Merge remote-tracking branch 'origin/develop' into dev-merge-rl
This commit is contained in:
robcaulk
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@@ -225,14 +225,16 @@ Mandatory parameters are marked as **Required**, which means that they are requi
|
||||
| `webhook.webhookexitcancel` | Payload to send on exit order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.webhookexitfill` | Payload to send on exit order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
|
||||
| | **Rest API / FreqUI**
|
||||
| | **Rest API / FreqUI / Producer-Consumer**
|
||||
| `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
|
||||
| `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4
|
||||
| `api_server.listen_port` | Bind Port. See the [API Server documentation](rest-api.md) for more details. <br>**Datatype:** Integer between 1024 and 65535
|
||||
| `api_server.verbosity` | Logging verbosity. `info` will print all RPC Calls, while "error" will only display errors. <br>**Datatype:** Enum, either `info` or `error`. Defaults to `info`.
|
||||
| `api_server.username` | Username for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
|
||||
| `api_server.password` | Password for API server. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.**<br> **Datatype:** String
|
||||
| `api_server.ws_token` | API token for the Message WebSocket. See the [API Server documentation](rest-api.md) for more details. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
|
||||
| `bot_name` | Name of the bot. Passed via API to a client - can be shown to distinguish / name bots.<br> *Defaults to `freqtrade`*<br> **Datatype:** String
|
||||
| `external_message_consumer` | Enable [Producer/Consumer mode](producer-consumer.md) for more details. <br> **Datatype:** Dict
|
||||
| | **Other**
|
||||
| `initial_state` | Defines the initial application state. If set to stopped, then the bot has to be explicitly started via `/start` RPC command. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
|
||||
| `force_entry_enable` | Enables the RPC Commands to force a Trade entry. More information below. <br> **Datatype:** Boolean
|
||||
|
||||
@@ -26,7 +26,7 @@ usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
|
||||
[--timerange TIMERANGE] [--dl-trades]
|
||||
[--exchange EXCHANGE]
|
||||
[-t TIMEFRAMES [TIMEFRAMES ...]] [--erase]
|
||||
[--data-format-ohlcv {json,jsongz,hdf5}]
|
||||
[--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
|
||||
[--data-format-trades {json,jsongz,hdf5}]
|
||||
[--trading-mode {spot,margin,futures}]
|
||||
[--prepend]
|
||||
@@ -55,7 +55,7 @@ optional arguments:
|
||||
list. Default: `1m 5m`.
|
||||
--erase Clean all existing data for the selected
|
||||
exchange/pairs/timeframes.
|
||||
--data-format-ohlcv {json,jsongz,hdf5}
|
||||
--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
|
||||
Storage format for downloaded candle (OHLCV) data.
|
||||
(default: `json`).
|
||||
--data-format-trades {json,jsongz,hdf5}
|
||||
@@ -76,7 +76,7 @@ Common arguments:
|
||||
`userdir/config.json` or `config.json` whichever
|
||||
exists). Multiple --config options may be used. Can be
|
||||
set to `-` to read config from stdin.
|
||||
-d PATH, --datadir PATH
|
||||
-d PATH, --datadir PATH, --data-dir PATH
|
||||
Path to directory with historical backtesting data.
|
||||
--userdir PATH, --user-data-dir PATH
|
||||
Path to userdata directory.
|
||||
@@ -179,9 +179,11 @@ freqtrade download-data --exchange binance --pairs ETH/USDT XRP/USDT BTC/USDT --
|
||||
|
||||
Freqtrade currently supports 3 data-formats for both OHLCV and trades data:
|
||||
|
||||
* `json` (plain "text" json files)
|
||||
* `jsongz` (a gzip-zipped version of json files)
|
||||
* `hdf5` (a high performance datastore)
|
||||
* `json` - plain "text" json files
|
||||
* `jsongz` - a gzip-zipped version of json files
|
||||
* `hdf5` - a high performance datastore
|
||||
* `feather` - a dataformat based on Apache Arrow
|
||||
* `parquet` - columnar datastore
|
||||
|
||||
By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
|
||||
|
||||
@@ -200,38 +202,74 @@ If the default data-format has been changed during download, then the keys `data
|
||||
!!! Note
|
||||
You can convert between data-formats using the [convert-data](#sub-command-convert-data) and [convert-trade-data](#sub-command-convert-trade-data) methods.
|
||||
|
||||
#### Dataformat comparison
|
||||
|
||||
The following comparisons have been made with the following data, and by using the linux `time` command.
|
||||
|
||||
```
|
||||
Found 6 pair / timeframe combinations.
|
||||
+----------+-------------+--------+---------------------+---------------------+
|
||||
| Pair | Timeframe | Type | From | To |
|
||||
|----------+-------------+--------+---------------------+---------------------|
|
||||
| BTC/USDT | 5m | spot | 2017-08-17 04:00:00 | 2022-09-13 19:25:00 |
|
||||
| ETH/USDT | 1m | spot | 2017-08-17 04:00:00 | 2022-09-13 19:26:00 |
|
||||
| BTC/USDT | 1m | spot | 2017-08-17 04:00:00 | 2022-09-13 19:30:00 |
|
||||
| XRP/USDT | 5m | spot | 2018-05-04 08:10:00 | 2022-09-13 19:15:00 |
|
||||
| XRP/USDT | 1m | spot | 2018-05-04 08:11:00 | 2022-09-13 19:22:00 |
|
||||
| ETH/USDT | 5m | spot | 2017-08-17 04:00:00 | 2022-09-13 19:20:00 |
|
||||
+----------+-------------+--------+---------------------+---------------------+
|
||||
```
|
||||
|
||||
Timings have been taken in a not very scientific way with the following command, which forces reading the data into memory.
|
||||
|
||||
``` bash
|
||||
time freqtrade list-data --show-timerange --data-format-ohlcv <dataformat>
|
||||
```
|
||||
|
||||
| Format | Size | timing |
|
||||
|------------|-------------|-------------|
|
||||
| `json` | 149Mb | 25.6s |
|
||||
| `jsongz` | 39Mb | 27s |
|
||||
| `hdf5` | 145Mb | 3.9s |
|
||||
| `feather` | 72Mb | 3.5s |
|
||||
| `parquet` | 83Mb | 3.8s |
|
||||
|
||||
Size has been taken from the BTC/USDT 1m spot combination for the timerange specified above.
|
||||
|
||||
To have a best performance/size mix, we recommend the use of either feather or parquet.
|
||||
|
||||
#### Sub-command convert data
|
||||
|
||||
```
|
||||
usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
|
||||
[-d PATH] [--userdir PATH]
|
||||
[-p PAIRS [PAIRS ...]] --format-from
|
||||
{json,jsongz,hdf5} --format-to
|
||||
{json,jsongz,hdf5} [--erase]
|
||||
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
|
||||
{json,jsongz,hdf5,feather,parquet} --format-to
|
||||
{json,jsongz,hdf5,feather,parquet} [--erase]
|
||||
[--exchange EXCHANGE]
|
||||
[-t TIMEFRAMES [TIMEFRAMES ...]]
|
||||
[--trading-mode {spot,margin,futures}]
|
||||
[--candle-types {spot,,futures,mark,index,premiumIndex,funding_rate} [{spot,,futures,mark,index,premiumIndex,funding_rate} ...]]
|
||||
[--candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]]
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
|
||||
Limit command to these pairs. Pairs are space-
|
||||
separated.
|
||||
--format-from {json,jsongz,hdf5}
|
||||
--format-from {json,jsongz,hdf5,feather,parquet}
|
||||
Source format for data conversion.
|
||||
--format-to {json,jsongz,hdf5}
|
||||
--format-to {json,jsongz,hdf5,feather,parquet}
|
||||
Destination format for data conversion.
|
||||
--erase Clean all existing data for the selected
|
||||
exchange/pairs/timeframes.
|
||||
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
|
||||
Specify which tickers to download. Space-separated
|
||||
list. Default: `1m 5m`.
|
||||
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
|
||||
config is provided.
|
||||
--trading-mode {spot,margin,futures}
|
||||
-t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
|
||||
Specify which tickers to download. Space-separated
|
||||
list. Default: `1m 5m`.
|
||||
--trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
|
||||
Select Trading mode
|
||||
--candle-types {spot,,futures,mark,index,premiumIndex,funding_rate} [{spot,,futures,mark,index,premiumIndex,funding_rate} ...]
|
||||
--candle-types {spot,futures,mark,index,premiumIndex,funding_rate} [{spot,futures,mark,index,premiumIndex,funding_rate} ...]
|
||||
Select candle type to use
|
||||
|
||||
Common arguments:
|
||||
@@ -245,7 +283,7 @@ Common arguments:
|
||||
`userdir/config.json` or `config.json` whichever
|
||||
exists). Multiple --config options may be used. Can be
|
||||
set to `-` to read config from stdin.
|
||||
-d PATH, --datadir PATH
|
||||
-d PATH, --datadir PATH, --data-dir PATH
|
||||
Path to directory with historical backtesting data.
|
||||
--userdir PATH, --user-data-dir PATH
|
||||
Path to userdata directory.
|
||||
@@ -267,20 +305,24 @@ freqtrade convert-data --format-from json --format-to jsongz --datadir ~/.freqtr
|
||||
usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
|
||||
[-d PATH] [--userdir PATH]
|
||||
[-p PAIRS [PAIRS ...]] --format-from
|
||||
{json,jsongz,hdf5} --format-to
|
||||
{json,jsongz,hdf5} [--erase]
|
||||
{json,jsongz,hdf5,feather,parquet}
|
||||
--format-to
|
||||
{json,jsongz,hdf5,feather,parquet}
|
||||
[--erase] [--exchange EXCHANGE]
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
|
||||
Show profits for only these pairs. Pairs are space-
|
||||
Limit command to these pairs. Pairs are space-
|
||||
separated.
|
||||
--format-from {json,jsongz,hdf5}
|
||||
--format-from {json,jsongz,hdf5,feather,parquet}
|
||||
Source format for data conversion.
|
||||
--format-to {json,jsongz,hdf5}
|
||||
--format-to {json,jsongz,hdf5,feather,parquet}
|
||||
Destination format for data conversion.
|
||||
--erase Clean all existing data for the selected
|
||||
exchange/pairs/timeframes.
|
||||
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
|
||||
config is provided.
|
||||
|
||||
Common arguments:
|
||||
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
|
||||
@@ -293,7 +335,7 @@ Common arguments:
|
||||
`userdir/config.json` or `config.json` whichever
|
||||
exists). Multiple --config options may be used. Can be
|
||||
set to `-` to read config from stdin.
|
||||
-d PATH, --datadir PATH
|
||||
-d PATH, --datadir PATH, --data-dir PATH
|
||||
Path to directory with historical backtesting data.
|
||||
--userdir PATH, --user-data-dir PATH
|
||||
Path to userdata directory.
|
||||
@@ -318,9 +360,9 @@ This command will allow you to repeat this last step for additional timeframes w
|
||||
usage: freqtrade trades-to-ohlcv [-h] [-v] [--logfile FILE] [-V] [-c PATH]
|
||||
[-d PATH] [--userdir PATH]
|
||||
[-p PAIRS [PAIRS ...]]
