Python yazılım dili ile yazılan, geliştirilmeye hazır Endeks Analizi açık kaynak kodlarımız sizinle.
Python dilinde yazılan bu algoritma ile S&P 500 , Dow Jones ve NASDAQ endeksleri hakkında Veri Analizi yapmanız mümkün.
Kodlar aşağıdaki gibi ve çalışmaya hazır. Kopyala yapıştır yöntemiyle alarak kullanabilir, kendi isteklerinize göre geliştirebilir, veri aralıklarını değiştirebilir veya tablo başlıkları ve verileri tamamen değiştirerek istediğiniz başka bir konunun analizini yapabilirsiniz. Bunun için algoritma bilmeniz ve kodlardan anlamanız gerekir.
Programın kaynak kodlarını aşağıda paylaştık. Umarım işinize yarar. Python dili ile ilgili dökümantasyonlara buradan ulaşabilirsiniz.
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"sp = pd.read_csv(\"^GSPC(1).csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"nasdaq = pd.read_csv(\"NQ=F.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"dj = pd.read_csv(\"^DJI.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(251, 7) (304, 7) (251, 7)\n"
]
}
],
"source": [
"print(sp.shape, nasdaq.shape, dj.shape)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method DataFrame.isnull of Date Open High Low Close Adj Close \\\n",
"0 2019-05-20 7514.75 7559.25 7361.50 7391.750000 7391.750000 \n",
"1 2019-05-21 7408.25 7485.25 7400.75 7464.000000 7464.000000 \n",
"2 2019-05-22 7462.75 7475.00 7391.25 7430.000000 7430.000000 \n",
"3 2019-05-23 7430.75 7434.75 7268.00 7311.000000 7311.000000 \n",
"4 2019-05-24 7316.75 7379.75 7300.50 7315.500000 7315.500000 \n",
"5 2019-05-26 NaN NaN NaN NaN NaN \n",
"6 2019-05-28 7315.50 7371.75 7280.25 7295.250000 7295.250000 \n",
"7 2019-05-29 7291.50 7296.00 7183.25 7216.000000 7216.000000 \n",
"8 2019-05-30 7214.75 7271.25 7208.50 7255.250000 7255.250000 \n",
"9 2019-05-31 7251.75 7256.00 7129.25 7133.500000 7133.500000 \n",
"10 2019-06-02 NaN NaN NaN NaN NaN \n",
"11 2019-06-03 7110.00 7132.50 6941.25 6993.750000 6993.750000 \n",
"12 2019-06-04 7008.75 7186.50 6981.75 7179.250000 7179.250000 \n",
"13 2019-06-05 7189.00 7245.25 7150.25 7228.500000 7228.500000 \n",
"14 2019-06-06 7213.75 7299.25 7181.00 7282.250000 7282.250000 \n",
"15 2019-06-07 7266.50 7453.00 7257.75 7419.500000 7419.500000 \n",
"16 2019-06-09 NaN NaN NaN NaN NaN \n",
"17 2019-06-10 7469.00 7578.50 7431.25 7515.500000 7515.500000 \n",
"18 2019-06-11 7519.50 7600.75 7492.75 7518.250000 7518.250000 \n",
"19 2019-06-12 7517.75 7531.00 7461.00 7473.250000 7473.250000 \n",
"20 2019-06-13 7476.00 7536.00 7421.00 7522.750000 7522.750000 \n",
"21 2019-06-14 7503.25 7520.75 7451.50 7478.250000 7478.250000 \n",
"22 2019-06-16 NaN NaN NaN NaN NaN \n",
"23 2019-06-17 7481.00 7550.75 7476.00 7534.250000 7534.250000 \n",
"24 2019-06-18 7542.25 7693.50 7514.75 7645.250000 7645.250000 \n",
"25 2019-06-19 7641.00 7685.75 7601.75 7674.250000 7674.250000 \n",
"26 2019-06-20 7668.00 7793.00 7660.00 7745.250000 7745.250000 \n",
