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Rogeman/pythonchart_ruleofthree.ipynb

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pythonchart_ruleofthree.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>nice_timestamp</th>\n",
" <th>price</th>\n",
" <th>volume</th>\n",
" <th>init_CURR</th>\n",
" <th>action</th>\n",
" <th>init_balance</th>\n",
" <th>countervalue</th>\n",
" <th>fee</th>\n",
" <th>final_balance</th>\n",
" <th>final_CURR</th>\n",
" <th>final_bal_EUR</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1389173189</td>\n",
" <td>NaN</td>\n",
" <td>624.01000</td>\n",
" <td>0.20000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>13.0</td>\n",
" <td>XBT</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1389173198</td>\n",
" <td>NaN</td>\n",
" <td>624.01000</td>\n",
" <td>0.09767</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>13.0</td>\n",
" <td>XBT</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1389173198</td>\n",
" <td>2014/01/08 10:26:38</td>\n",
" <td>623.53836</td>\n",
" <td>0.01358</td>\n",
" <td>XBT</td>\n",
" <td>hold</td>\n",
" <td>13.0</td>\n",
" <td>13.0</td>\n",
" <td>0.0</td>\n",
" <td>13.0</td>\n",
" <td>XBT</td>\n",
" <td>8105.99868</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1389173265</td>\n",
" <td>2014/01/08 10:27:45</td>\n",
" <td>623.53836</td>\n",
" <td>0.14896</td>\n",
" <td>XBT</td>\n",
" <td>hold</td>\n",
" <td>13.0</td>\n",
" <td>13.0</td>\n",
" <td>0.0</td>\n",
" <td>13.0</td>\n",
" <td>XBT</td>\n",
" <td>8105.99868</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1389173339</td>\n",
" <td>2014/01/08 10:28:59</td>\n",
" <td>623.54066</td>\n",
" <td>0.20000</td>\n",
" <td>XBT</td>\n",
" <td>hold</td>\n",
" <td>13.0</td>\n",
" <td>13.0</td>\n",
" <td>0.0</td>\n",
" <td>13.0</td>\n",
" <td>XBT</td>\n",
" <td>8106.02858</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" timestamp nice_timestamp price volume init_CURR action \\\n",
"0 1389173189 NaN 624.01000 0.20000 NaN NaN \n",
"1 1389173198 NaN 624.01000 0.09767 NaN NaN \n",
"2 1389173198 2014/01/08 10:26:38 623.53836 0.01358 XBT hold \n",
"3 1389173265 2014/01/08 10:27:45 623.53836 0.14896 XBT hold \n",
"4 1389173339 2014/01/08 10:28:59 623.54066 0.20000 XBT hold \n",
"\n",
" init_balance countervalue fee final_balance final_CURR final_bal_EUR \n",
"0 NaN NaN NaN 13.0 XBT NaN \n",
"1 NaN NaN NaN 13.0 XBT NaN \n",
"2 13.0 13.0 0.0 13.0 XBT 8105.99868 \n",
"3 13.0 13.0 0.0 13.0 XBT 8105.99868 \n",
"4 13.0 13.0 0.0 13.0 XBT 8106.02858 "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"df= pd.read_csv(\"./backtest_output.txt\", names=[\"timestamp\",\"nice_timestamp\",\"price\",\"volume\",\"init_CURR\",\"action\",\"init_balance\",\"countervalue\",\"fee\",\"final_balance\",\"final_CURR\",\"final_bal_EUR\"])\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>nice_timestamp</th>\n",
" <th>price</th>\n",
" <th>volume</th>\n",
" <th>init_CURR</th>\n",
" <th>action</th>\n",
" <th>init_balance</th>\n",
" <th>countervalue</th>\n",
" <th>fee</th>\n",
" <th>final_balance</th>\n",
" <th>final_CURR</th>\n",
" <th>final_bal_EUR</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4853499</th>\n",
" <td>1487641379</td>\n",