|
||||
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
|
||||
[-t TIMEFRAMES [TIMEFRAMES ...]]
|
||||
[--exchange EXCHANGE]
|
||||
[--data-format-ohlcv {json,jsongz,hdf5}]
|
||||
[--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
|
||||
[--data-format-trades {json,jsongz,hdf5}]
|
||||
|
||||
optional arguments:
|
||||
@@ -328,12 +370,12 @@ optional arguments:
|
||||
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
|
||||
Limit command to these pairs. Pairs are space-
|
||||
separated.
|
||||
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
|
||||
-t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
|
||||
Specify which tickers to download. Space-separated
|
||||
list. Default: `1m 5m`.
|
||||
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
|
||||
config is provided.
|
||||
--data-format-ohlcv {json,jsongz,hdf5}
|
||||
--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
|
||||
Storage format for downloaded candle (OHLCV) data.
|
||||
(default: `json`).
|
||||
--data-format-trades {json,jsongz,hdf5}
|
||||
@@ -351,7 +393,7 @@ Common arguments:
|
||||
`userdir/config.json` or `config.json` whichever
|
||||
exists). Multiple --config options may be used. Can be
|
||||
set to `-` to read config from stdin.
|
||||
-d PATH, --datadir PATH
|
||||
-d PATH, --datadir PATH, --data-dir PATH
|
||||
Path to directory with historical backtesting data.
|
||||
--userdir PATH, --user-data-dir PATH
|
||||
Path to userdata directory.
|
||||
@@ -371,7 +413,7 @@ You can get a list of downloaded data using the `list-data` sub-command.
|
||||
```
|
||||
usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
|
||||
[--userdir PATH] [--exchange EXCHANGE]
|
||||
[--data-format-ohlcv {json,jsongz,hdf5}]
|
||||
[--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}]
|
||||
[-p PAIRS [PAIRS ...]]
|
||||
[--trading-mode {spot,margin,futures}]
|
||||
[--show-timerange]
|
||||
@@ -380,13 +422,13 @@ optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
|
||||
config is provided.
|
||||
--data-format-ohlcv {json,jsongz,hdf5}
|
||||
--data-format-ohlcv {json,jsongz,hdf5,feather,parquet}
|
||||
Storage format for downloaded candle (OHLCV) data.
|
||||
(default: `json`).
|
||||
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
|
||||
Limit command to these pairs. Pairs are space-
|
||||
separated.
|
||||
--trading-mode {spot,margin,futures}
|
||||
--trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
|
||||
Select Trading mode
|
||||
--show-timerange Show timerange available for available data. (May take
|
||||
a while to calculate).
|
||||
@@ -402,7 +444,7 @@ Common arguments:
|
||||
`userdir/config.json` or `config.json` whichever
|
||||
exists). Multiple --config options may be used. Can be
|
||||
set to `-` to read config from stdin.
|
||||
-d PATH, --datadir PATH
|
||||
-d PATH, --datadir PATH, --data-dir PATH
|
||||
Path to directory with historical backtesting data.
|
||||
--userdir PATH, --user-data-dir PATH
|
||||
Path to userdata directory.
|
||||
|
||||
@@ -60,11 +60,18 @@ Binance supports [time_in_force](configuration.md#understand-order_time_in_force
|
||||
Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
|
||||
On futures, Binance supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
|
||||
|
||||
### Binance Blacklist
|
||||
### Binance Blacklist recommendation
|
||||
|
||||
For Binance, it is suggested to add `"BNB/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `BNB` on the account or unless you're willing to disable using `BNB` for fees.
|
||||
Binance accounts may use `BNB` for fees, and if a trade happens to be on `BNB`, further trades may consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
|
||||
|
||||
### Binance sites
|
||||
|
||||
Binance has been split into 2, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
|
||||
|
||||
* [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
|
||||
* [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
|
||||
|
||||
### Binance Futures
|
||||
|
||||
Binance has specific (unfortunately complex) [Futures Trading Quantitative Rules](https://www.binance.com/en/support/faq/4f462ebe6ff445d4a170be7d9e897272) which need to be followed, and which prohibit a too low stake-amount (among others) for too many orders.
|
||||
@@ -87,12 +94,14 @@ When trading on Binance Futures market, orderbook must be used because there is
|
||||
},
|
||||
```
|
||||
|
||||
### Binance sites
|
||||
#### Binance futures settings
|
||||
|
||||
Binance has been split into 2, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
|
||||
Users will also have to have the futures-setting "Position Mode" set to "One-way Mode", and "Asset Mode" set to "Single-Asset Mode".
|
||||
These settings will be checked on startup, and freqtrade will show an error if this setting is wrong.
|
||||
|
||||
* [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
|
||||
* [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
|
||||
|
||||
|
||||
Freqtrade will not attempt to change these settings.
|
||||
|
||||
## Kraken
|
||||
|
||||
|
||||
217
docs/freqai-configuration.md
Normal file
217
docs/freqai-configuration.md
Normal file
@@ -0,0 +1,217 @@
|
||||
# Configuration
|
||||
|
||||
`FreqAI` is configured through the typical [Freqtrade config file](configuration.md) and the standard [Freqtrade strategy](strategy-customization.md). Examples of `FreqAI` config and strategy files can be found in `config_examples/config_freqai.example.json` and `freqtrade/templates/FreqaiExampleStrategy.py`, respectively.
|
||||
|
||||
## Setting up the configuration file
|
||||
|
||||
Although there are plenty of additional parameters to choose from, as highlighted in the [parameter table](freqai-parameter-table.md#parameter-table), a `FreqAI` config must at minimum include the following parameters (the parameter values are only examples):
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"enabled": true,
|
||||
"purge_old_models": true,
|
||||
"train_period_days": 30,
|
||||
"backtest_period_days": 7,
|
||||
"identifier" : "unique-id",
|
||||
"feature_parameters" : {
|
||||
"include_timeframes": ["5m","15m","4h"],
|
||||
"include_corr_pairlist": [
|
||||
"ETH/USD",
|
||||
"LINK/USD",
|
||||
"BNB/USD"
|
||||
],
|
||||
"label_period_candles": 24,
|
||||
"include_shifted_candles": 2,
|
||||
"indicator_periods_candles": [10, 20]
|
||||
},
|
||||
"data_split_parameters" : {
|
||||
"test_size": 0.25
|
||||
},
|
||||
"model_training_parameters" : {
|
||||
"n_estimators": 100
|
||||
},
|
||||
}
|
||||
```
|
||||
|
||||
A full example config is available in `config_examples/config_freqai.example.json`.
|
||||
|
||||
## Building a `FreqAI` strategy
|
||||
|
||||
The `FreqAI` strategy requires including the following lines of code in the standard [Freqtrade strategy](strategy-customization.md):
|
||||
|
||||
```python
|
||||
# user should define the maximum startup candle count (the largest number of candles
|
||||
# passed to any single indicator)
|
||||
startup_candle_count: int = 20
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
# the model will return all labels created by user in `populate_any_indicators`
|
||||
# (& appended targets), an indication of whether or not the prediction should be accepted,
|
||||
# the target mean/std values for each of the labels created by user in
|
||||
# `populate_any_indicators()` for each training period.
|
||||
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
"""
|
||||
Function designed to automatically generate, name and merge features
|
||||
from user indicated timeframes in the configuration file. User controls the indicators
|
||||
passed to the training/prediction by prepending indicators with `'%-' + coin `
|
||||
(see convention below). I.e. user should not prepend any supporting metrics
|
||||
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
|
||||
model.
|
||||
:param pair: pair to be used as informative
|
||||
:param df: strategy dataframe which will receive merges from informatives
|
||||
:param tf: timeframe of the dataframe which will modify the feature names
|
||||
:param informative: the dataframe associated with the informative pair
|
||||
:param coin: the name of the coin which will modify the feature names.
|
||||
"""
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
if informative is None:
|
||||
informative = self.dp.get_pair_dataframe(pair, tf)
|
||||
|
||||
# first loop is automatically duplicating indicators for time periods
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
|
||||
t = int(t)
|
||||
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
|
||||
indicators = [col for col in informative if col.startswith("%")]
|
||||
# This loop duplicates and shifts all indicators to add a sense of recency to data
|
||||
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
|
||||
if n == 0:
|
||||
continue
|
||||
informative_shift = informative[indicators].shift(n)
|
||||
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
|
||||
informative = pd.concat((informative, informative_shift), axis=1)
|
||||
|
||||
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
|
||||
skip_columns = [
|
||||
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
|
||||
]
|
||||
df = df.drop(columns=skip_columns)
|
||||
|
||||
# Add generalized indicators here (because in live, it will call this
|
||||
# function to populate indicators during training). Notice how we ensure not to
|
||||
# add them multiple times
|
||||
if set_generalized_indicators:
|
||||
|
||||
# user adds targets here by prepending them with &- (see convention below)
|
||||
# If user wishes to use multiple targets, a multioutput prediction model
|
||||
# needs to be used such as templates/CatboostPredictionMultiModel.py
|
||||
df["&-s_close"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.mean()
|
||||
/ df["close"]
|
||||
- 1
|
||||
)
|
||||
|
||||
return df
|
||||
|
||||
|
||||
```
|
||||
|
||||
Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
|
||||
|
||||
Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`.
|
||||
|
||||
!!! Note
|
||||
The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
|
||||
|
||||
!!! Note
|
||||
Features **must** be defined in `populate_any_indicators()`. Defining `FreqAI` features in `populate_indicators()`
|
||||
will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, the following structure inside `populate_any_indicators()` should be used
|
||||
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
|
||||
|
||||
```python
|
||||
def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False):
|
||||
|
||||
...
|
||||
|
||||
# Add generalized indicators here (because in live, it will call only this function to populate
|
||||
# indicators for retraining). Notice how we ensure not to add them multiple times by associating
|
||||
# these generalized indicators to the basepair/timeframe
|
||||
if set_generalized_indicators:
|
||||
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
|
||||
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
|
||||
|
||||
# user adds targets here by prepending them with &- (see convention below)
|
||||
# If user wishes to use multiple targets, a multioutput prediction model
|
||||
# needs to be used such as templates/CatboostPredictionMultiModel.py
|
||||
df["&-s_close"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.mean()
|
||||
/ df["close"]
|
||||
- 1
|
||||
)
|
||||
```
|
||||
|
||||
Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.
|
||||
|
||||
## Important dataframe key patterns
|
||||
|
||||
Below are the values you can expect to include/use inside a typical strategy dataframe (`df[]`):
|
||||
|
||||
| DataFrame Key | Description |
|
||||
|------------|-------------|
|
||||
| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside `FreqAI` (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back as the predictions. For example, to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), you would set `df['&-s_close']`. `FreqAI` makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
|
||||
| `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
|
||||
| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, `FreqAI` will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -1 and 2.