"27 2019-06-21 7737.00 7743.25 7701.50 7723.609863 7723.609863 \n",
"28 2019-06-23 NaN NaN NaN NaN NaN \n",
"29 2019-06-24 7744.50 7794.25 7731.75 7758.750000 7758.750000 \n",
".. ... ... ... ... ... ... \n",
"274 2020-04-14 8448.75 8708.00 8415.25 8675.250000 8675.250000 \n",
"275 2020-04-15 8655.25 8681.75 8486.50 8532.750000 8532.750000 \n",
"276 2020-04-16 8539.00 8966.75 8539.00 8926.250000 8926.250000 \n",
"277 2020-04-17 8915.75 8952.50 8689.50 8812.000000 8812.000000 \n",
"278 2020-04-19 8747.50 8763.75 8733.00 8744.250000 8744.250000 \n",
"279 2020-04-20 8817.00 8846.00 8687.00 8721.750000 8721.750000 \n",
"280 2020-04-21 8647.25 8709.25 8342.00 8445.000000 8445.000000 \n",
"281 2020-04-22 8447.00 8700.00 8400.00 8431.750000 8431.750000 \n",
"282 2020-04-23 8598.50 8786.50 8532.00 8551.000000 8551.000000 \n",
"283 2020-04-24 8535.75 8780.25 8505.00 8763.750000 8763.750000 \n",
"284 2020-04-26 8760.75 8770.00 8730.00 8747.000000 8747.000000 \n",
"285 2020-04-27 8836.75 8898.00 8789.00 8819.750000 8819.750000 \n",
"286 2020-04-28 8795.50 8945.75 8654.50 8764.250000 8764.250000 \n",
"287 2020-04-29 8759.25 9041.00 8733.75 8719.750000 8719.750000 \n",
"288 2020-04-30 9109.50 9144.75 8875.75 9036.500000 9036.500000 \n",
"289 2020-05-01 8847.00 8879.00 8672.00 8988.500000 8988.500000 \n",
"290 2020-05-03 8624.00 8642.25 8562.00 8579.500000 8579.500000 \n",
"291 2020-05-04 8624.00 8828.00 8556.25 8718.000000 8718.000000 \n",
"292 2020-05-05 8843.50 9020.00 8822.25 8956.000000 8956.000000 \n",
"293 2020-05-06 8929.25 9056.50 8916.00 8961.250000 8961.250000 \n",
"294 2020-05-07 9022.75 9137.50 9004.00 9117.000000 9117.000000 \n",
"295 2020-05-08 9208.00 9238.00 9119.00 9222.500000 9222.500000 \n",
"296 2020-05-10 9194.75 9257.25 9174.75 9249.000000 9249.000000 \n",
"297 2020-05-11 9274.25 9339.00 9118.50 9271.750000 9271.750000 \n",
"298 2020-05-12 9253.25 9341.25 9008.00 9012.750000 9012.750000 \n",
"299 2020-05-13 9086.50 9202.75 8878.25 9015.750000 9015.750000 \n",
"300 2020-05-14 9000.25 9107.50 8847.00 9099.750000 9099.750000 \n",
"301 2020-05-15 9095.50 9148.50 8925.50 9117.500000 9117.500000 \n",
"302 2020-05-17 9135.00 9179.00 9110.25 9171.750000 9171.750000 \n",
"303 2020-05-18 9195.50 9244.50 9175.00 9214.000000 9214.000000 \n",
"\n",
" Volume \n",
"0 626777.0 \n",
"1 634294.0 \n",
"2 436744.0 \n",
"3 455678.0 \n",
"4 640165.0 \n",
"5 NaN \n",
"6 407810.0 \n",
"7 528983.0 \n",
"8 568052.0 \n",
"9 403475.0 \n",
"10 NaN \n",
"11 540433.0 \n",
"12 771723.0 \n",
"13 565396.0 \n",
"14 534798.0 \n",
"15 433916.0 \n",
"16 NaN \n",
"17 484835.0 \n",
"18 463884.0 \n",
"19 461725.0 \n",
"20 388858.0 \n",
"21 396174.0 \n",
"22 NaN \n",
"23 254822.0 \n",
"24 184038.0 \n",
"25 209971.0 \n",
"26 112587.0 \n",
"27 101514.0 \n",
"28 NaN \n",
"29 379078.0 \n",
".. ... \n",
"274 157345858.0 \n",
"275 159285002.0 \n",
"276 192049231.0 \n",
"277 201638423.0 \n",
"278 582500.0 \n",
"279 166372961.0 \n",
