# Bot Optimization
This page explains where to customize your strategies, validate their 
performance by using Backtesting, and tuning them by finding the optimal
parameters with Hyperopt.

## Table of Contents
- [Change your strategy](#change-your-strategy)
- [Add more Indicator](#add-more-indicator)
- [Test your strategy with Backtesting](#test-your-strategy-with-backtesting)
- [Find optimal parameters with Hyperopt](#find-optimal-parameters-with-hyperopt)
- [Show your buy strategy on a graph](#show-your-buy-strategy-on-a-graph)

## Change your strategy
The bot is using buy and sell strategies to buy and sell your trades. 
Both are customizable.

### Buy strategy
The default buy strategy is located in the file 
[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L73-L92). 
Edit the function `populate_buy_trend()` to update your buy strategy.

Sample:
```python
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
    """
    Based on TA indicators, populates the buy signal for the given dataframe
    :param dataframe: DataFrame
    :return: DataFrame with buy column
    """
    dataframe.loc[
        (
            (dataframe['rsi'] < 35) &
            (dataframe['fastd'] < 35) &
            (dataframe['adx'] > 30) &
            (dataframe['plus_di'] > 0.5)
        ) |
        (
            (dataframe['adx'] > 65) &
            (dataframe['plus_di'] > 0.5)
        ),
        'buy'] = 1

    return dataframe
```

### Sell strategy
The default buy strategy is located in the file 
[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L95-L115)
Edit the function `populate_sell_trend()` to update your buy strategy.

Sample:
```python
def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
    """
    Based on TA indicators, populates the sell signal for the given dataframe
    :param dataframe: DataFrame
    :return: DataFrame with buy column
    """
    dataframe.loc[
        (
            (
                (crossed_above(dataframe['rsi'], 70)) |
                (crossed_above(dataframe['fastd'], 70))
            ) &
            (dataframe['adx'] > 10) &
            (dataframe['minus_di'] > 0)
        ) |
        (
            (dataframe['adx'] > 70) &
            (dataframe['minus_di'] > 0.5)
        ),
        'sell'] = 1
    return dataframe
```

## Add more Indicator
As you have seen, buy and sell strategies need indicators. You can see 
the indicators in the file 
[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L95-L115).
Of course you can add more indicators by extending the list contained in
the function `populate_indicators()`.

Sample:
```python
def populate_indicators(dataframe: DataFrame) -> DataFrame:
    """
    Adds several different TA indicators to the given DataFrame
    """
    dataframe['sar'] = ta.SAR(dataframe)
    dataframe['adx'] = ta.ADX(dataframe)
    stoch = ta.STOCHF(dataframe)
    dataframe['fastd'] = stoch['fastd']
    dataframe['fastk'] = stoch['fastk']
    dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
    dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
    dataframe['mfi'] = ta.MFI(dataframe)
    dataframe['rsi'] = ta.RSI(dataframe)
    dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
    dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
    dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
    dataframe['ao'] = awesome_oscillator(dataframe)
    macd = ta.MACD(dataframe)
    dataframe['macd'] = macd['macd']
    dataframe['macdsignal'] = macd['macdsignal']
    dataframe['macdhist'] = macd['macdhist']
    hilbert = ta.HT_SINE(dataframe)
    dataframe['htsine'] = hilbert['sine']
    dataframe['htleadsine'] = hilbert['leadsine']
    dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
    dataframe['plus_di'] = ta.PLUS_DI(dataframe)
    dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
    dataframe['minus_di'] = ta.MINUS_DI(dataframe)
    return dataframe
```

## Test your strategy with Backtesting
Now you have good Buy and Sell strategies, you want to test it against
real data. This is what we call [backtesting](https://en.wikipedia.org/wiki/Backtesting).

Backtesting will use the crypto-currencies (pair) tickers located in 
[/freqtrade/tests/testdata](https://github.com/gcarq/freqtrade/tree/develop/freqtrade/tests/testdata).
If the 5 min and 1 min ticker for the crypto-currencies to test is not 
already in the `testdata` folder, backtesting will download them 
automatically. Testdata files will not be updated until your specify it.

### Run a backtesting against the currencies listed in your config file
**With 5 min tickers (Per default)**
```bash
python3 ./freqtrade/main.py -c config.json backtesting --realistic-simulation
```

**With 1 min tickers**
```bash
python3 ./freqtrade/main.py -c config.json backtesting --realistic-simulation --ticker-interval 1
```

**Reload your testdata files**
```bash
python3 ./freqtrade/main.py -c config.json backtesting --realistic-simulation --refresh-pairs-cached
```

**With live data (do not alter your testdata files)**
```bash
python3 ./freqtrade/main.py -c config.json backtesting --realistic-simulation --live
```

## Find optimal parameters with Hyperopt
*To be completed, please feel free to complete this section.*

## Show your buy strategy on a graph
*To be completed, please feel free to complete this section.*

## Next step
Now you have a perfect bot and want to control it from Telegram. Your
next step is to learn [Telegram usage](https://github.com/gcarq/freqtrade/blob/develop/docs/telegram-usage.md).