|
||||
| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence `FreqAI` has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
|
||||
| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from `FreqAI`. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
|
||||
|
||||
## Setting the `startup_candle_count`
|
||||
|
||||
The `startup_candle_count` in the `FreqAI` strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., Ta-Lib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`.
|
||||
|
||||
!!! Note
|
||||
There are instances where the Ta-Lib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean:
|
||||
|
||||
```
|
||||
2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319.
|
||||
```
|
||||
|
||||
## Creating a dynamic target threshold
|
||||
|
||||
Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. `FreqAI` allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.
|
||||
|
||||
```python
|
||||
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
|
||||
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
|
||||
```
|
||||
|
||||
To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_prediction_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"fit_live_prediction_candles": 300,
|
||||
}
|
||||
```
|
||||
|
||||
If this value is set, `FreqAI` will initially use the predictions from the training data and subsequently begin introducing real prediction data as it is generated. `FreqAI` will save this historical data to be reloaded if you stop and restart a model with the same `identifier`.
|
||||
|
||||
## Using different prediction models
|
||||
|
||||
`FreqAI` has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `Catboost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`. However, it is possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of the training procedures.
|
||||
|
||||
### Setting classifier targets
|
||||
|
||||
`FreqAI` includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example:
|
||||
|
||||
```python
|
||||
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
|
||||
```
|
||||
|
||||
Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column.
|
||||
78
docs/freqai-developers.md
Normal file
78
docs/freqai-developers.md
Normal file
@@ -0,0 +1,78 @@
|
||||
# Development
|
||||
|
||||
## Project architecture
|
||||
|
||||
The architecture and functions of `FreqAI` are generalized to encourages development of unique features, functions, models, etc.
|
||||
|
||||
The class structure and a detailed algorithmic overview is depicted in the following diagram:
|
||||
|
||||
|
||||
|
||||
As shown, there are three distinct objects comprising `FreqAI`:
|
||||
|
||||
* **IFreqaiModel** - A singular persistent object containing all the necessary logic to collect, store, and process data, engineer features, run training, and inference models.
|
||||
* **FreqaiDataKitchen** - A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools.
|
||||
* **FreqaiDataDrawer** - A singular persistent object containing all the historical predictions, models, and save/load methods.
|
||||
|
||||
There are a variety of built-in [prediction models](freqai-configuration.md#using-different-prediction-models) which inherit directly from `IFreqaiModel`. Each of these models have full access to all methods in `IFreqaiModel` and can therefore override any of those functions at will. However, advanced users will likely stick to overriding `fit()`, `train()`, `predict()`, and `data_cleaning_train/predict()`.
|
||||
|
||||
## Data handling
|
||||
|
||||
`FreqAI` aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash resilience by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified.
|
||||
|
||||
### File structure
|
||||
|
||||
The file structure is automatically generated based on the model `identifier` set in the [config](freqai-configuration.md#setting-up-the-configuration-file). The following structure shows where the data is stored for post processing:
|
||||
|
||||
| Structure | Description |
|
||||
|-----------|-------------|
|
||||
| `config_*.json` | A copy of the model specific configuration file. |
|
||||
| `historic_predictions.pkl` | A file containing all historic predictions generated during the lifetime of the `identifier` model during live deployment. `historic_predictions.pkl` is used to reload the model after a crash or a config change. A backup file is always held incase of corruption on the main file. **`FreqAI` automatically detects corruption and replaces the corrupted file with the backup**. |
|
||||
| `pair_dictionary.json` | A file containing the training queue as well as the on disk location of the most recently trained model. |
|
||||
| `sub-train-*_TIMESTAMP` | A folder containing all the files associated with a single model, such as: <br>
|
||||
|| `*_metadata.json` - Metadata for the model, such as normalization max/mins, expected training feature list, etc. <br>
|
||||
|| `*_model.*` - The model file saved to disk for reloading from a crash. Can be `joblib` (typical boosting libs), `zip` (stable_baselines), `hd5` (keras type), etc. <br>
|
||||
|| `*_pca_object.pkl` - The [Principal component analysis (PCA)](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis) transform (if `principal_component_analysis: true` is set in the config) which will be used to transform unseen prediction features. <br>
|
||||
|| `*_svm_model.pkl` - The [Support Vector Machine (SVM)](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm) model which is used to detect outliers in unseen prediction features. <br>
|
||||
|| `*_trained_df.pkl` - The dataframe containing all the training features used to train the `identifier` model. This is used for computing the [Dissimilarity Index (DI)](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) and can also be used for post-processing. <br>
|
||||
|| `*_trained_dates.df.pkl` - The dates associated with the `trained_df.pkl`, which is useful for post-processing. |
|
||||
|
||||
The example file structure would look like this:
|
||||
|
||||
```
|
||||
├── models
|
||||
│ └── unique-id
|
||||
│ ├── config_freqai.example.json
|
||||
│ ├── historic_predictions.backup.pkl
|
||||
│ ├── historic_predictions.pkl
|
||||
│ ├── pair_dictionary.json
|
||||
│ ├── sub-train-1INCH_1662821319
|
||||
│ │ ├── cb_1inch_1662821319_metadata.json
|
||||
│ │ ├── cb_1inch_1662821319_model.joblib
|
||||
│ │ ├── cb_1inch_1662821319_pca_object.pkl
|
||||
│ │ ├── cb_1inch_1662821319_svm_model.joblib
|
||||
│ │ ├── cb_1inch_1662821319_trained_dates_df.pkl
|
||||
│ │ └── cb_1inch_1662821319_trained_df.pkl
|
||||
│ ├── sub-train-1INCH_1662821371
|
||||
│ │ ├── cb_1inch_1662821371_metadata.json
|
||||
│ │ ├── cb_1inch_1662821371_model.joblib
|
||||
│ │ ├── cb_1inch_1662821371_pca_object.pkl
|
||||
│ │ ├── cb_1inch_1662821371_svm_model.joblib
|
||||
│ │ ├── cb_1inch_1662821371_trained_dates_df.pkl
|
||||
│ │ └── cb_1inch_1662821371_trained_df.pkl
|
||||
│ ├── sub-train-ADA_1662821344
|
||||
│ │ ├── cb_ada_1662821344_metadata.json
|
||||
│ │ ├── cb_ada_1662821344_model.joblib
|
||||
│ │ ├── cb_ada_1662821344_pca_object.pkl
|
||||
│ │ ├── cb_ada_1662821344_svm_model.joblib
|
||||
│ │ ├── cb_ada_1662821344_trained_dates_df.pkl
|
||||
│ │ └── cb_ada_1662821344_trained_df.pkl
|
||||
│ └── sub-train-ADA_1662821399
|
||||
│ ├── cb_ada_1662821399_metadata.json
|
||||
│ ├── cb_ada_1662821399_model.joblib
|
||||
│ ├── cb_ada_1662821399_pca_object.pkl
|
||||
│ ├── cb_ada_1662821399_svm_model.joblib
|
||||
│ ├── cb_ada_1662821399_trained_dates_df.pkl
|
||||
│ └── cb_ada_1662821399_trained_df.pkl
|
||||
|
||||
```
|
||||
268
docs/freqai-feature-engineering.md
Normal file
268
docs/freqai-feature-engineering.md
Normal file
@@ -0,0 +1,268 @@
|
||||
# Feature engineering
|
||||
|
||||
## Defining the features
|
||||
|
||||
Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%`, while labels/targets are prepended with `&`.
|
||||
|
||||
Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the `FreqAI` config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
|
||||
|
||||
It is advisable to start from the template `populate_any_indicators()` in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
|
||||
|
||||
```python
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
"""
|
||||
Function designed to automatically generate, name, and merge features
|
||||
from user-indicated timeframes in the configuration file. The user controls the indicators
|
||||
passed to the training/prediction by prepending indicators with `'%-' + coin `
|
||||
(see convention below). I.e., the user should not prepend any supporting metrics
|
||||
(e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the
|
||||
model.
|
||||
:param pair: pair to be used as informative
|
||||
:param df: strategy dataframe which will receive merges from informatives
|
||||
:param tf: timeframe of the dataframe which will modify the feature names
|
||||
:param informative: the dataframe associated with the informative pair
|
||||
:param coin: the name of the coin which will modify the feature names.
|
||||
"""
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
if informative is None:
|
||||
informative = self.dp.get_pair_dataframe(pair, tf)
|
||||
|
||||
# first loop is automatically duplicating indicators for time periods
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
|
||||
t = int(t)
|
||||
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
|
||||
bollinger = qtpylib.bollinger_bands(
|
||||
qtpylib.typical_price(informative), window=t, stds=2.2
|
||||
)
|
||||
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
|
||||
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
|
||||
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
|
||||
|
||||
informative[f"%-{coin}bb_width-period_{t}"] = (
|
||||
informative[f"{coin}bb_upperband-period_{t}"]
|
||||
- informative[f"{coin}bb_lowerband-period_{t}"]
|
||||
) / informative[f"{coin}bb_middleband-period_{t}"]
|
||||
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
|
||||
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
|
||||
)
|
||||
|
||||
informative[f"%-{coin}relative_volume-period_{t}"] = (
|
||||
informative["volume"] / informative["volume"].rolling(t).mean()
|
||||
)
|
||||
|
||||
indicators = [col for col in informative if col.startswith("%")]
|
||||
# This loop duplicates and shifts all indicators to add a sense of recency to data
|
||||
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
|
||||
if n == 0:
|
||||
continue
|
||||
informative_shift = informative[indicators].shift(n)
|
||||
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
|
||||
informative = pd.concat((informative, informative_shift), axis=1)
|
||||
|
||||
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
|
||||
skip_columns = [
|
||||
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
|
||||
]
|
||||
df = df.drop(columns=skip_columns)
|
||||
|
||||
# Add generalized indicators here (because in live, it will call this
|
||||
# function to populate indicators during training). Notice how we ensure not to
|
||||
# add them multiple times
|
||||
if set_generalized_indicators:
|
||||
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
|
||||
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
|
||||
|
||||
# user adds targets here by prepending them with &- (see convention below)
|
||||
# If user wishes to use multiple targets, a multioutput prediction model
|
||||
# needs to be used such as templates/CatboostPredictionMultiModel.py
|
||||
df["&-s_close"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.mean()
|
||||
/ df["close"]
|
||||
- 1
|
||||
)
|
||||
|
||||
return df
|
||||
```
|
||||
|
||||
In the presented example, the user does not wish to pass the `bb_lowerband` as a feature to the model,
|
||||
and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the
|
||||
model for training/prediction and has therefore prepended it with `%`.
|
||||
|
||||
After having defined the `base features`, the next step is to expand upon them using the powerful `feature_parameters` in the configuration file:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
//...
|
||||
"feature_parameters" : {
|
||||
"include_timeframes": ["5m","15m","4h"],
|
||||
"include_corr_pairlist": [
|
||||
"ETH/USD",
|
||||
"LINK/USD",
|
||||
"BNB/USD"
|
||||
],
|
||||
"label_period_candles": 24,
|
||||
"include_shifted_candles": 2,
|
||||
"indicator_periods_candles": [10, 20]
|
||||
},
|
||||
//...