"280 226945058.0 \n",
"281 368770.0 \n",
"282 157192323.0 \n",
"283 141646451.0 \n",
"284 556167.0 \n",
"285 139152562.0 \n",
"286 157506137.0 \n",
"287 417522.0 \n",
"288 448803.0 \n",
"289 403881.0 \n",
"290 955357.0 \n",
"291 376472.0 \n",
"292 138527264.0 \n",
"293 160386979.0 \n",
"294 150146947.0 \n",
"295 147616823.0 \n",
"296 673663.0 \n",
"297 164856701.0 \n",
"298 182119666.0 \n",
"299 252786223.0 \n",
"300 241955317.0 \n",
"301 201546625.0 \n",
"302 596131.0 \n",
"303 15984652.0 \n",
"\n",
"[304 rows x 7 columns]>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nasdaq.isnull"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NASDAQ pazar günleride çalıştığı için cumartesi günleri NULL değer almış. Silersek sorun olmayacağını düşünüyorum."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"nasdaq = nasdaq.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(282, 7)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nasdaq.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Elimizde Amerikanın en büyük 3 borsa endeksinin 1 yıllık verileri bulunmaktadır. NASDAQ elektronik bir piyasa olduğu için pazar günleri de çalışıyor. Daha önce S&p500 indeksi incelemiştik. Şimdi ise bu 3 borsayı karşılaştırmalı inceleyeceğiz."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2019-05-20</td>\n",
" <td>2841.939941</td>\n",
" <td>2853.860107</td>\n",
" <td>2831.290039</td>\n",
" <td>2840.229980</td>\n",
" <td>2840.229980</td>\n",
" <td>3288870000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2019-05-21</td>\n",
" <td>2854.020020</td>\n",
" <td>2868.879883</td>\n",
" <td>2854.020020</td>\n",
" <td>2864.360107</td>\n",
" <td>2864.360107</td>\n",
" <td>3218700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2019-05-22</td>\n",
" <td>2856.060059</td>\n",
" <td>2865.469971</td>\n",
" <td>2851.110107</td>\n",
" <td>2856.270020</td>\n",
" <td>2856.270020</td>\n",
" <td>3192510000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2019-05-23</td>\n",
" <td>2836.699951</td>\n",
" <td>2836.699951</td>\n",
" <td>2805.489990</td>\n",
" <td>2822.239990</td>\n",
" <td>2822.239990</td>\n",
" <td>3891980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2019-05-24</td>\n",
" <td>2832.409912</td>\n",
" <td>2841.360107</td>\n",
" <td>2820.189941</td>\n",
" <td>2826.060059</td>\n",
" <td>2826.060059</td>\n",
" <td>2887390000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close \\\n",
"0 2019-05-20 2841.939941 2853.860107 2831.290039 2840.229980 \n",
"1 2019-05-21 2854.020020 2868.879883 2854.020020 2864.360107 \n",
"2 2019-05-22 2856.060059 2865.469971 2851.110107 2856.270020 \n",
"3 2019-05-23 2836.699951 2836.699951 2805.489990 2822.239990 \n",
"4 2019-05-24 2832.409912 2841.360107 2820.189941 2826.060059 \n",
"\n",
" Adj Close Volume \n",
"0 2840.229980 3288870000 \n",
"1 2864.360107 3218700000 \n",
"2 2856.270020 3192510000 \n",
"3 2822.239990 3891980000 \n",