" <td>2017/02/21 02:42:59</td>\n",
" <td>1021.516</td>\n",
" <td>3.0000</td>\n",
" <td>EUR</td>\n",
" <td>sell</td>\n",
" <td>0.000001</td>\n",
" <td>0.000001</td>\n",
" <td>0.0</td>\n",
" <td>0.000001</td>\n",
" <td>EUR</td>\n",
" <td>0.000001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4853500</th>\n",
" <td>1487641379</td>\n",
" <td>2017/02/21 02:42:59</td>\n",
" <td>1021.030</td>\n",
" <td>3.1104</td>\n",
" <td>EUR</td>\n",
" <td>sell</td>\n",
" <td>0.000001</td>\n",
" <td>0.000001</td>\n",
" <td>0.0</td>\n",
" <td>0.000001</td>\n",
" <td>EUR</td>\n",
" <td>0.000001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4853501</th>\n",
" <td>1487641379</td>\n",
" <td>2017/02/21 02:42:59</td>\n",
" <td>1021.030</td>\n",
" <td>6.8896</td>\n",
" <td>EUR</td>\n",
" <td>hold</td>\n",
" <td>0.000001</td>\n",
" <td>0.000001</td>\n",
" <td>0.0</td>\n",
" <td>0.000001</td>\n",
" <td>EUR</td>\n",
" <td>0.000001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4853502</th>\n",
" <td>1487641379</td>\n",
" <td>2017/02/21 02:42:59</td>\n",
" <td>1021.000</td>\n",
" <td>0.1500</td>\n",
" <td>EUR</td>\n",
" <td>hold</td>\n",
" <td>0.000001</td>\n",
" <td>0.000001</td>\n",
" <td>0.0</td>\n",
" <td>0.000001</td>\n",
" <td>EUR</td>\n",
" <td>0.000001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4853503</th>\n",
" <td>1487641381</td>\n",
" <td>2017/02/21 02:43:01</td>\n",
" <td>1020.870</td>\n",
" <td>2.9604</td>\n",
" <td>EUR</td>\n",
" <td>sell</td>\n",
" <td>0.000001</td>\n",
" <td>0.000001</td>\n",
" <td>0.0</td>\n",
" <td>0.000001</td>\n",
" <td>EUR</td>\n",
" <td>0.000001</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" timestamp nice_timestamp price volume init_CURR action \\\n",
"4853499 1487641379 2017/02/21 02:42:59 1021.516 3.0000 EUR sell \n",
"4853500 1487641379 2017/02/21 02:42:59 1021.030 3.1104 EUR sell \n",
"4853501 1487641379 2017/02/21 02:42:59 1021.030 6.8896 EUR hold \n",
"4853502 1487641379 2017/02/21 02:42:59 1021.000 0.1500 EUR hold \n",
"4853503 1487641381 2017/02/21 02:43:01 1020.870 2.9604 EUR sell \n",
"\n",
" init_balance countervalue fee final_balance final_CURR \\\n",
"4853499 0.000001 0.000001 0.0 0.000001 EUR \n",
"4853500 0.000001 0.000001 0.0 0.000001 EUR \n",
"4853501 0.000001 0.000001 0.0 0.000001 EUR \n",
"4853502 0.000001 0.000001 0.0 0.000001 EUR \n",
"4853503 0.000001 0.000001 0.0 0.000001 EUR \n",
"\n",
" final_bal_EUR \n",
"4853499 0.000001 \n",
"4853500 0.000001 \n",
"4853501 0.000001 \n",
"4853502 0.000001 \n",
"4853503 0.000001 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>price</th>\n",
" <th>volume</th>\n",
" <th>init_balance</th>\n",
" <th>countervalue</th>\n",
" <th>fee</th>\n",
" <th>final_balance</th>\n",
" <th>final_bal_EUR</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>4.853504e+06</td>\n",
" <td>4.853504e+06</td>\n",
" <td>4.853504e+06</td>\n",
" <td>4.853502e+06</td>\n",
" <td>4.853502e+06</td>\n",
" <td>4.853502e+06</td>\n",
" <td>4.853504e+06</td>\n",
" <td>4.853502e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.450809e+09</td>\n",
" <td>4.868212e+02</td>\n",
" <td>8.807199e-01</td>\n",
" <td>2.899128e+00</td>\n",
" <td>2.899727e+00</td>\n",
" <td>1.200722e-03</td>\n",
" <td>2.899130e+00</td>\n",