|
||||
}
|
||||
```
|
||||
|
||||
The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
|
||||
|
||||
You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
|
||||
|
||||
`include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells `FreqAI` to include the past 2 candles for each of the features in the feature set.
|
||||
|
||||
In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
|
||||
$= 3 * 3 * 3 * 2 * 2 = 108$.
|
||||
|
||||
### Returning additional info from training
|
||||
|
||||
Important metrics can be returned to the strategy at the end of each model training by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the custom prediction model class.
|
||||
|
||||
`FreqAI` takes the `my_new_value` assigned in this dictionary and expands it to fit the dataframe that is returned to the strategy. You can then use the returned metrics in your strategy through `dataframe['my_new_value']`. An example of how return values can be used in `FreqAI` are the `&*_mean` and `&*_std` values that are used to [created a dynamic target threshold](freqai-configuration.md#creating-a-dynamic-target-threshold).
|
||||
|
||||
Another example, where the user wants to use live metrics from the trade database, is shown below:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"extra_returns_per_train": {"total_profit": 4}
|
||||
}
|
||||
```
|
||||
|
||||
You need to set the standard dictionary in the config so that `FreqAI` can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, the preset values are what will be returned.
|
||||
|
||||
## Feature normalization
|
||||
|
||||
`FreqAI` is strict when it comes to data normalization. The train features, $X^{train}$, are always normalized to [-1, 1] using a shifted min-max normalization:
|
||||
|
||||
$$X^{train}_{norm} = 2 * \frac{X^{train} - X^{train}.min()}{X^{train}.max() - X^{train}.min()} - 1$$
|
||||
|
||||
All other data (test data and unseen prediction data in dry/live/backtest) is always automatically normalized to the training feature space according to industry standards. `FreqAI` stores all the metadata required to ensure that test and prediction features will be properly normalized and that predictions are properly denormalized. For this reason, it is not recommended to eschew industry standards and modify `FreqAI` internals - however - advanced users can do so by inheriting `train()` in their custom `IFreqaiModel` and using their own normalization functions.
|
||||
|
||||
## Data dimensionality reduction with Principal Component Analysis
|
||||
|
||||
You can reduce the dimensionality of your features by activating the `principal_component_analysis` in the config:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"principal_component_analysis": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
This will perform PCA on the features and reduce their dimensionality so that the explained variance of the data set is >= 0.999. Reducing data dimensionality makes training the model faster and hence allows for more up-to-date models.
|
||||
|
||||
## Inlier metric
|
||||
|
||||
The `inlier_metric` is a metric aimed at quantifying how similar a the features of a data point are to the most recent historic data points.
|
||||
|
||||
You define the lookback window by setting `inlier_metric_window` and `FreqAI` computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function (CDF) is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. The figure below explains the concept for an `inlier_metric_window` of 5.
|
||||
|
||||
|
||||
|
||||
`FreqAI` adds the `inlier_metric` to the training features and hence gives the model access to a novel type of temporal information.
|
||||
|
||||
This function does **not** remove outliers from the data set.
|
||||
|
||||
## Weighting features for temporal importance
|
||||
|
||||
`FreqAI` allows you to set a `weight_factor` to weight recent data more strongly than past data via an exponential function:
|
||||
|
||||
$$ W_i = \exp(\frac{-i}{\alpha*n}) $$
|
||||
|
||||
where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points in a feature set.
|
||||
|
||||
|
||||
|
||||
## Outlier detection
|
||||
|
||||
Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. `FreqAI` implements a variety of methods to identify such outliers and hence mitigate risk.
|
||||
|
||||
### Identifying outliers with the Dissimilarity Index (DI)
|
||||
|
||||
The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model.
|
||||
|
||||
You can tell `FreqAI` to remove outlier data points from the training/test data sets using the DI by including the following statement in the config:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"DI_threshold": 1
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. To do so, `FreqAI` measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points:
|
||||
|
||||
$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$
|
||||
|
||||
where $d_{ab}$ is the distance between the normalized points $a$ and $b$, and $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$, for a set of training data points is simply the mean of the average distances:
|
||||
|
||||
$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$
|
||||
|
||||
$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data:
|
||||
|
||||
$$ d_k = \arg \min d_{k,i} $$
|
||||
|
||||
This enables the estimation of the Dissimilarity Index as:
|
||||
|
||||
$$ DI_k = d_k/\overline{d} $$
|
||||
|
||||
You can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model. A higher `DI_threshold` means that the DI is more lenient and allows predictions further away from the training data to be used whilst a lower `DI_threshold` has the opposite effect and hence discards more predictions.
|
||||
|
||||
Below is a figure that describes the DI for a 3D data set.
|
||||
|
||||
|
||||
|
||||
### Identifying outliers using a Support Vector Machine (SVM)
|
||||
|
||||
You can tell `FreqAI` to remove outlier data points from the training/test data sets using a Support Vector Machine (SVM) by including the following statement in the config:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"use_SVM_to_remove_outliers": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The SVM will be trained on the training data and any data point that the SVM deems to be beyond the feature space will be removed.
|
||||
|
||||
`FreqAI` uses `sklearn.linear_model.SGDOneClassSVM` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html) (external website)) and you can elect to provide additional parameters for the SVM, such as `shuffle`, and `nu`.
|
||||
|
||||
The parameter `shuffle` is by default set to `False` to ensure consistent results. If it is set to `True`, running the SVM multiple times on the same data set might result in different outcomes due to `max_iter` being to low for the algorithm to reach the demanded `tol`. Increasing `max_iter` solves this issue but causes the procedure to take longer time.
|
||||
|
||||
The parameter `nu`, *very* broadly, is the amount of data points that should be considered outliers and should be between 0 and 1.
|
||||
|
||||
### Identifying outliers with DBSCAN
|
||||
|
||||
You can configure `FreqAI` to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"use_DBSCAN_to_remove_outliers": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be.
|
||||
|
||||
Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$.
|
||||
|
||||
|
||||
|
||||
`FreqAI` uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (external website)) with `min_samples` ($N$) taken as 1/4 of the no. of time points in the feature set. `eps` ($\varepsilon$) is computed automatically as the elbow point in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set.
|
||||
51
docs/freqai-parameter-table.md
Normal file
51
docs/freqai-parameter-table.md
Normal file
@@ -0,0 +1,51 @@
|
||||
# Parameter table
|
||||
|
||||
The table below will list all configuration parameters available for `FreqAI`. Some of the parameters are exemplified in `config_examples/config_freqai.example.json`.
|
||||
|
||||
Mandatory parameters are marked as **Required** and have to be set in one of the suggested ways.
|
||||
|
||||
| Parameter | Description |
|
||||
|------------|-------------|
|
||||
| | **General configuration parameters**
|
||||
| `freqai` | **Required.** <br> The parent dictionary containing all the parameters for controlling `FreqAI`. <br> **Datatype:** Dictionary.
|
||||
| `train_period_days` | **Required.** <br> Number of days to use for the training data (width of the sliding window). <br> **Datatype:** Positive integer.
|
||||
| `backtest_period_days` | **Required.** <br> Number of days to inference from the trained model before sliding the `train_period_days` window defined above, and retraining the model during backtesting (more info [here](freqai-running.md#backtesting)). This can be fractional days, but beware that the provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
|
||||
| `identifier` | **Required.** <br> A unique ID for the current model. If models are saved to disk, the `identifier` allows for reloading specific pre-trained models/data. <br> **Datatype:** String.
|
||||
| `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> **Datatype:** Float > 0. <br> Default: 0 (models retrain as often as possible).
|
||||
| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> **Datatype:** Positive integer. <br> Default: 0 (models never expire).
|
||||
| `purge_old_models` | Delete obsolete models. <br> **Datatype:** Boolean. <br> Default: `False` (all historic models remain on disk).
|
||||
| `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
|
||||
| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
|
||||
| `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.
|
||||
| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
|
||||
| | **Feature parameters**
|
||||
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
|
||||
| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
|
||||
| `include_corr_pairlist` | A list of correlated coins that `FreqAI` will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
|
||||
| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
|
||||
| `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, `FreqAI` will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
|
||||
| `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)). <br> **Datatype:** Positive float (typically < 1).
|
||||
| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. `FreqAI` uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN <br> **Datatype:** Positive integer.
|
||||
| `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset. <br> **Datatype:** List of positive integers.
|
||||
| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. defaults to `False`.
|
||||
| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features.<br> **Datatype:** Integer, defaults to `0`.
|
||||
| `DI_threshold` | Activates the use of the Dissimilarity Index for outlier detection when set to > 0. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Positive float (typically < 1).
|
||||
| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
|
||||
| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
|
||||
| `use_DBSCAN_to_remove_outliers` | Cluster data using the DBSCAN algorithm to identify and remove outliers from training and prediction data. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan). <br> **Datatype:** Boolean.
|
||||
| `inlier_metric_window` | If set, `FreqAI` adds an `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`, i.e., the number of previous time points to compare the current candle to. Details of how the `inlier_metric` is computed can be found [here](freqai-feature-engineering.md#inlier-metric). <br> **Datatype:** Integer. <br> Default: 0.
|
||||
| `noise_standard_deviation` | If set, `FreqAI` adds noise to the training features with the aim of preventing overfitting. `FreqAI` generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in `FreqAI` is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: 0.
|
||||
| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, `FreqAI` will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
|
||||
| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
|
||||
| | **Data split parameters**
|
||||
| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
|
||||
| `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
|
||||
| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Defaut: `False`.
|
||||
| | **Model training parameters**
|
||||
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model. <br> **Datatype:** Dictionary.
|
||||
| `n_estimators` | The number of boosted trees to fit in regression. <br> **Datatype:** Integer.
|
||||
| `learning_rate` | Boosting learning rate during regression. <br> **Datatype:** Float.
|
||||
| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.
|
||||
| | **Extraneous parameters**
|
||||
| `keras` | If the selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
|
||||
| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: 2.
|
||||
156
docs/freqai-running.md
Normal file
156
docs/freqai-running.md
Normal file
@@ -0,0 +1,156 @@
|
||||
# Running FreqAI
|
||||
|
||||
There are two ways to train and deploy an adaptive machine learning model - live deployment and historical backtesting. In both cases, `FreqAI` runs/simulates periodic retraining of models as shown in the following figure:
|
||||
|
||||
|
||||
|
||||
## Live deployments
|
||||
|
||||
FreqAI can be run dry/live using the following command:
|
||||
|
||||
```bash
|
||||
freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor
|
||||
```
|
||||
|
||||
When launched, FreqAI will start training a new model, with a new `identifier`, based on the config settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If you do not want FreqAI to retrain new models as often as possible, you can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, you can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours.
|
||||
|
||||
Trained models are by default saved to disk to allow for reuse during backtesting or after a crash. You can opt to [purge old models](#purging-old-model-data) to save disk space by setting `"purge_old_models": true` in the config.