"4 2826.060059 2887390000 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2019-05-20</td>\n",
" <td>7514.75</td>\n",
" <td>7559.25</td>\n",
" <td>7361.50</td>\n",
" <td>7391.75</td>\n",
" <td>7391.75</td>\n",
" <td>626777.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2019-05-21</td>\n",
" <td>7408.25</td>\n",
" <td>7485.25</td>\n",
" <td>7400.75</td>\n",
" <td>7464.00</td>\n",
" <td>7464.00</td>\n",
" <td>634294.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2019-05-22</td>\n",
" <td>7462.75</td>\n",
" <td>7475.00</td>\n",
" <td>7391.25</td>\n",
" <td>7430.00</td>\n",
" <td>7430.00</td>\n",
" <td>436744.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2019-05-23</td>\n",
" <td>7430.75</td>\n",
" <td>7434.75</td>\n",
" <td>7268.00</td>\n",
" <td>7311.00</td>\n",
" <td>7311.00</td>\n",
" <td>455678.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2019-05-24</td>\n",
" <td>7316.75</td>\n",
" <td>7379.75</td>\n",
" <td>7300.50</td>\n",
" <td>7315.50</td>\n",
" <td>7315.50</td>\n",
" <td>640165.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Adj Close Volume\n",
"0 2019-05-20 7514.75 7559.25 7361.50 7391.75 7391.75 626777.0\n",
"1 2019-05-21 7408.25 7485.25 7400.75 7464.00 7464.00 634294.0\n",
"2 2019-05-22 7462.75 7475.00 7391.25 7430.00 7430.00 436744.0\n",
"3 2019-05-23 7430.75 7434.75 7268.00 7311.00 7311.00 455678.0\n",
"4 2019-05-24 7316.75 7379.75 7300.50 7315.50 7315.50 640165.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nasdaq.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2019-05-20</td>\n",
" <td>25655.310547</td>\n",
" <td>25751.710938</td>\n",
" <td>25560.550781</td>\n",
" <td>25679.900391</td>\n",
" <td>25679.900391</td>\n",
" <td>279560000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2019-05-21</td>\n",
" <td>25782.339844</td>\n",
" <td>25898.269531</td>\n",
" <td>25779.609375</td>\n",
" <td>25877.330078</td>\n",
" <td>25877.330078</td>\n",
" <td>260870000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2019-05-22</td>\n",
" <td>25818.460938</td>\n",
" <td>25878.210938</td>\n",
" <td>25755.109375</td>\n",
" <td>25776.609375</td>\n",
" <td>25776.609375</td>\n",
" <td>241760000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2019-05-23</td>\n",
" <td>25657.990234</td>\n",
" <td>25657.990234</td>\n",
" <td>25328.089844</td>\n",
" <td>25490.470703</td>\n",
" <td>25490.470703</td>\n",
" <td>316940000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2019-05-24</td>\n",
" <td>25551.070313</td>\n",
" <td>25670.810547</td>\n",
" <td>25496.199219</td>\n",
" <td>25585.689453</td>\n",
" <td>25585.689453</td>\n",
" <td>201370000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close \\\n",
"0 2019-05-20 25655.310547 25751.710938 25560.550781 25679.900391 \n",
"1 2019-05-21 25782.339844 25898.269531 25779.609375 25877.330078 \n",
"2 2019-05-22 25818.460938 25878.210938 25755.109375 25776.609375 \n",
