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@ -29,25 +29,25 @@ The backtesting is very easy with freqtrade.
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#### With 5 min tickers (Per default)
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```bash
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python3 ./freqtrade/main.py backtesting --realistic-simulation
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python3 ./freqtrade/main.py backtesting
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```
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#### With 1 min tickers
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```bash
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python3 ./freqtrade/main.py backtesting --realistic-simulation --ticker-interval 1m
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python3 ./freqtrade/main.py backtesting --ticker-interval 1m
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```
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#### Update cached pairs with the latest data
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```bash
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python3 ./freqtrade/main.py backtesting --realistic-simulation --refresh-pairs-cached
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python3 ./freqtrade/main.py backtesting --refresh-pairs-cached
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```
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#### With live data (do not alter your testdata files)
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```bash
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python3 ./freqtrade/main.py backtesting --realistic-simulation --live
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python3 ./freqtrade/main.py backtesting --live
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```
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#### Using a different on-disk ticker-data source
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@ -126,7 +126,7 @@ optional arguments:
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-h, --help show this help message and exit
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-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
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specify ticker interval (1m, 5m, 30m, 1h, 1d)
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--realistic-simulation
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--enable-position-tracking
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Disables buying the same pair multiple times to
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simulate real world limitations
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--timerange TIMERANGE
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@ -164,7 +164,7 @@ To optimize your strategy, you can use hyperopt parameter hyperoptimization
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to find optimal parameter values for your stategy.
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```
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usage: main.py hyperopt [-h] [-i TICKER_INTERVAL] [--realistic-simulation]
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usage: main.py hyperopt [-h] [-i TICKER_INTERVAL] [--enable-position-tracking]
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[--timerange TIMERANGE] [-e INT]
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[-s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]]
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@ -172,7 +172,7 @@ optional arguments:
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-h, --help show this help message and exit
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-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
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specify ticker interval (1m, 5m, 30m, 1h, 1d)
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--realistic-simulation
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--enable-position-tracking
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Disables buying the same pair multiple times to
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simulate real world limitations
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--timerange TIMERANGE specify what timerange of data to use.
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