|
||||
|
||||
To start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), you only need to specify the `identifier` of the specific model:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"identifier": "example",
|
||||
"live_retrain_hours": 0.5
|
||||
}
|
||||
```
|
||||
|
||||
In this case, although FreqAI will initiate with a pre-trained model, it will still check to see how much time has elapsed since the model was trained. If a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will start training a new model.
|
||||
|
||||
### Automatic data download
|
||||
|
||||
FreqAI automatically downloads the proper amount of data needed to ensure training of a model through the defined `train_period_days` and `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters).
|
||||
|
||||
### Saving prediction data
|
||||
|
||||
All predictions made during the lifetime of a specific `identifier` model are stored in `historical_predictions.pkl` to allow for reloading after a crash or changes made to the config.
|
||||
|
||||
### Purging old model data
|
||||
|
||||
FreqAI stores new model files after each successful training. These files become obsolete as new models are generated to adapt to new market conditions. If you are planning to leave FreqAI running for extended periods of time with high frequency retraining, you should enable `purge_old_models` in the config:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"purge_old_models": true,
|
||||
}
|
||||
```
|
||||
|
||||
This will automatically purge all models older than the two most recently trained ones to save disk space.
|
||||
|
||||
## Backtesting
|
||||
|
||||
The FreqAI backtesting module can be executed with the following command:
|
||||
|
||||
```bash
|
||||
freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
|
||||
```
|
||||
|
||||
If this command has never been executed with the existing config file, FreqAI will train a new model
|
||||
for each pair, for each backtesting window within the expanded `--timerange`.
|
||||
|
||||
Backtesting mode requires [downloading the necessary data](#downloading-data-to-cover-the-full-backtest-period) before deployment (unlike in dry/live mode where FreqAI handles the data downloading automatically). You should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-size-of-the-sliding-training-window-and-backtesting-duration).
|
||||
|
||||
!!! Note "Model reuse"
|
||||
Once the training is completed, you can execute the backtesting again with the same config file and
|
||||
FreqAI will find the trained models and load them instead of spending time training. This is useful
|
||||
if you want to tweak (or even hyperopt) buy and sell criteria inside the strategy. If you
|
||||
*want* to retrain a new model with the same config file, you should simply change the `identifier`.
|
||||
This way, you can return to using any model you wish by simply specifying the `identifier`.
|
||||
|
||||
---
|
||||
|
||||
### Saving prediction data
|
||||
|
||||
To allow for tweaking your strategy (**not** the features!), FreqAI will automatically save the predictions during backtesting so that they can be reused for future backtests and live runs using the same `identifier` model. This provides a performance enhancement geared towards enabling **high-level hyperopting** of entry/exit criteria.
|
||||
|
||||
An additional directory called `predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder.
|
||||
|
||||
To change your **features**, you **must** set a new `identifier` in the config to signal to `FreqAI` to train new models.
|
||||
|
||||
To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config.
|
||||
|
||||
### Downloading data to cover the full backtest period
|
||||
|
||||
For live/dry deployments, FreqAI will download the necessary data automatically. However, to use backtesting functionality, you need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). You need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that there is a sufficient amount of training data *before* the start of the backtesting timerange. The amount of additional data can be roughly estimated by moving the start date of the timerange backwards by `train_period_days` and the `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters) from the beginning of the desired backtesting timerange.
|
||||
|
||||
As an example, to backtest the `--timerange 20210501-20210701` using the [example config](freqai-configuration.md#setting-up-the-configuration-file) which sets `train_period_days` to 30, together with `startup_candle_count: 40` on a maximum `include_timeframes` of 1h, the start date for the downloaded data needs to be `20210501` - 30 days - 40 * 1h / 24 hours = 20210330 (31.7 days earlier than the start of the desired training timerange).
|
||||
|
||||
### Deciding the size of the sliding training window and backtesting duration
|
||||
|
||||
The backtesting timerange is defined with the typical `--timerange` parameter in the configuration file. The duration of the sliding training window is set by `train_period_days`, whilst `backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be
|
||||
a float to indicate sub-daily retraining in live/dry mode). In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file) (found in `config_examples/config_freqai.example.json`), the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. After the training of the model, FreqAI will backtest the subsequent 7 days. The "sliding window" then moves one week forward (emulating FreqAI retraining once per week in live mode) and the new model uses the previous 30 days (including the 7 days used for backtesting by the previous model) to train. This is repeated until the end of `--timerange`. This means that if you set `--timerange 20210501-20210701`, FreqAI will have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks).
|
||||
|
||||
!!! Note
|
||||
Although fractional `backtest_period_days` is allowed, you should be aware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, by setting a `--timerange` of 10 days, and a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. Because of this, a true backtest of FreqAI adaptive training would take a *very* long time. The best way to fully test a model is to run it dry and let it train constantly. In this case, backtesting would take the exact same amount of time as a dry run.
|
||||
|
||||
## Defining model expirations
|
||||
|
||||
During dry/live mode, FreqAI trains each coin pair sequentially (on separate threads/GPU from the main Freqtrade bot). This means that there is always an age discrepancy between models. If you are training on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. This may be undesirable if the characteristic time scale (the trade duration target) for a strategy is less than 4 hours. You can decide to only make trade entries if the model is less than a certain number of hours old by setting the `expiration_hours` in the config file:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"expiration_hours": 0.5,
|
||||
}
|
||||
```
|
||||
|
||||
In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old.
|
||||
|
||||
## Controlling the model learning process
|
||||
|
||||
Model training parameters are unique to the selected machine learning library. FreqAI allows you to set any parameter for any library using the `model_training_parameters` dictionary in the config. The example config (found in `config_examples/config_freqai.example.json`) shows some of the example parameters associated with `Catboost` and `LightGBM`, but you can add any parameters available in those libraries or any other machine learning library you choose to implement.
|
||||
|
||||
Data split parameters are defined in `data_split_parameters` which can be any parameters associated with Scikit-learn's `train_test_split()` function. `train_test_split()` has a parameters called `shuffle` which allows to shuffle the data or keep it unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. More details about these parameters can be found the [Scikit-learn website](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
|
||||
|
||||
The FreqAI specific parameter `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file), the user is asking for `labels` that are 24 candles in the future.
|
||||
|
||||
## Continual learning
|
||||
|
||||
You can choose to adopt a continual learning scheme by setting `"continual_learning": true` in the config. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `false` which means that all new models are trained from scratch, without input from previous models.
|
||||
|
||||
## Hyperopt
|
||||
|
||||
You can hyperopt using the same command as for [typical Freqtrade hyperopt](hyperopt.md):
|
||||
|
||||
```bash
|
||||
freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --timerange 20220428-20220507
|
||||
```
|
||||
|
||||
`hyperopt` requires you to have the data pre-downloaded in the same fashion as if you were doing [backtesting](#backtesting). In addition, you must consider some restrictions when trying to hyperopt FreqAI strategies:
|
||||
|
||||
- The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI.
|
||||
- It's not possible to hyperopt indicators in the `populate_any_indicators()` function. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
|
||||
- The backtesting instructions also apply to hyperopt.
|
||||
|
||||
The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. You need to focus on hyperopting parameters that are not used in your features. For example, you should not try to hyperopt rolling window lengths in the feature creation, or any part of the FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
|
||||
|
||||
A good example of a hyperoptable parameter in FreqAI is a threshold for the [Dissimilarity Index (DI)](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) `DI_values` beyond which we consider data points as outliers:
|
||||
|
||||
```python
|
||||
di_max = IntParameter(low=1, high=20, default=10, space='buy', optimize=True, load=True)
|
||||
dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1, 0)
|
||||
```
|
||||
|
||||
This specific hyperopt would help you understand the appropriate `DI_values` for your particular parameter space.
|
||||
|
||||
## Setting up a follower
|
||||
|
||||
You can indicate to the bot that it should not train models, but instead should look for models trained by a leader with a specific `identifier` by defining:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"follow_mode": true,
|
||||
"identifier": "example"
|
||||
}
|
||||
```
|
||||
|
||||
In this example, the user has a leader bot with the `"identifier": "example"`. The leader bot is already running or is launched simultaneously with the follower. The follower will load models created by the leader and inference them to obtain predictions instead of training its own models.
|
||||
917
docs/freqai.md
917
docs/freqai.md
@@ -1,75 +1,72 @@
|
||||
|
||||
|
||||
# FreqAI
|
||||
# `FreqAI`
|
||||
|
||||
FreqAI is a module designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input features.
|
||||
## Introduction
|
||||
|
||||
`FreqAI` is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input features.
|
||||
|
||||
Features include:
|
||||
|
||||
* **Self-adaptive retraining**: retrain models during [live deployments](#running-the-model-live) to self-adapt to the market in an unsupervised manner.
|
||||
* **Rapid feature engineering**: create large rich [feature sets](#feature-engineering) (10k+ features) based on simple user-created strategies.
|
||||
* **High performance**: adaptive retraining occurs on a separate thread (or on GPU if available) from inferencing and bot trade operations. Newest models and data are kept in memory for rapid inferencing.
|
||||
* **Realistic backtesting**: emulate self-adaptive retraining with a [backtesting module](#backtesting) that automates past retraining.
|
||||
* **Modifiability**: use the generalized and robust architecture for incorporating any [machine learning library/method](#building-a-custom-prediction-model) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network.
|
||||
* **Smart outlier removal**: remove outliers from training and prediction data sets using a variety of [outlier detection techniques](#outlier-removal).
|
||||
* **Crash resilience**: store model to disk to make reloading from a crash fast and easy, and [purge obsolete files](#purging-old-model-data) for sustained dry/live runs.
|
||||
* **Automatic data normalization**: [normalize the data](#feature-normalization) in a smart and statistically safe way.
|
||||
* **Automatic data download**: compute the data download timerange and update historic data (in live deployments).
|
||||
* **Cleaning of incoming data**: handle NaNs safely before training and prediction.
|
||||
* **Dimensionality reduction**: reduce the size of the training data via [Principal Component Analysis](#reducing-data-dimensionality-with-principal-component-analysis).
|
||||
* **Deploying bot fleets**: set one bot to train models while a fleet of [follower bots](#setting-up-a-follower) inference the models and handle trades.