"3 2019-05-23 25657.990234 25657.990234 25328.089844 25490.470703 \n",
"4 2019-05-24 25551.070313 25670.810547 25496.199219 25585.689453 \n",
"\n",
" Adj Close Volume \n",
"0 25679.900391 279560000 \n",
"1 25877.330078 260870000 \n",
"2 25776.609375 241760000 \n",
"3 25490.470703 316940000 \n",
"4 25585.689453 201370000 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dj.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2983.027402135456 8231.08971561702 26370.62047129085\n"
]
}
],
"source": [
"print(sp[\"Close\"].mean(), nasdaq[\"Close\"].mean(), dj[\"Close\"].mean())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"211.08322225482632 644.5625838571945 2139.6924617735717\n"
]
}
],
"source": [
"print(sp[\"Close\"].std(), nasdaq[\"Close\"].std(), dj[\"Close\"].std())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3386.149902 9733.5 29551.419922000005\n"
]
}
],
"source": [
"print(sp[\"Close\"].max(), nasdaq[\"Close\"].max(), dj[\"Close\"].max())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2237.399902 6688.75 18591.929688\n"
]
}
],
"source": [
"print(sp[\"Close\"].min(), nasdaq[\"Close\"].min(), dj[\"Close\"].min())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bölgedeki en büyük endeksin Dow Jones olduğunu söyleyebiliriz. En haraketli endeksin ise NASDAQ olduğunu standart sapmaya bakara söyleyebilirim.\n",
"Maximum ve minimum değerlere bakarak ise çok büyük bir olay olmadığı sürece eğer bu indekslere yatırım yaparsanız ne kadar para kaybedebilirsiniz ve ya en fazla ne kadar kazanabilirsiniz görebilirsiniz. Diğerlerinden farklı olduğu için nasdaq ayrı görselleştirilecek"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"spc = sp[\"Close\"]\n",
"djc = dj[\"Close\"]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"spc.index = pd.DatetimeIndex(sp[\"Date\"])\n",
"djc.index = pd.DatetimeIndex(sp[\"Date\"])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"nasdaqc = nasdaq[\"Close\"]\n",
"nasdaqc.index = pd.DatetimeIndex(nasdaq[\"Date\"])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f6221263278>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"ax.plot(spc,linestyle='-', linewidth=1)\n",
"ax.plot(djc, linestyle='-', linewidth=1)\n",
"ax.legend([\"S&P500\", \"Dow Jones\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gördüğümüz gibi endekslerin değerinden dolayı SP500 de daha önce görüntülediğimiz dataları görüntüleyemiyoruz ama SP500 endeksindeki düşüşü diğer endekslerde de görüyoruz."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f6220defda0>]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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
