@ -324,6 +324,35 @@ class Awesomestrategy(IStrategy):
!!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
***
### Additional data (informative_pairs)
#### Get data for non-tradeable pairs
Data for additional, informative pairs (reference pairs) can be beneficial for some strategies.
Ohlcv data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see above).
These parts will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting.
The pairs need to be specified as tuples in the format `("pair", "interval")`, with pair as the first and time interval as the second argument.
Sample:
``` python
def informative_pairs(self):
return [("ETH/USDT", "5m"),
("BTC/TUSD", "15m"),
]
```
!!! Warning
As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short.
All intervals and all pairs can be specified as long as they are available (and active) on the used exchange.
It is however better to use resampling to longer time-intervals when possible
to avoid hammering the exchange with too many requests and risk being blocked.
***
### Additional data (DataProvider)
The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
@ -334,17 +363,67 @@ Please always check the mode of operation to select the correct method to get da
#### Possible options for DataProvider
- `available_pairs` - Property with tuples listing cached pairs with their intervals (pair, interval).
- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
- [`available_pairs`](#available_pairs) - Property with tuples listing cached pairs with their intervals (pair, interval).
- [`current_whitelist()`](#current_whitelist) - Returns a current list of whitelisted pairs. Useful for accessing dynamic whitelists (ie. VolumePairlist)
- [`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).
- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
- `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).
- `orderbook(pair, maximum)` - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
- `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.
- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
- [`orderbook(pair, maximum)`](#orderbookpair-maximum) - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
- `runmode` - Property containing the current runmode.
#### Example: fetch live / historical candle (OHLCV) data for the first informative pair
#### Example Usages:
#### *available_pairs*
``` python
if self.dp:
for pair, timeframe in self.dp.available_pairs:
print(f"available {pair}, {timeframe}")
```
#### *current_whitelist()*
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.
The strategy might look something like this:
*Scan through the top 10 pairs by volume using the `VolumePairList` every minute and use a 14 day ATR to buy and sell.*
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!
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.
This is where calling `self.dp.current_whitelist()` comes in handy.
```python
class SampleStrategy(IStrategy):
# strategy init stuff...
ticker_interval = '1m'
# more strategy init stuff..
def informative_pairs(self):
# get access to all pairs available in whitelist.
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1d') for pair in pairs]
This option cannot currently be used during hyperopt.
#### Orderbook
#### *orderbook(pair, maximum)*
``` python
if self.dp:
@ -373,37 +452,7 @@ if self.dp:
The order book is not part of the historic data which means backtesting and hyperopt will not work if this
method is used.
#### Available Pairs
``` python
if self.dp:
for pair, timeframe in self.dp.available_pairs:
print(f"available {pair}, {timeframe}")
```
#### Get data for non-tradeable pairs
Data for additional, informative pairs (reference pairs) can be beneficial for some strategies.
Ohlcv data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see above).
These parts will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting.
The pairs need to be specified as tuples in the format `("pair", "interval")`, with pair as the first and time interval as the second argument.
Sample:
``` python
def informative_pairs(self):
return [("ETH/USDT", "5m"),
("BTC/TUSD", "15m"),
]
```
!!! Warning
As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short.
All intervals and all pairs can be specified as long as they are available (and active) on the used exchange.
It is however better to use resampling to longer time-intervals when possible
to avoid hammering the exchange with too many requests and risk being blocked.
***
### Additional data (Wallets)
The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
@ -426,6 +475,7 @@ if self.wallets:
- `get_used(asset)` - currently tied up balance (open orders)
- `get_total(asset)` - total available balance - sum of the 2 above
***
### Additional data (Trades)
A history of Trades can be retrieved in the strategy by querying the database.
Paul D. Mendes
6 years ago