|
||||
* **Self-adaptive retraining** - Retrain models during [live deployments](freqai-running.md#live-deployments) to self-adapt to the market in a supervised manner
|
||||
* **Rapid feature engineering** - Create large rich [feature sets](freqai-feature-engineering.md#feature-engineering) (10k+ features) based on simple user-created strategies
|
||||
* **High performance** - Threading allows for adaptive model retraining on a separate thread (or on GPU if available) from model inferencing (prediction) and bot trade operations. Newest models and data are kept in RAM for rapid inferencing
|
||||
* **Realistic backtesting** - Emulate self-adaptive training on historic data with a [backtesting module](freqai-running.md#backtesting) that automates retraining
|
||||
* **Extensibility** - The generalized and robust architecture allows for incorporating any [machine learning library/method](freqai-configuration.md#using-different-prediction-models) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network
|
||||
* **Smart outlier removal** - Remove outliers from training and prediction data sets using a variety of [outlier detection techniques](freqai-feature-engineering.md#outlier-detection)
|
||||
* **Crash resilience** - Store trained models to disk to make reloading from a crash fast and easy, and [purge obsolete files](freqai-running.md#purging-old-model-data) for sustained dry/live runs
|
||||
* **Automatic data normalization** - [Normalize the data](freqai-feature-engineering.md#feature-normalization) in a smart and statistically safe way
|
||||
* **Automatic data download** - Compute timeranges for data downloads and update historic data (in live deployments)
|
||||
* **Cleaning of incoming data** - Handle NaNs safely before training and model inferencing
|
||||
* **Dimensionality reduction** - Reduce the size of the training data via [Principal Component Analysis](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis)
|
||||
* **Deploying bot fleets** - Set one bot to train models while a fleet of [follower bots](freqai-running.md#setting-up-a-follower) inference the models and handle trades
|
||||
|
||||
## Quick start
|
||||
|
||||
The easiest way to quickly test FreqAI is to run it in dry mode with the following command
|
||||
The easiest way to quickly test `FreqAI` is to run it in dry mode with the following command:
|
||||
|
||||
```bash
|
||||
freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates
|
||||
```
|
||||
|
||||
The user will see the boot-up process of automatic data downloading, followed by simultaneous training and trading.
|
||||
You will see the boot-up process of automatic data downloading, followed by simultaneous training and trading.
|
||||
|
||||
The example strategy, example prediction model, and example config can be found in
|
||||
An example strategy, prediction model, and config to use as a starting points can be found in
|
||||
`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, and
|
||||
`config_examples/config_freqai.example.json`, respectively.
|
||||
|
||||
## General approach
|
||||
|
||||
The user provides FreqAI with a set of custom *base* indicators (the same way as in a typical Freqtrade strategy) as well as target values (*labels*).
|
||||
FreqAI trains a model to predict the target values based on the input of custom indicators, for each pair in the whitelist. These models are consistently retrained to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining) and deploy dry/live runs. In dry/live conditions, FreqAI can be set to constant retraining in a background thread in an effort to keep models as up to date as possible.
|
||||
You provide `FreqAI` with a set of custom *base indicators* (the same way as in a [typical Freqtrade strategy](strategy-customization.md)) as well as target values (*labels*). For each pair in the whitelist, `FreqAI` trains a model to predict the target values based on the input of custom indicators. The models are then consistently retrained, with a predetermined frequency, to adapt to market conditions. `FreqAI` offers the ability to both backtest strategies (emulating reality with periodic retraining on historic data) and deploy dry/live runs. In dry/live conditions, `FreqAI` can be set to constant retraining in a background thread to keep models as up to date as possible.
|
||||
|
||||
An overview of the algorithm is shown below, explaining the data processing pipeline and the model usage.
|
||||
An overview of the algorithm, explaining the data processing pipeline and model usage, is shown below.
|
||||
|
||||
|
||||
|
||||
### Important machine learning vocabulary
|
||||
|
||||
**Features** - the quantities with which a model is trained. All features for a single candle is stored as a vector. In FreqAI, the user
|
||||
builds the feature sets from anything they can construct in the strategy.
|
||||
**Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle is stored as a vector. In `FreqAI`, you build a feature data sets from anything you can construct in the strategy.
|
||||
|
||||
**Labels** - the target values that a model is trained
|
||||
toward. Each set of features is associated with a single label that is
|
||||
defined by the user within the strategy. These labels intentionally look into the
|
||||
future, and are not available to the model during dry/live/backtesting.
|
||||
**Labels** - the target values that a model is trained toward. Each feature vector is associated with a single label that is defined by you within the strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting.
|
||||
|
||||
**Training** - the process of feeding individual feature sets, composed of historic data, with associated labels into the
|
||||
model with the goal of matching input feature sets to associated labels.
|
||||
**Training** - the process of "teaching" the model to match the feature sets to the associated labels. Different types of models "learn" in different ways. More information about the different models can be found [here](freqai-configuration.md#using-different-prediction-models).
|
||||
|
||||
**Train data** - a subset of the historic data that is fed to the model during
|
||||
training. This data directly influences weight connections in the model.
|
||||
**Train data** - a subset of the feature data set that is fed to the model during training. This data directly influences weight connections in the model.
|
||||
|
||||
**Test data** - a subset of the historic data that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model.
|
||||
**Test data** - a subset of the feature data set that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model.
|
||||
|
||||
**Inferencing** - the process of feeding a trained model new data on which it will make a prediction.
|
||||
|
||||
## Install prerequisites
|
||||
|
||||
The normal Freqtrade install process will ask the user if they wish to install FreqAI dependencies. The user should reply "yes" to this question if they wish to use FreqAI. If the user did not reply yes, they can manually install these dependencies after the install with:
|
||||
The normal Freqtrade install process will ask if you wish to install `FreqAI` dependencies. You should reply "yes" to this question if you wish to use `FreqAI`. If you did not reply yes, you can manually install these dependencies after the install with:
|
||||
|
||||
``` bash
|
||||
pip install -r requirements-freqai.txt
|
||||
```
|
||||
|
||||
!!! Note
|
||||
Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since Catboost does not provide wheels for this platform.
|
||||
Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since it does not provide wheels for this platform.
|
||||
|
||||
### Usage with docker
|
||||
|
||||
@@ -81,857 +78,29 @@ This image contains the regular freqAI dependencies. Similar to native installs,
|
||||
|
||||
### Parameter table
|
||||
|
||||
The table below will list all configuration parameters available for FreqAI, presented in the same order as `config_examples/config_freqai.example.json`.
|
||||
## Common pitfalls
|
||||
|
||||
Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
|
||||
|
||||
| Parameter | Description |
|
||||
|------------|-------------|
|
||||
| | **General configuration parameters**
|
||||
| `freqai` | **Required.** <br> The parent dictionary containing all the parameters for controlling FreqAI. <br> **Datatype:** Dictionary.
|
||||
| `purge_old_models` | Delete obsolete models (otherwise, all historic models will remain on disk). <br> **Datatype:** Boolean. Default: `False`.
|
||||
| `train_period_days` | **Required.** <br> Number of days to use for the training data (width of the sliding window). <br> **Datatype:** Positive integer.
|
||||
| `backtest_period_days` | **Required.** <br> Number of days to inference from the trained model before sliding the window defined above, and retraining the model. This can be fractional days, but beware that the user-provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
|
||||
| `save_backtest_models` | Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when users wish to tune entry/exit parameters). If a user wishes to save models to disk when running backtesting, they should activate `save_backtest_models`. A user may wish to do this if they plan to use the same model files for starting a dry/live instance with the same `identifier`. <br> **Datatype:** Boolean. Default: `False`.
|
||||
| `identifier` | **Required.** <br> A unique name for the current model. This can be reused to reload pre-trained models/data. <br> **Datatype:** String.
|
||||
| `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> Default set to 0, which means the model will retrain as often as possible. <br> **Datatype:** Float > 0.
|
||||
| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> Defaults set to 0, which means models never expire. <br> **Datatype:** Positive integer.
|
||||
| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training data set. <br> **Datatype:** Positive integer.
|
||||
| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. Default: `False`.
|
||||
| `continual_learning` | If true, FreqAI will start training new models from the final state of the most recently trained model. <br> **Datatype:** Boolean. Default: `False`.
|
||||
| | **Feature parameters**
|
||||
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](#feature-engineering). <br> **Datatype:** Dictionary.
|
||||
| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base asset feature set. <br> **Datatype:** List of timeframes (strings).
|
||||
| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](#feature-engineering)) will be created for each coin in this list, and that set of features is added to the base asset feature set. <br> **Datatype:** List of assets (strings).
|
||||
| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). The user can create custom labels, making use of this parameter or not. <br> **Datatype:** Positive integer.
|
||||
| `include_shifted_candles` | Add features from previous candles to subsequent candles to add historical information. FreqAI takes all features from the `include_shifted_candles` previous candles, duplicates and shifts them so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
|
||||
| `weight_factor` | Used to set weights for training data points according to their recency. See details about how it works [here](#controlling-the-model-learning-process). <br> **Datatype:** Positive float (typically < 1).
|
||||
| `indicator_max_period_candles` | **No longer used**. User must use the strategy set `startup_candle_count` which defines the maximum *period* used in `populate_any_indicators()` for indicator creation (timeframe independent). FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN <br> **Datatype:** positive integer.
|
||||
| `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set. <br> **Datatype:** List of positive integers.
|
||||
| `stratify_training_data` | This value is used to indicate the grouping of the data. For example, 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](#stratifying-the-data-for-training-and-testing-the-model) <br> **Datatype:** Positive integer.
|
||||
| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis)
|
||||
| `plot_feature_importance` | Create an interactive feature importance plot for each model.<br> **Datatype:** Boolean.<br> **Datatype:** Boolean, defaults to `False`
|
||||
| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index). <br> **Datatype:** Positive float (typically < 1).
|
||||
| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training data set, as well as from incoming data points. See details about how it works [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
|
||||
| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
|
||||
| `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan). <br> **Datatype:** Boolean.
|
||||
| `inlier_metric_window` | If set, FreqAI will add the `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`. Details of how the `inlier_metric` is computed can be found [here](#using-the-inliermetric) <br> **Datatype:** int. Default: 0
|
||||
| `noise_standard_deviation` | If > 0, FreqAI adds noise to the training features. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. Value should be kept relative to the normalized space between -1 and 1). In other words, since data is always normalized between -1 and 1 in FreqAI, the user can expect a `noise_standard_deviation: 0.05` to see 32% of data randomly increased/decreased by more than 2.5% (i.e. the percent of data falling within the first standard deviation). Good for preventing overfitting. <br> **Datatype:** int. Default: 0
|
||||
| `outlier_protection_percentage` | If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection while keeping the original dataset intact. If the outlier protection is triggered, no predictions will be made based on the training data. <br> **Datatype:** Float. Default: `30`
|
||||
| `reverse_train_test_order` | If true, FreqAI will train on the latest data split and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, users should be careful to understand unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. Default: False
|
||||
| | **Data split parameters**
|
||||
| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
|
||||
| `test_size` | Fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
|
||||
| `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean.
|
||||
| | **Model training parameters**
|
||||
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected model library. For example, if the user uses `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If the user selects a different model, such as `PPO` from stable_baselines3, this dictionary can contain any parameter from that model. <br> **Datatype:** Dictionary.
|
||||
| `n_estimators` | The number of boosted trees to fit in regression. <br> **Datatype:** Integer.
|
||||
| `learning_rate` | Boosting learning rate during regression. <br> **Datatype:** Float.
|
||||
| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.
|
||||
| | *Reinforcement Learning Parameters**
|
||||
| `rl_config` | A dictionary containing the control parameters for a Reinforcement Learning model. <br> **Datatype:** Dictionary.
|
||||
| `train_cycles` | Training time steps will be set based on the `train_cycles * number of training data points. <br> **Datatype:** Integer.
|
||||
| `cpu_count` | Number of processors to dedicate to the Reinforcement Learning training process. <br> **Datatype:** int.
