Merge pull request #5 from Corfucinas/sourcery/pull-4
Update fork (refactored)
This commit is contained in:
Pedro Torres
GitHub
commit
48ea6b3dd2
@ -108,7 +108,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
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| `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> **Datatype:** Boolean
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| `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
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| `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
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| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Intege
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| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
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| `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
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| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
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| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
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@ -248,14 +248,14 @@ git clone https://github.com/freqtrade/freqtrade.git
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Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
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As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.17‑cp36‑cp36m‑win32.whl` (make sure to use the version matching your python version)
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As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version)
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```cmd
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>cd \path\freqtrade-develop
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>python -m venv .env
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>.env\Scripts\activate.bat
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REM optionally install ta-lib from wheel
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REM >pip install TA_Lib‑0.4.17‑cp36‑cp36m‑win32.whl
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REM >pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl
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>pip install -r requirements.txt
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>pip install -e .
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>freqtrade
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@ -324,67 +324,14 @@ 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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Data for additional, informative pairs (reference pairs) can be beneficial for some strategies.
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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).
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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 below).
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These parts will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting.
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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.
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@ -404,6 +351,107 @@ 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 `5m` 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 5 minutes and use a 14 day ATR to buy and sell.*
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Due to the limited available data, it's very difficult to resample our `5m` candles into daily candles for use in a 14 day ATR. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. 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 = '5m'
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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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# Do other stuff
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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 +474,7 @@ 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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@ -10,6 +10,7 @@ from typing import Any, Dict, List, Optional, Tuple
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from pandas import DataFrame
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from freqtrade.data.history import load_pair_history
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from freqtrade.exceptions import OperationalException
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from freqtrade.exchange import Exchange
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from freqtrade.state import RunMode
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@ -18,9 +19,10 @@ logger = logging.getLogger(__name__)
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class DataProvider:
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def __init__(self, config: dict, exchange: Exchange) -> None:
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def __init__(self, config: dict, exchange: Exchange, pairlists=None) -> None:
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self._config = config
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self._exchange = exchange
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self._pairlists = pairlists
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def refresh(self,
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pairlist: List[Tuple[str, str]],
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@ -116,3 +118,17 @@ class DataProvider:
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can be "live", "dry-run", "backtest", "edgecli", "hyperopt" or "other".
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"""
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return RunMode(self._config.get('runmode', RunMode.OTHER))
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def current_whitelist(self) -> List[str]:
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"""
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fetch latest available whitelist.
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Useful when you have a large whitelist and need to call each pair as an informative pair.
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As available pairs does not show whitelist until after informative pairs have been cached.
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:return: list of pairs in whitelist
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"""
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if self._pairlists:
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return self._pairlists.whitelist
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else:
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raise OperationalException("Dataprovider was not initialized with a pairlist provider.")
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@ -71,15 +71,15 @@ class FreqtradeBot:
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self.wallets = Wallets(self.config, self.exchange)
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self.dataprovider = DataProvider(self.config, self.exchange)
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self.pairlists = PairListManager(self.exchange, self.config)
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self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists)
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# Attach Dataprovider to Strategy baseclass
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IStrategy.dp = self.dataprovider
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# Attach Wallets to Strategy baseclass
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IStrategy.wallets = self.wallets
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self.pairlists = PairListManager(self.exchange, self.config)
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# Initializing Edge only if enabled
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self.edge = Edge(self.config, self.exchange, self.strategy) if \
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self.config.get('edge', {}).get('enabled', False) else None
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@ -946,32 +946,31 @@ class FreqtradeBot:
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:return: Reason for cancel
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"""
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# if trade is not partially completed, just cancel the trade
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if order['remaining'] == order['amount'] or order.get('filled') == 0.0:
