# Python - K-Line and Bollinger Bands
## Time Interval Conversion of K-Line
* Take 1 min K in TXF for example(conversion to 30 min K)
1. import packages and read csv
```python
import pandas as pd
prices = pd.read_csv("TXF-220101-174827.csv")
```
2. **Set index to datetime format in order to use library**
```python
prices = prices.set_index(pd.DatetimeIndex(prices["Date"]))
```
3. extract hours and filter out data in pre-market
- TXF trading time: 08:45 am to 13:45 pm
```python
hours = prices.index.hour
prices = prices.iloc[(hours < 14) & (hours > 7)]
```
4. **Use resample() and agg() method from pandas**
```python
prices_30 = prices.resample(
"30min",
closed="right",
label="right",
offset="15min"
).agg({
"Open": "first",
"High": "max",
"Low": "min",
"Close": "last",
"Volume": "sum"
}).dropna()
```
Result:
## Bollinger Bands
1. import ta packages
- Documentations: https://technical-analysis-library-in-python.readthedocs.io/en/latest/index.html
```python
import ta
```
2. read in 30min K data(created by steps written above)
```python
df = pd.read_csv("output/TXF_30.csv")
```
3. create bollinger bands object
```python
# Create BollingerBands Object
# window -> n period of MA
# window_dec -> nth variance(Band Width)
bb = ta.volatility.BollingerBands(close=df["Close"], window=20, window_dev=2)
```
4. use method inside the object
```python
df["BB_h"] = bb.bollinger_hband()
df["BB_m"] = bb.bollinger_mavg()
df["BB_l"] = bb.bollinger_lband()
```
By using TA library, it is rather easy to generate all indicators often used in technical analysis by implementing the following code.
```python
all_features = ta.add_all_ta_features(
df,
open="Open",
high="High",
low="Low",
close="Close",
volume="Volume",
fillna=True
)
```
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