|
||||
| `max_trade_duration_candles`| Guides the agent training to keep trades below desired length. Example usage shown in `prediction_models/ReinforcementLearner.py` within the user customizable `calculate_reward()` <br> **Datatype:** int.
|
||||
| `model_type` | Model string from stable_baselines3 or SBcontrib. Available strings include: `'TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO', 'PPO', 'A2C', 'DQN'`. User should ensure that `model_training_parameters` match those available to the corresponding stable_baselines3 model by visiting their documentaiton. [PPO doc](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) (external website) <br> **Datatype:** string.
|
||||
| `policy_type` | One of the available policy types from stable_baselines3 <br> **Datatype:** string.
|
||||
| `continual_learning` | If true, the agent will start new trainings from the model selected during the previous training. If false, a new agent is trained from scratch for each training. <br> **Datatype:** Bool.
|
||||
| `thread_count` | Number of threads to dedicate to the Reinforcement Learning training process. <br> **Datatype:** int.
|
||||
| `model_reward_parameters` | Parameters used inside the user customizable `calculate_reward()` function in `ReinforcementLearner.py` <br> **Datatype:** int.
|
||||
| | **Extraneous parameters**
|
||||
| `keras` | If your model makes use of keras (typical of Tensorflow based prediction models), activate this flag so that the model save/loading follows keras standards. Default value `false` <br> **Datatype:** boolean.
|
||||
| `conv_width` | The width of a convolutional neural network input tensor or the `ReinforcementLearningModel` `window_size`. This replaces the need for `shift` by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. Default value, 2 <br> **Datatype:** integer.
|
||||
|
||||
### Important dataframe key patterns
|
||||
|
||||
Below are the values the user can expect to include/use inside a typical strategy dataframe (`df[]`):
|
||||
|
||||
| DataFrame Key | Description |
|
||||
|------------|-------------|
|
||||
| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back to the user as the predictions. For example, if the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), they set `df['&-s_close']`. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
|
||||
| `df['&*_std/mean']` | Standard deviation and mean values of the user-defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
|
||||
| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets the user know if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](#removing-outliers-with-the-dissimilarity-index)) of the input data point is above the user-defined threshold, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -1 and 2.
|
||||
| `df['DI_values']` | Dissimilarity Index values are proxies to the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. <br> **Datatype:** Float.
|
||||
| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, the user can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#feature-engineering). <br> **Note**: Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, they can prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
|
||||
|
||||
### File structure
|
||||
|
||||
`user_data_dir/models/` contains all the data associated with the trainings and backtests.
|
||||
This file structure is heavily controlled and inferenced by the `FreqaiDataKitchen()`
|
||||
and should therefore not be modified.
|
||||
|
||||
### Example config file
|
||||
|
||||
The user interface is isolated to the typical Freqtrade config file. A FreqAI config should include:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"enabled": true,
|
||||
"startup_candles": 10000,
|
||||
"purge_old_models": true,
|
||||
"train_period_days": 30,
|
||||
"backtest_period_days": 7,
|
||||
"identifier" : "unique-id",
|
||||
"feature_parameters" : {
|
||||
"include_timeframes": ["5m","15m","4h"],
|
||||
"include_corr_pairlist": [
|
||||
"ETH/USD",
|
||||
"LINK/USD",
|
||||
"BNB/USD"
|
||||
],
|
||||
"label_period_candles": 24,
|
||||
"include_shifted_candles": 2,
|
||||
"indicator_periods_candles": [10, 20]
|
||||
},
|
||||
"data_split_parameters" : {
|
||||
"test_size": 0.25
|
||||
},
|
||||
"model_training_parameters" : {
|
||||
"n_estimators": 100
|
||||
},
|
||||
}
|
||||
```
|
||||
|
||||
## Building a FreqAI strategy
|
||||
|
||||
The FreqAI strategy requires the user to include the following lines of code in the standard Freqtrade strategy:
|
||||
|
||||
```python
|
||||
# user should define the maximum startup candle count (the largest number of candles
|
||||
# passed to any single indicator)
|
||||
startup_candle_count: int = 20
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
# the model will return all labels created by user in `populate_any_indicators`
|
||||
# (& appended targets), an indication of whether or not the prediction should be accepted,
|
||||
# the target mean/std values for each of the labels created by user in
|
||||
# `populate_any_indicators()` for each training period.
|
||||
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
"""
|
||||
Function designed to automatically generate, name and merge features
|
||||
from user indicated timeframes in the configuration file. User controls the indicators
|
||||
passed to the training/prediction by prepending indicators with `'%-' + coin `
|
||||
(see convention below). I.e. user should not prepend any supporting metrics
|
||||
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
|
||||
model.
|
||||
:param pair: pair to be used as informative
|
||||
:param df: strategy dataframe which will receive merges from informatives
|
||||
:param tf: timeframe of the dataframe which will modify the feature names
|
||||
:param informative: the dataframe associated with the informative pair
|
||||
:param coin: the name of the coin which will modify the feature names.
|
||||
"""
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
if informative is None:
|
||||
informative = self.dp.get_pair_dataframe(pair, tf)
|
||||
|
||||
# first loop is automatically duplicating indicators for time periods
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
|
||||
t = int(t)
|
||||
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
|
||||
indicators = [col for col in informative if col.startswith("%")]
|
||||
# This loop duplicates and shifts all indicators to add a sense of recency to data
|
||||
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
|
||||
if n == 0:
|
||||
continue
|
||||
informative_shift = informative[indicators].shift(n)
|
||||
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
|
||||
informative = pd.concat((informative, informative_shift), axis=1)
|
||||
|
||||
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
|
||||
skip_columns = [
|
||||
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
|
||||
]
|
||||
df = df.drop(columns=skip_columns)
|
||||
|
||||
# Add generalized indicators here (because in live, it will call this
|
||||
# function to populate indicators during training). Notice how we ensure not to
|
||||
# add them multiple times
|
||||
if set_generalized_indicators:
|
||||
|
||||
# user adds targets here by prepending them with &- (see convention below)
|
||||
# If user wishes to use multiple targets, a multioutput prediction model
|
||||
# needs to be used such as templates/CatboostPredictionMultiModel.py
|
||||
df["&-s_close"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.mean()
|
||||
/ df["close"]
|
||||
- 1
|
||||
)
|
||||
|
||||
return df
|
||||
|
||||
|
||||
```
|
||||
|
||||
Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](#feature-engineering)) and labels ([more information](#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`.
|
||||
|
||||
*Important*: The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
|
||||
|
||||
### Setting the `startup_candle_count`
|
||||
Users need to take care to set the `startup_candle_count` in their strategy the same way they would for any normal Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling on the `dataprovider` to avoid any NaNs at the beginning of the first training. Users can easily set this value by identifying the longest period (in candle units) that they pass to their indicator creation functions (e.g. talib functions). In the present example, the user would pass 20 to as this value (since it is the maximum value in their `indicators_periods_candles`).
|
||||
|
||||
!!! Note
|
||||
Typically it is best for users to be safe and multiply their expected `startup_candle_count` by 2. There are instances where the talib functions actually require more data than just the passed `period`. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Look out for this log message to confirm that your data is clean:
|
||||
|
||||
```
|
||||
2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319.
|
||||
```
|
||||
|
||||
|
||||
## Creating a dynamic target
|
||||
|
||||
The `&*_std/mean` return values describe the statistical fit of the user defined label *during the most recent training*. This value allows the user to know the rarity of a given prediction. For example, `templates/FreqaiExampleStrategy.py`, creates a `target_roi` which is based on filtering out predictions that are below a given z-score of 1.25.
|
||||
|
||||
```python
|
||||
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
|
||||
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
|
||||
```
|
||||
|
||||
If the user wishes to consider the population
|
||||
of *historical predictions* for creating the dynamic target instead of the trained labels, (as discussed above) the user
|
||||
can do so by setting `fit_live_prediction_candles` in the config to the number of historical prediction candles
|
||||
the user wishes to use to generate target statistics.
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"fit_live_prediction_candles": 300,
|
||||
}
|
||||
```
|
||||
|
||||
If the user sets this value, FreqAI will initially use the predictions from the training data
|
||||
and subsequently begin introducing real prediction data as it is generated. FreqAI will save
|
||||
this historical data to be reloaded if the user stops and restarts a model with the same `identifier`.
|
||||
|
||||
## Building a custom prediction model
|
||||
|
||||
FreqAI has multiple example prediction model libraries, such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression.
|
||||
However, the user can customize and create their own prediction models using the `IFreqaiModel` class.
|
||||
The user is encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures.
|
||||
|
||||
## Feature engineering
|
||||
|
||||
Features are added by the user inside the `populate_any_indicators()` method of the strategy
|
||||
by prepending indicators with `%`, and labels with `&`.
|
||||
|
||||
There are some important components/structures that the user *must* include when building their feature set; the use of these is shown below:
|
||||
|
||||
```python
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
"""
|
||||
Function designed to automatically generate, name, and merge features
|
||||
from user-indicated timeframes in the configuration file. The user controls the indicators
|
||||
passed to the training/prediction by prepending indicators with `'%-' + coin `
|
||||
(see convention below). I.e., the user should not prepend any supporting metrics
|
||||
(e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the
|
||||
model.
|
||||
:param pair: pair to be used as informative
|
||||
:param df: strategy dataframe which will receive merges from informatives
|
||||
:param tf: timeframe of the dataframe which will modify the feature names
|
||||
:param informative: the dataframe associated with the informative pair
|
||||
:param coin: the name of the coin which will modify the feature names.
|
||||
"""
|
||||
|
||||
coin = pair.split('/')[0]
|
||||
|
||||
if informative is None:
|
||||
informative = self.dp.get_pair_dataframe(pair, tf)
|
||||
|
||||
# first loop is automatically duplicating indicators for time periods
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
|
||||
t = int(t)
|
||||
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
|
||||
bollinger = qtpylib.bollinger_bands(
|
||||
qtpylib.typical_price(informative), window=t, stds=2.2
|
||||
)
|
||||
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
|
||||
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
|
||||
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
|
||||
|
||||
informative[f"%-{coin}bb_width-period_{t}"] = (
|
||||
informative[f"{coin}bb_upperband-period_{t}"]
|
||||
- informative[f"{coin}bb_lowerband-period_{t}"]
|
||||
) / informative[f"{coin}bb_middleband-period_{t}"]
|
||||
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
|
||||
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
|
||||
)
|
||||
|
||||
informative[f"%-{coin}relative_volume-period_{t}"] = (
|
||||
informative["volume"] / informative["volume"].rolling(t).mean()
|
||||
)
|
||||
|
||||
indicators = [col for col in informative if col.startswith("%")]
|
||||
# This loop duplicates and shifts all indicators to add a sense of recency to data
|
||||
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
|
||||
if n == 0:
|
||||
continue
|
||||
informative_shift = informative[indicators].shift(n)
|
||||
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
|
||||
informative = pd.concat((informative, informative_shift), axis=1)
|
||||
|
||||
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
|
||||
skip_columns = [
|
||||
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
|
||||
]
|
||||
df = df.drop(columns=skip_columns)
|
||||
|
||||
# Add generalized indicators here (because in live, it will call this
|
||||
# function to populate indicators during training). Notice how we ensure not to
|
||||
# add them multiple times
|
||||
if set_generalized_indicators:
|
||||
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
|
||||
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
|
||||
|
||||
# user adds targets here by prepending them with &- (see convention below)
|
||||
# If user wishes to use multiple targets, a multioutput prediction model
|
||||
# needs to be used such as templates/CatboostPredictionMultiModel.py
|
||||
df["&-s_close"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.mean()
|
||||
/ df["close"]
|
||||
- 1
|
||||
)
|
||||
|
||||
return df
|
||||
```
|
||||
|
||||
In the presented example strategy, the user does not wish to pass the `bb_lowerband` as a feature to the model,
|
||||
and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the
|
||||
model for training/prediction and has therefore prepended it with `%`.
