Updated docs for #3267
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Paul D. Mendes
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e864db1843
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@ -324,62 +324,9 @@ class Awesomestrategy(IStrategy):
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!!! Note
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If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
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### Additional data (DataProvider)
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***
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The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
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All methods return `None` in case of failure (do not raise an exception).
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Please always check the mode of operation to select the correct method to get data (samples see below).
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#### Possible options for DataProvider
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- `available_pairs` - Property with tuples listing cached pairs with their intervals (pair, interval).
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- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
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- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
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- `get_pair_dataframe(pair, timeframe)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
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- `orderbook(pair, maximum)` - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
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- `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on Market data structure.
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- `runmode` - Property containing the current runmode.
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#### Example: fetch live / historical candle (OHLCV) data for the first informative pair
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``` python
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if self.dp:
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inf_pair, inf_timeframe = self.informative_pairs()[0]
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informative = self.dp.get_pair_dataframe(pair=inf_pair,
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timeframe=inf_timeframe)
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```
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!!! Warning "Warning about backtesting"
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Be carefull when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
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for the backtesting runmode) provides the full time-range in one go,
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so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).
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!!! Warning "Warning in hyperopt"
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This option cannot currently be used during hyperopt.
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#### Orderbook
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``` python
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if self.dp:
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if self.dp.runmode.value in ('live', 'dry_run'):
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ob = self.dp.orderbook(metadata['pair'], 1)
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dataframe['best_bid'] = ob['bids'][0][0]
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dataframe['best_ask'] = ob['asks'][0][0]
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```
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!!! Warning
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The order book is not part of the historic data which means backtesting and hyperopt will not work if this
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method is used.
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#### Available Pairs
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``` python
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if self.dp:
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for pair, timeframe in self.dp.available_pairs:
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print(f"available {pair}, {timeframe}")
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```
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### Additional data (informative_pairs)
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#### Get data for non-tradeable pairs
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@ -404,6 +351,108 @@ def informative_pairs(self):
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It is however better to use resampling to longer time-intervals when possible
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to avoid hammering the exchange with too many requests and risk being blocked.
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***
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### Additional data (DataProvider)
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The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
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All methods return `None` in case of failure (do not raise an exception).
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Please always check the mode of operation to select the correct method to get data (samples see below).
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#### Possible options for DataProvider
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- [`available_pairs`](#available_pairs) - Property with tuples listing cached pairs with their intervals (pair, interval).
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- [`current_whitelist()`](#current_whitelist) - Returns a current list of whitelisted pairs. Useful for accessing dynamic whitelists (ie. VolumePairlist)
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- [`get_pair_dataframe(pair, timeframe)`](#get_pair_dataframepair-timeframe) - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
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- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
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- `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on Market data structure.
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- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
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- [`orderbook(pair, maximum)`](#orderbookpair-maximum) - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
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- `runmode` - Property containing the current runmode.
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#### Example Usages:
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#### *available_pairs*
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``` python
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if self.dp:
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for pair, timeframe in self.dp.available_pairs:
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print(f"available {pair}, {timeframe}")
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```
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#### *current_whitelist()*
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Imagine you've developed a strategy that trades the `1m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume.
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The strategy might look something like this:
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*Scan through the top 10 pairs by volume using the `VolumePairList` every minute and use a 14 day ATR to buy and sell.*
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Due to the limited available data, it's impossible to resample our `1m` candles into daily candles for use in the 14 day ATR. Most exchanges limit us to just 500 candles which effectively gives us around 1/3 of a daily candle. We need 14 days at least!
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Since we can't resample our data we will have to use an informative pair; and since our whitelist will be dynamic we don't know which pair(s) to use.
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This is where calling `self.dp.current_whitelist()` comes in handy.
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```python
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class SampleStrategy(IStrategy):
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# strategy init stuff...
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ticker_interval = '1m'
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# more strategy init stuff..
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def informative_pairs(self):
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# get access to all pairs available in whitelist.
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pairs = self.dp.current_whitelist()
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# Assign tf to each pair so they can be downloaded and cached for strategy.
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informative_pairs = [(pair, '1d') for pair in pairs]
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return informative_pairs
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def populate_indicators(self, dataframe, metadata):
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# Get the informative pair
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informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
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# Get the 14 day ATR.
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atr = ta.ATR(informative, timeperiod=14)
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# Assign the Daily atr to the 1 minute dataframe.
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dataframe['daily_atr'] = atr
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```
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#### *get_pair_dataframe(pair, timeframe)*
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``` python
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# fetch live / historical candle (OHLCV) data for the first informative pair
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if self.dp:
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inf_pair, inf_timeframe = self.informative_pairs()[0]
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informative = self.dp.get_pair_dataframe(pair=inf_pair,
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timeframe=inf_timeframe)
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```
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!!! Warning "Warning about backtesting"
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Be carefull when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
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for the backtesting runmode) provides the full time-range in one go,
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so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).
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!!! Warning "Warning in hyperopt"
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This option cannot currently be used during hyperopt.
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#### *orderbook(pair, maximum)*
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``` python
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if self.dp:
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if self.dp.runmode.value in ('live', 'dry_run'):
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ob = self.dp.orderbook(metadata['pair'], 1)
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dataframe['best_bid'] = ob['bids'][0][0]
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dataframe['best_ask'] = ob['asks'][0][0]
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```
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!!! Warning
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The order book is not part of the historic data which means backtesting and hyperopt will not work if this
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method is used.
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***
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### Additional data (Wallets)
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The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
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@ -426,6 +475,7 @@ if self.wallets:
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- `get_used(asset)` - currently tied up balance (open orders)
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- `get_total(asset)` - total available balance - sum of the 2 above
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***
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### Additional data (Trades)
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A history of Trades can be retrieved in the strategy by querying the database.
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