@ def aobv(
if slow < fast:
fast, slow = slow, fast
_length = max(fast, slow, max_lookback, min_lookback)
_length = max(max_lookback, min_lookback) + slow
close = v_series(close, _length)
volume = v_series(volume, _length)
pandas_ta/volume/efi.py
+7 -2
View File
@@ -2,8 +2,13 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.utils import v_drift, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_series
from pandas_ta.utils import (
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_series
)
def efi(
pandas_ta/volume/eom.py
+12 -6
View File
@@ -2,8 +2,13 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap import hl2, sma
from pandas_ta.utils import non_zero_range, v_drift
from pandas_ta.utils import v_pos_default, v_offset, v_series
from pandas_ta.utils import (
non_zero_range,
v_drift,
v_pos_default,
v_offset,
v_series
)
def eom(
@@ -41,10 +46,11 @@ def eom(
"""
# Validate
length = v_pos_default(length, 14)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
volume = v_series(volume, length)
_length = length + 1
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
volume = v_series(volume, _length)
if high is None or low is None or close is None or volume is None:
return
pandas_ta/volume/kvo.py
+10 -4
View File
@@ -4,8 +4,14 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.overlap import hlc3
from pandas_ta.utils import signed_series, v_drift, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_series
from pandas_ta.utils import (
signed_series,
v_drift,
v_mamode,
v_offset,
v_pos_default,
v_series
)
def kvo(
@@ -44,7 +50,8 @@ def kvo(
# Validate
fast = v_pos_default(fast, 34)
slow = v_pos_default(slow, 55)
_length = max(fast, slow - 1)
signal = v_pos_default(signal, 13)
_length = max(fast, slow) + signal
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
@@ -53,7 +60,6 @@ def kvo(
if high is None or low is None or close is None or volume is None:
return
signal = v_pos_default(signal, 13)
mamode = v_mamode(mamode, "ema")
drift = v_drift(drift)
offset = v_offset(offset)
pandas_ta/volume/mfi.py
+18 -8
View File
@@ -3,7 +3,13 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.overlap import hlc3
from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_series, v_talib
from pandas_ta.utils import (
v_drift,
v_offset,
v_pos_default,
v_series,
v_talib
)
def mfi(
@@ -39,10 +45,11 @@ def mfi(
"""
# Validate
length = v_pos_default(length, 14)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
volume = v_series(volume, length)
_length = length + 1
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
volume = v_series(volume, _length)
if high is None or low is None or close is None or volume is None:
return
@@ -59,9 +66,12 @@ def mfi(
typical_price = hlc3(high=high, low=low, close=close, talib=mode_tal)
raw_money_flow = typical_price * volume
tdf = DataFrame(
{"diff": 0, "rmf": raw_money_flow, "+mf": 0, "-mf": 0}
)
tdf = DataFrame({
"diff": 0,
"rmf": raw_money_flow,
"+mf": 0,
"-mf": 0
})
tdf.loc[(typical_price.diff(drift) > 0), "diff"] = 1
tdf.loc[tdf["diff"] == 1, "+mf"] = raw_money_flow
pandas_ta/volume/pvt.py
+3 -2
View File
@@ -32,8 +32,9 @@ def pvt(
"""
# Validate
drift = v_drift(drift)
close = v_series(close, drift)
volume = v_series(volume, drift)
_drift = drift + 1
close = v_series(close, _drift)
volume = v_series(volume, _drift)
if close is None or volume is None:
return
pandas_ta/volume/wb_tsv.py
+11 -3
View File
@@ -2,8 +2,15 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.utils import signed_series, v_drift, v_mamode
from pandas_ta.utils import v_pos_default, v_offset, v_series, zero
from pandas_ta.utils import (
signed_series,
v_drift,
v_mamode,
v_pos_default,
v_offset,
v_series,
zero
)
def wb_tsv(
@@ -45,7 +52,8 @@ def wb_tsv(
# Validate
length = v_pos_default(length, 18)
signal = v_pos_default(signal, 10)
close = v_series(close, max(length, signal))
_length = max(length, signal) - 2
close = v_series(close, _length)
if close is None:
return
setup.py
+1 -1
View File
@@ -20,7 +20,7 @@ setup(
"pandas_ta.volatility",
"pandas_ta.volume"
],
version=".".join(("0", "3", "63b")),
version=".".join(("0", "3", "64b")),
description=long_description,
long_description=long_description,
author="Kevin Johnson",
tests/config.py
+1 -1
View File
@@ -88,7 +88,7 @@ _tdpy = pandas_ta.RATE["TRADING_DAYS_PER_YEAR"]
sample_data = load(
n = [
-2 * _tdpy, -_tdpy,
-90, 0, 90,
-89, 0, 89,
_tdpy, 2 * _tdpy
][0],
verbose=VERBOSE
tests/test_ext_indicator_overlap_ext.py
+1 -1
View File
@@ -86,7 +86,7 @@ class TestOverlapExtension(TestCase):
def test_linreg_ext(self):
self.data.ta.linreg(append=True)
self.assertIsInstance(self.data, DataFrame)
self.assertEqual(self.data.columns[-1], "LR_14")
self.assertEqual(self.data.columns[-1], "LINREG_14")
def test_mama_ext(self):
self.data.ta.mama(append=True)
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