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if not self.exchange.check_order_canceled_empty(order):
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reason = "cancelled due to timeout"
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try:
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# if trade is not partially completed, just delete the trade
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self.exchange.cancel_order(trade.open_order_id, trade.pair)
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except InvalidOrderException:
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logger.exception(f"Could not cancel sell order {trade.open_order_id}")
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return 'error cancelling order'
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logger.info('Sell order %s for %s.', reason, trade)
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else:
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reason = "cancelled on exchange"
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logger.info('Sell order %s for %s.', reason, trade)
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if not (
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order['remaining'] == order['amount'] or order.get('filled') == 0.0
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):
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# TODO: figure out how to handle partially complete sell orders
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return 'partially filled - keeping order open'
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if self.exchange.check_order_canceled_empty(order):
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reason = "cancelled on exchange"
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else:
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reason = "cancelled due to timeout"
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try:
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# if trade is not partially completed, just delete the trade
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self.exchange.cancel_order(trade.open_order_id, trade.pair)
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except InvalidOrderException:
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logger.exception(f"Could not cancel sell order {trade.open_order_id}")
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return 'error cancelling order'
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logger.info('Sell order %s for %s.', reason, trade)
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trade.close_rate = None
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trade.close_rate_requested = None
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trade.close_profit = None
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trade.close_profit_abs = None
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trade.close_date = None
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trade.is_open = True
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trade.open_order_id = None
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trade.close_rate = None
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trade.close_rate_requested = None
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trade.close_profit = None
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trade.close_profit_abs = None
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trade.close_date = None
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trade.is_open = True
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trade.open_order_id = None
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return reason
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# TODO: figure out how to handle partially complete sell orders
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return 'partially filled - keeping order open'
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return reason
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def _safe_sell_amount(self, pair: str, amount: float) -> float:
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"""
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@ -1,8 +1,11 @@
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from unittest.mock import MagicMock
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from pandas import DataFrame
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import pytest
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.pairlist.pairlistmanager import PairListManager
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from freqtrade.exceptions import OperationalException
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from freqtrade.state import RunMode
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from tests.conftest import get_patched_exchange
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@ -64,8 +67,8 @@ def test_get_pair_dataframe(mocker, default_conf, ohlcv_history):
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assert dp.get_pair_dataframe("NONESENSE/AAA", ticker_interval).empty
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# Test with and without parameter
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assert dp.get_pair_dataframe("UNITTEST/BTC",
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ticker_interval).equals(dp.get_pair_dataframe("UNITTEST/BTC"))
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assert dp.get_pair_dataframe("UNITTEST/BTC", ticker_interval)\
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.equals(dp.get_pair_dataframe("UNITTEST/BTC"))
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default_conf["runmode"] = RunMode.LIVE
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dp = DataProvider(default_conf, exchange)
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@ -90,10 +93,7 @@ def test_available_pairs(mocker, default_conf, ohlcv_history):
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dp = DataProvider(default_conf, exchange)
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assert len(dp.available_pairs) == 2
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assert dp.available_pairs == [
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("XRP/BTC", ticker_interval),
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("UNITTEST/BTC", ticker_interval),
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]
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assert dp.available_pairs == [("XRP/BTC", ticker_interval), ("UNITTEST/BTC", ticker_interval), ]
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def test_refresh(mocker, default_conf, ohlcv_history):
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@ -152,3 +152,27 @@ def test_market(mocker, default_conf, markets):
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res = dp.market('UNITTEST/BTC')
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assert res is None
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def test_current_whitelist(mocker, default_conf, tickers):
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# patch default conf to volumepairlist
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default_conf['pairlists'][0] = {'method': 'VolumePairList', "number_assets": 5}
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mocker.patch.multiple('freqtrade.exchange.Exchange',
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exchange_has=MagicMock(return_value=True),
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get_tickers=tickers)
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exchange = get_patched_exchange(mocker, default_conf)
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pairlist = PairListManager(exchange, default_conf)
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dp = DataProvider(default_conf, exchange, pairlist)
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# Simulate volumepairs from exchange.
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pairlist.refresh_pairlist()
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assert dp.current_whitelist() == pairlist._whitelist
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# The identity of the 2 lists should be identical
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assert dp.current_whitelist() is pairlist._whitelist
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with pytest.raises(OperationalException):
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dp = DataProvider(default_conf, exchange)
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dp.current_whitelist()
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