73OvS54LDgdIazvvXT0QFevHiVeNcg8RHU13n4NZ31lHb4GT60EgmJ4RSVddAXlkNZ6VEUVXXgL+XB2H+ns2aiN3FlORwzhwSwX+W7AXg9qlJnD+qP542Cx/Mmcy8TTl8tTmXH3YeBOCu6YNc3Tm3npFIXYMTL5uFv3y1jbyyWnw8rNQ0OPjXFWOaXKlHBXq7FoA5HoJ8PVl2z1Si3J5MtlqEz2875bi9p9a9jR0YxOvL0/lyUw6pBytYnV50QoNDg8NJvUOxv6iKBz/fSri/Fx/MmdwkQFTVNfC7DzaxYNsBAOLD/BgS5c+WbCM4ZJdUM+OZZVTUGk8CXDjauDljYnwI3h4WvtyUw4drMvkptYBFvzvdtXjT8dKngoNSij98spmCiloKKmp5fMFOHr1weJO+7db2e+ybHeSW1vDhzScxMf7EX5F0lgfOHcqyPflMiAvh7hmDXek2q4VfjB1AZZ3DFRwuO2ziN0+bhbGxwazLKOaOM5O59YxEymrqifA/8XeIxIYe/ysnredonKbky025gHGiPZHumLuRr7fk8vdfjACgps7Bla+s4oObJzMw1I/K2gYue3kF23PK+NO5QxkVE0Swrwffbc9j/tYD/Pb9DaxIK6TBaTzvc/vUJNcFl7eHlamDI1zLwf5h5mBCO7Csa3v1qeDgcCpGRAdwdkok+4uqjAHZzblMjAth9pj+nDuiX4uB4rWf0nl7ZQa/nhLfowMDQEK4nfd+PbnVhVPOG9GPrKIqZo3o1+LV//NXjKHB6XS1jo5ljWBN6yz9Ar2JDPBi9T5jbKxxkr4T5estRlD6cG0mIX6evHvTJK58ZSW/emMN3//udD5am8nW7DJeunosM4cfutuq3JymZUVaIRPjQrhyUizJEXbC/ZuOmc0a0Y/5Ww8wOiaI35zR+jM+nUmMWTB6nvHjx6u1a9d2eP/S6nr+tzwdp4KvN+ew11yA5cHzUjg1KczVHMwqrmLqk0uYOjiC/14zrsufMtY0rWW3vrOO+VuNLpvkCDsLf3f6cXuvrOIqlIKYEF/qGpwM+tN817Yzh0Tw2vUTePPnfTw0bxsXjYnmsw3ZjI4JarHrs6ymHn8v2xHPLTX1Dp5YsIsbp8QTfYwr4onIOqXU+KPl61MtB3eBPh7cOd24m+au6clsyzHm2PnzF8ZyjAvunMKQqAAWbc+j3qG4/xw9lbKmdWdjY4NdwSGnpBql1HH5zjY4nFz60gpyy2qYOjiCUw5bo/uKicY43fBo48aIzzYY3UF3nz2YlrTlwVRvDyt/Pj/lWIrdbnoaQ4wpk4dHB/LyteO4+fQEAJ78djebs0r4fudB4lp48ljTtO5lTGyQ63VlnYOy6oYj5O64hdvzyCmt4fyR/dmcVcpfv9oOwNOXjmLunMlMN++gc79b772bJnFqcliLx+uu+mzLoSX+3h7cN2soVhH+s2Qvi3YY99Jfd9LALi6ZpmlHMzw6EC+bhXB/L7KKq8kqqSLQt/Mn4/t8Yzb9Ar155rLROJyK+VtzySmp4aIx0U1aKu6zC48dePR1PbobHRxacM+MwVw0Jpq9+ZVkFVdx3sj+XV0kTdOOwtvDyke3nEROSQ23vLOOjMIq7F62Tp92YltOGeMGBmO1CFaLNJsfzN0fZw6hsKK2R964oYNDC0SE5Eh/1xPOmqb1DCMHBOFpKwPgb19tp86hWPPAmZ029lBWU09WcbVrXOFobj0j8eiZuik95qBpWq8S6GMM8OaU1lBQUUthZV2nHXtnbjkAKf16/1P4OjhomtarHH73z96DnbPG9KbMEi79r7FA5VAdHDRN03oWX08rNsuhbqS0gsqj7lNd56DYrYXR0vNfjWtXXzi6P5EBvX9iRx0cNE3rVUSEAJ9DrYe2tByuenUlY/66EIBVaYWMePg7trhNiAewZHc+04ZE8OzlY/rEM086OGia1usEeB+616YtLYf1+0sAyC+v5c9fbKOitoG3VuxzbU8vqCSjsIozBjdfT6K30sFB07ReJ9Ct5ZCW3/Yxhwc+28KuvHKSI+x8uTmHqjrjQbolu4zJKM8YFNG5Be3GdHDQNK3XaexWCvXzZH9RFbUNrS8u6T6+8N32PKYkh/H7swdRU+8kLd9odSzZlU9CmF+fmg24w8FBRGJEZLGI7BCRbSJyh5n+sIhki8hG8+cct33uE5FUEdklIjPc0meaaakicu+xVUnTtL6uMTiMHRiMU8H+wirqHc4W85bXHppmw2YRHjo/xTVdTnpBJdV1DlakFXJ6H+pSgmN7CK4B+L1Sar2I+APrRGShue0ZpdST7plFJAVj3ehhQH9gkYg0riP5AnAWkAWsEZF5Sqntx1A2TdP6sMbbWccNDGbh9jx+2HmQp77bzSe3nsyIAU2n1DhQWgMYczNdODqapAh/quuMlsb/vb8BX08rdQ1Ozhjcd7qU4NjWkM4Fcs3X5SKyA2j9OXKYDcxVStUC6SKSyqG1plPNtacRkblmXh0cNE3rkAAf49Q2JsaYjO/rLbnUOZxszi5pNTjcf85Q1+pxPp5WQv08Kayso8oMFJN6+Fou7dUpYw4iEgeMAVaZSbeLyGYReV1EGmecigYy3XbLMtNaS2/pfeaIyFoRWZufn98ZRdc0rRdqHJAeEOJLZICXaynOrBYWAWoMDlGHLbvp/mR1hL9Xj5wf6Vgcc3AQETvwCXCnUqoMeBFIBEZjtCyeaszawu7qCOnNE5V6WSk1Xik1Pjy8b/X/aZrWdqcmhTFzWBSR/l4khNlpHHPOLKpqlvf7nXn4elqbrck8zpxJ9Z0bJ/HxLScf9zJ3N8c08Z6IeGAEhneVUp8CKKXy3La/Anxl/poFuC9KPADIMV+3lq5pmtZuIwcE8dI14wBIjPBjRVohAJmHtRw2Zpbw7bY87pkxGE9b02vlV68dT155TZN1GfqSY7lbSYDXgB1Kqafd0vu5ZbvLF+NxAAAgAElEQVQI2Gq+ngdcLiJeIhIPJAOrgTVAsojEi4gnxqD1vI6WS9M0zV1CmN31Oru4acsh1Xx6+ryR/ThcsJ9nnw0McGzdSqcA1wDTDrtt9QkR2SIim4GpwF0ASqltwIcYA80LgNuUUg6lVANwO/AtsAP40MyraZp2zBLCD63nUFBRx5QnfnA921BUWQtAiJ9nl5StOzuWu5V+ouXxgm+OsM+jwKMtpH9zpP00TdM6KjHcaDlMSQ7jxz0FZBZVk1taQ/8gH4oq6/GwCnYvvbTN4fQT