" <td>5.493902e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.817489e+07</td>\n",
" <td>2.224472e+02</td>\n",
" <td>2.494329e+00</td>\n",
" <td>9.938649e+01</td>\n",
" <td>9.941039e+01</td>\n",
" <td>1.109688e-01</td>\n",
" <td>9.938646e+01</td>\n",
" <td>1.410595e+02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.389173e+09</td>\n",
" <td>1.481712e+02</td>\n",
" <td>1.000000e-08</td>\n",
" <td>9.000000e-10</td>\n",
" <td>9.000000e-10</td>\n",
" <td>0.000000e+00</td>\n",
" <td>9.000000e-10</td>\n",
" <td>1.915000e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.428970e+09</td>\n",
" <td>3.112394e+02</td>\n",
" <td>3.650000e-02</td>\n",
" <td>1.000000e-09</td>\n",
" <td>1.000000e-09</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1.000000e-09</td>\n",
" <td>3.677000e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.458598e+09</td>\n",
" <td>4.241906e+02</td>\n",
" <td>1.660000e-01</td>\n",
" <td>2.556000e-07</td>\n",
" <td>2.557000e-07</td>\n",
" <td>0.000000e+00</td>\n",
" <td>2.556000e-07</td>\n",
" <td>4.926000e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.474221e+09</td>\n",
" <td>6.140200e+02</td>\n",
" <td>6.841050e-01</td>\n",
" <td>5.422000e-07</td>\n",
" <td>5.423000e-07</td>\n",
" <td>0.000000e+00</td>\n",
" <td>5.422000e-07</td>\n",
" <td>6.620000e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.487641e+09</td>\n",
" <td>1.089000e+03</td>\n",
" <td>3.733930e+02</td>\n",
" <td>8.084924e+03</td>\n",
" <td>8.106000e+03</td>\n",
" <td>2.107560e+01</td>\n",
" <td>8.084924e+03</td>\n",
" <td>8.106029e+03</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" timestamp price volume init_balance countervalue \\\n",
"count 4.853504e+06 4.853504e+06 4.853504e+06 4.853502e+06 4.853502e+06 \n",
"mean 1.450809e+09 4.868212e+02 8.807199e-01 2.899128e+00 2.899727e+00 \n",
"std 2.817489e+07 2.224472e+02 2.494329e+00 9.938649e+01 9.941039e+01 \n",
"min 1.389173e+09 1.481712e+02 1.000000e-08 9.000000e-10 9.000000e-10 \n",
"25% 1.428970e+09 3.112394e+02 3.650000e-02 1.000000e-09 1.000000e-09 \n",
"50% 1.458598e+09 4.241906e+02 1.660000e-01 2.556000e-07 2.557000e-07 \n",
"75% 1.474221e+09 6.140200e+02 6.841050e-01 5.422000e-07 5.423000e-07 \n",
"max 1.487641e+09 1.089000e+03 3.733930e+02 8.084924e+03 8.106000e+03 \n",
"\n",
" fee final_balance final_bal_EUR \n",
"count 4.853502e+06 4.853504e+06 4.853502e+06 \n",
"mean 1.200722e-03 2.899130e+00 5.493902e+00 \n",
"std 1.109688e-01 9.938646e+01 1.410595e+02 \n",
"min 0.000000e+00 9.000000e-10 1.915000e-07 \n",
"25% 0.000000e+00 1.000000e-09 3.677000e-07 \n",
"50% 0.000000e+00 2.556000e-07 4.926000e-07 \n",
"75% 0.000000e+00 5.422000e-07 6.620000e-07 \n",
"max 2.107560e+01 8.084924e+03 8.106029e+03 "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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jYkcasSrvLO8TrU/fAa98B1b8Gn53Fdy+vPtiM8b0WnYhPJHMuN8rq96Hqi3+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\nC7YdUSSVqQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8c716637f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.loc[0:30000,['nice_timestamp','price','final_bal_EUR']].plot()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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