0pqm9WoDgn24b9YQHrtoBO//ejIAe80pNYor6wj29ewTE+m1lw4Omqb1aiLCzacnEhPiS1KE0YpoHGsorKzTXUqt0MFB07Q+I8zuSYC3zRUciqt0cGiNDg6apvUZIkJShJ3tuWUopSjWLYdW6eCgaVqfMmNYFBv2l/DRuiyKdMuhVXqIXtO0PuWmKQks3Z3Pn7/YSk29k2BfHRxaolsOmqb1KVaL8Oxlo/HzNK6NQ+06OLREBwdN0/qciABvnr5sNFaLMDDU7+g79EG6W0nTtD7p9EHhbPjzWfjrB+BapD8VTdP6rMZFgbTmxH391J5ERPKBjK4uRycIAwq6uhCdrDfVSdele9J16biBSqmjrnnQY4NDbyEia5VS47u6HJ2pN9VJ16V70nU5/vSAtKZpmtaMDg6apmlaMzo4dL2Xu7oAx0FvqpOuS/ek63Kc6TEHTdM0rRndctA0TdOa0cFB0zRNa0YHh3YSkRgRWSwiO0Rkm4jcYaaHiMhCEdlj/htspg8RkRUiUisidx92rDtEZKt5nDuP8J4zRWSXiKSKyL1u6T+6rd+dIyKf94I6TROR9eYx3hSRdj2o2UV1eV1EDorI1sPSLzH3dYpIu29V7EBdrhKRzebPzyIyyu1YLX7eLbzndeZx94jIdW7pj4pIpohUtLce3bAuC0Rkk1mOl0TE2oPrssTcv/E8ENGeuhyRUkr/tOMH6AeMNV/7A7uBFOAJ4F4z/V7gH+brCGACxtrZd7sdZziwFfDFeFJ9EZDcwvtZgb1AAuAJbAJSWsj3CXBtT64TxsVKJjDIzPcX4MbuXBcz72nAWGDrYelDgcHAEmD8Cfh/ORkINl/PAla1828oBEgz/w02Xzceb7JZnooT9Dd2POsSYP4rGN+by3twXTr0t9WWn6O2HETEW0RWu0XaR8z0eBFZZUayD0TE00z3Mn9PNbfHuR3rPjN9l4jMcEtvU/TsDpRSuUqp9ebrcmAHEA3MBt40s70JXGjmOaiUWgPUH3aoocBKpVSVUqoBWApc1MJbTgRSlVJpSqk6YK75Xi4i4g9MAzrUcuhGdQoFapVSu818C4GLu3ldUEotA4paSN+hlNrVnvIfY11+VkoVm+krgQHm66P+DZlmAAuVUkXmcRYCM81jr1RK5faSupSZeWwYJ+V23ZXTnepyPLWlW6kWmKaUGgWMBmaKyGTgH8AzSqlkoBi40cx/I1CslEoCnjHzISIpwOXAMIyK/UdErGaT7gWMiJoCXGHm7fbMwDcGWAVENn55zH+P1rzbCpwmIqEi4gucA8S0kC8a42q6UZaZ5u4i4Hu3P/oO6+I6FQAebl0wv2xl/zY5QXU5ITpQlxuB+ebrtvwNtSffMekOdRGRb4GDQDnwcQeq0XicOLr+/+V/ZpfSgyIiHahGi44aHJShsZ/Rw/xRGFeqjR+qK0rSNHp+DJxpFng2MFcpVauUSgdSMSJnW6NntyIidowm6Z0dOSkrpXZgBM6FwAKMJmVDS2/V0u6H/X4F8H57y9Dsjbq4TspoJ18OPCMiqzG+uC3tf1QnsC7HXXvrIiJTMU5Cf2xMaiFbS1fLbc3XYd2lLkqpGRjdQ14Y57J26yZ1uUopNQKYYv5cc7RytFWbnnMwr+7XAUkYV/n/xGhyJ5nbY4D5SqnhYgzKzVRKZZnb9gKTgIfNfd4x01/jUASdqZS6yUy/BpiklLr9SGUKCwtTcXFx7autpmlaH7du3boC1YaJ99p0J4hSygGMFpEg4DOM/thm2cx/W4tyraW31HppMWKJyBxgDkBsbCxr1649Ssk1TdM0dyLSptms23Urq1KqBGN0fDIQJIduMxwA5JivszD7Zs3tgRiDda70w/ZpLb2l939ZKTVeKTU+PPyogU/TtG7q6YW7eWvFPtrSc6F1jaO2HEQkHKhXSpWIiA8wHaMvdjHGgOFc4DrgC3OXeebvK8ztPyillIjMA94TkaeB/kAysBqjRZEsIvFANkaf85WdV0VN0zois6jKPIHDhPgQJsSFEOLXvvWWd+SW8eS3uzh/VH8uHBPNwbIa3l21n+e/3wPAPxfsYlxcMKckhjEhPoT5W3OZnBDK1MGdd7u+1jFHHXMQkZEYA8xWjJbGh0qpv4hIAkZgCAE2AFcrpWpFxBt4G2MEvwjjHuI081gPADdgDOzdqZSab6afAzxrvsfrSqlHj1bw8ePHK92tpGnHR73DyaznfmR/YRUI1DU4AYgL9WVAsC+zRkRx1aSBRzyGUoppTy0lvaASP08r4+JC+GlPPk4FCeF+3HJ6IhszS1iZVkhafqVrPxH49xVjOXdkv+Nax75KRNapNqwf0WMn3tPBQdPar6begbdH0weCt2aXsiq9iKsnx5JZVMX23HK+2pTDd9vzePXa8UwZFMaWrFJW7ytia3YpqQcr2J1XwZ/OHcpNUxJcx2k8lzTeTbk9p4xznv+RG06J551VGYTbvbhoTDSnDQonpX8Adre1m3NLq1mdXsSAYB/+9vUO9h6sYNHvTyfC3/sEfCp9iw4Omqa5OJ2KBz7fypebcvj8tpMJ8vXkQGkNIX6ezH5hOfnltVgtgsNpnA98PKzcPi2J35yR6DrZN2pwOPm/9zcwf+sB7pkxmKsnD+TbbQd4btEeZgyL4s/nG48pPfntLl5cupc1D0zHKoK/tw2L5ei34e/Nr+Csp5dy8+mJ/HHmkM7/MPo4HRw0rY9ocDhJza9ga3YZnjYLmzNLWLYnn3qH4vyR/Zg9Jpo3lu/j7ZUZeFotNDidmDEAD6tgtQh3TR9EYWUdgyP9GdovgKQIO5621u9XaXA4uefjzXy2IZswuycFFXUA2CzCnkdn4VQw5R8/kBTpz1s3TGx3nW57dz3L9uTz873T8Pf26NDnorWsrcGhXZOaaZrWfZTV1PP4/J18tj6b6nqHK93DKpyUGIZSin8tTuX5H1IBmHNaAmcMDue7bXnEhvhiswofrs3kvllDOSUprF3vbbNaeOqSUfh6WvlgTSYvXDmWoqo6Hvx8K0t257M+o5ic0hr+dF7HJju4+fQEvt6Sy/ur9zPntMQOHUM7NrrloGknUE29g5Vphfh72xgTE9ymbhaA/YVVZBRVsjW7jM1ZJWzNKaW8poGKmgZ+MTaakxPDGB4dQHWdk9hQXwJ9jKvtnJJq5m89wKBIO6cmhTXrIuoM5TX1+Ht7kFlUxZQnFrvSJ8QF8+5Nk4/YAjmSK19Zyd78Cn78w7QOH0NrTrccNO0E+2JjNmv2FVFW3YCH1cJvz0xiYKgfSik2Z5XywdpMvtyYQ3mtMQtHdJAPt5yewBUTY7FZjZNfdZ2DFxanEhXozeiYIAZH+bN0Vz6/fnstjddxA0N9GTkgCAGumjSQkxJDWy1T/yAfbjw1/rjWu7HbJybEl3tnDcFmEaYPjSQuzO+Yjnvz6Ylc9/pqPt+YzaXju2xaqz5Ltxw07RhlFlXx3ur9vLhkLwHeNkL8PMkvr8Wp4NenJbBk10E2Z5Xi7WHhnOHGGEBRZS3vrdrPmn3FJEfYeeSCYZycFMbLy/by2Dc7Xcf2tFlwOBWDI/158LwUhkT5E9zOZw16KqUUM55dRpCvJx/efFJXF6fX0C0HTTsO/rc8nU/XZ1PvcFLvcNLgVOSW1FDncDIiOpCPbz0JL5uV3NJq7vt0C89/v4dwfy/+euFwZo/uT4Db4OqFo6P5bnsej32zg2tfX82FY6JZuD2PkxND+cfFI9mUVcLmrFI8rMI1k+OICuxbt3WKCBPjQ/hiYw5KqePSJaa1Trcceoi6BicVtQ1YLeLqT3bncCosgv4CdSKnU7H7YDlr9xWzLqOYnQfK2ZFbxojoQPoFeuNhtWCzCuF2Ly4eN4CkCDse1qZ94zkl1QT6eODn1fp1WG5pNSf9/QcAJsaH8NfZwxkc5X9c69ZTvLsqgwc+28pPf5zKgGDfri5Or6BbDr3E3vwKrnl1FTmlNa60ifEhXDJuAJMTQgnw8aCgopab3lyLh1V46PxheHtYKamqo7ymAbuXjdMHhzc7aXWlgopa1mcUExfmR3KEvdsFNIdTUVBRy1++3M7XW4z1bcLsXgyPDmBKcgL3zBjc5s+zf5DPUfP0C/ThpavHUVnbwMXjBhw1f18yJCoAgJ255To4nGA6OHQTLTWbHU7F3R9torLOwe/PGoS/t43ymgZeXpbGPembmx3D7mXjqldXNUu/YmIMf//FyONW9rZSSnH/Z1t4f/WhdUsGBPuQFGFna3YZQb4e3DtzCNNTIl3bnU7V5jt6WrN2XxG1DU4EOCkxtMVglF1SzbLd+axOLyItv4JNWaUAXH9yHDecEk9MiM9xDWIzh0cdt2P3ZI0tqDUZRU3+LgBqGxw8Pn8nvzkjiXB/r64oXq+mg0MXq6l3cPPb61iVXkhsiC/D+gcyJjaIMTHBfLf9ABv2l/Dc5aOZPfrQwk+/OjWe7OJqftyTD4CPp5XxA0PoF+TNh2syiQ3xJTLAG7u3jf8u3ctHa7O4fVoy0W24iu1spdX1ZBZVUVxVx0+pBby/OpMrJsZw0ZgB7M2v4PsdeWQUVnFachjbc8u4+Z11hNu9qG1wEB/mR15ZLQE+Hlx30kCKqurYklXK8OhAbpua1Op7NjicbMspw9fTyt78Cm55Z71r23OXj2ZEdKDxdLDdeEr48w3ZfL7RmAg4zO6F3cvKyYmh5JXVcO+sIc2mm9BOHLuXjdMHhfPfpWlMjg9l6pBDE/Jt3F/C/5bvIzHcztWTjzzPk9Z+eszhOPn7/B0s211AmN2TeoeTAG8PHpk9jH6BPlTWNrA2o5iVaYV8u/UAaQWVXDp+AEWVdWzKKiW/vNZ1nPNG9uNfV4zp8FVrVnEV055cyuzR/fnnJaM6q3pt8p8lqTy7cA91Dqcr7byR/Xju8jFYW2gNlNfU8+yiPZTX1ONhtfDFxhxsViHUz5O95sRs/t42KmsbWHrPVGJCWu
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\n",
"text/plain": [
"<Figure size 432x288 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(3)\n",
"fig.suptitle('Farklı görselleştirmeler')\n",
"axs[0].plot(spc)\n",
"axs[1].plot(djc)\n",
"axs[2].plot(nasdaqc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tüm endekslerde bir yükseliş söz konusu iken benzer bir düşüş görülmüş. Amerika'daki bir olay tüm borsaları etkilemiş olabilir. Tarihsel inceleme yapmak gerekiyor."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Okumak isterseniz : Finans ve Veri Bilimi
Anahtar Kelimeler : Endeks Analizi, SP500, NASDAQ, Dow Jones, Amerikan Borsası, Hisse Analizleri, Karşılaştırmalı Veriler, Borsa İstatistikleri, Hisse Analizleri, Hisse Tahmin Yazılımı, Borsa Yazılımları, Finans Yazılımları, FinTech, Piyasalarda Yazılım, Python Veri Analizi, Python, Python Borsa Analizi, Python Yazılım Dili