|
||||
|
||||
The `include_timeframes` in the example config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the present case, the user is asking for the
|
||||
`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
|
||||
|
||||
The user can ask for each of the defined features to be included also from
|
||||
informative pairs using the `include_corr_pairlist`. This means that the feature
|
||||
set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD`).
|
||||
|
||||
`include_shifted_candles` indicates the number of previous
|
||||
candles to include in the feature set. For example, `include_shifted_candles: 2` tells
|
||||
FreqAI to include the past 2 candles for each of the features in the feature set.
|
||||
|
||||
In total, the number of features the user of the presented example strat has created is:
|
||||
length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
|
||||
$= 3 * 3 * 3 * 2 * 2 = 108$.
|
||||
|
||||
Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`).
|
||||
This is where the user will add single features and labels to their feature set to avoid duplication of them from
|
||||
various configuration parameters that multiply the feature set, such as `include_timeframes`.
|
||||
|
||||
!!! Note
|
||||
Features **must** be defined in `populate_any_indicators()`. Definining FreqAI features in `populate_indicators()`
|
||||
will cause the algorithm to fail in live/dry mode. If the user wishes to add generalized features that are not associated with
|
||||
a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()`
|
||||
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
|
||||
|
||||
```python
|
||||
def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False):
|
||||
|
||||
...
|
||||
|
||||
# Add generalized indicators here (because in live, it will call only this function to populate
|
||||
# indicators for retraining). Notice how we ensure not to add them multiple times by associating
|
||||
# these generalized indicators to the basepair/timeframe
|
||||
if set_generalized_indicators:
|
||||
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
|
||||
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
|
||||
|
||||
# user adds targets here by prepending them with &- (see convention below)
|
||||
# If user wishes to use multiple targets, a multioutput prediction model
|
||||
# needs to be used such as templates/CatboostPredictionMultiModel.py
|
||||
df["&-s_close"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.mean()
|
||||
/ df["close"]
|
||||
- 1
|
||||
)
|
||||
```
|
||||
|
||||
(Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.)
|
||||
|
||||
## Setting classifier targets
|
||||
|
||||
FreqAI includes the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. The user should take care to set the classes using strings:
|
||||
|
||||
```python
|
||||
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
|
||||
```
|
||||
|
||||
Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column.
|
||||
|
||||
## Running FreqAI
|
||||
|
||||
There are two ways to train and deploy an adaptive machine learning model. FreqAI enables live deployment as well as backtesting analyses. In both cases, a model is trained periodically, as shown in the following figure.
|
||||
|
||||
|
||||
|
||||
### Running the model live
|
||||
|
||||
FreqAI can be run dry/live using the following command:
|
||||
|
||||
```bash
|
||||
freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor
|
||||
```
|
||||
|
||||
By default, FreqAI will not find any existing models and will start by training a new one
|
||||
based on the user's configuration settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If the user does not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, the user can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours.
|
||||
|
||||
If the user wishes to start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), the user only needs to reuse
|
||||
the same `identifier` parameter:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"identifier": "example",
|
||||
"live_retrain_hours": 0.5
|
||||
}
|
||||
```
|
||||
|
||||
In this case, although FreqAI will initiate with a
|
||||
pre-trained model, it will still check to see how much time has elapsed since the model was trained,
|
||||
and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will retrain.
|
||||
|
||||
### Backtesting
|
||||
|
||||
The FreqAI backtesting module can be executed with the following command:
|
||||
|
||||
```bash
|
||||
freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
|
||||
```
|
||||
|
||||
Backtesting mode requires the user to have the data [pre-downloaded](#downloading-data-for-backtesting) (unlike in dry/live mode where FreqAI automatically downloads the necessary data). The user should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the user-set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration).
|
||||
|
||||
If this command has never been executed with the existing config file, it will train a new model
|
||||
for each pair, for each backtesting window within the expanded `--timerange`.
|
||||
|
||||
!!! Note "Model reuse"
|
||||
Once the training is completed, the user can execute the backtesting again with the same config file and
|
||||
FreqAI will find the trained models and load them instead of spending time training. This is useful
|
||||
if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. If the user
|
||||
*wants* to retrain a new model with the same config file, then they should simply change the `identifier`.
|
||||
This way, the user can return to using any model they wish by simply specifying the `identifier`.
|
||||
|
||||
---
|
||||
|
||||
### Hyperopt
|
||||
|
||||
Users can hyperopt using the same command as typical [hyperopt](hyperopt.md):
|
||||
|
||||
```bash
|
||||
freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --timerange 20220428-20220507
|
||||
```
|
||||
|
||||
Users need to have the data pre-downloaded in the same fashion as if they were doing a FreqAI [backtest](#backtesting). In addition, users must consider some restrictions when trying to [Hyperopt](hyperopt.md) FreqAI strategies:
|
||||
|
||||
- The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI.
|
||||
- It's not possible to hyperopt indicators in `populate_any_indicators()` function. This means that the user cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
|
||||
- The [Backtesting](#backtesting) instructions also apply to Hyperopt.
|
||||
|
||||
The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. Users need to focus on hyperopting parameters that are not used in their FreqAI features. For example, users should not try to hyperopt rolling window lengths in their feature creation, or any of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
|
||||
|
||||
A good example of a hyperoptable parameter in FreqAI is a value for `DI_values` beyond which we consider outliers and below which we consider inliers:
|
||||
|
||||
```python
|
||||
di_max = IntParameter(low=1, high=20, default=10, space='buy', optimize=True, load=True)
|
||||
dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1, 0)
|
||||
```
|
||||
|
||||
Which would help the user understand the appropriate Dissimilarity Index values for their particular parameter space.
|
||||
|
||||
### Deciding the size of the sliding training window and backtesting duration
|
||||
|
||||
The user defines the backtesting timerange with the typical `--timerange` parameter in the
|
||||
configuration file. The duration of the sliding training window is set by `train_period_days`, whilst
|
||||
`backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be
|
||||
a float to indicate sub-daily retraining in live/dry mode). In the presented example config,
|
||||
the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days.
|
||||
This means that if the user sets `--timerange 20210501-20210701`,
|
||||
FreqAI will train have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks). After the training of the model, FreqAI will backtest the subsequent 7 days. The "sliding window" then moves one week forward (emulating FreqAI retraining once per week in live mode) and the new model uses the previous 30 days (including the 7 days used for backtesting by the previous model) to train. This is repeated until the end of `--timerange`.
|
||||
|
||||
!!! Note
|
||||
Although fractional `backtest_period_days` is allowed, the user should be aware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. Because of this, a true backtest of FreqAI adaptive training would take a *very* long time. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run.
|
||||
|
||||
### Downloading data for backtesting
|
||||
Live/dry instances will download the data automatically for the user, but users who wish to use backtesting functionality still need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). FreqAI users need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that they have a sufficient amount of training data *before* the start of their backtesting timerange. The amount of additional data can be roughly estimated by moving the start date of the timerange backwards by `train_period_days` and the `startup_candle_count` ([details](#setting-the-startupcandlecount)) from the beginning of the desired backtesting timerange.
|
||||
|
||||
As an example, if we wish to backtest the `--timerange` above of `20210501-20210701`, and we use the example config which sets `train_period_days` to 15. The startup candle count is 40 on a maximum `include_timeframes` of 1h. We would need 20210501 - 15 days - 40 * 1h / 24 hours = 20210414 (16.7 days earlier than the start of the desired training timerange).
|
||||
|
||||
### Defining model expirations
|
||||
|
||||
During dry/live mode, FreqAI trains each coin pair sequentially (on separate threads/GPU from the main Freqtrade bot). This means that there is always an age discrepancy between models. If a user is training on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. This may be undesirable if the characteristic time scale (the trade duration target) for a strategy is less than 4 hours. The user can decide to only make trade entries if the model is less than
|
||||
a certain number of hours old by setting the `expiration_hours` in the config file:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"expiration_hours": 0.5,
|
||||
}
|
||||
```
|
||||
|
||||
In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old.
|
||||
|
||||
### Purging old model data
|
||||
|
||||
FreqAI stores new model files each time it retrains. These files become obsolete as new models are trained and FreqAI adapts to new market conditions. Users planning to leave FreqAI running for extended periods of time with high frequency retraining should enable `purge_old_models` in their config:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"purge_old_models": true,
|
||||
}
|
||||
```
|
||||
|
||||
This will automatically purge all models older than the two most recently trained ones.
|
||||
|
||||
### Returning additional info from training
|
||||
|
||||
The user may find that there are some important metrics that they'd like to return to the strategy at the end of each model training.
|
||||
The user can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy.
|
||||
The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is
|
||||
the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of the particular target (label) during the most recent training.
|
||||
An example, where the user wants to use live metrics from the trade database, is shown below:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"extra_returns_per_train": {"total_profit": 4}
|
||||
}
|
||||
```
|
||||
|
||||
The user needs to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, this is the value that will be returned.
|
||||
|
||||
### Setting up a follower
|
||||
|
||||
The user can define:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"follow_mode": true,
|
||||
"identifier": "example"
|
||||
}
|
||||
```
|
||||
|
||||
to indicate to the bot that it should not train models, but instead should look for models trained by a leader with the same `identifier`. In this example, the user has a leader bot with the `identifier: "example"`. The leader bot is already running or launching simultaneously as the follower.
|
||||
The follower will load models created by the leader and inference them to obtain predictions.
|
||||
|
||||
## Data manipulation techniques
|
||||
|
||||
### Feature normalization
|
||||
|
||||
The feature set created by the user is automatically normalized to the training data. This includes all test data and unseen prediction data (dry/live/backtest).
|
||||
|
||||
### Reducing data dimensionality with Principal Component Analysis
|
||||
|
||||
Users can reduce the dimensionality of their features by activating the `principal_component_analysis` in the config:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"principal_component_analysis": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
This will perform PCA on the features and reduce the dimensionality of the data so that the explained variance of the data set is >= 0.999.
|
||||
|
||||
### Stratifying the data for training and testing the model
|
||||
|
||||
The user can stratify (group) the training/testing data using:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"stratify_training_data": 3
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
This will split the data chronologically so that every Xth data point is used to test the model after training. In the
|
||||
example above, the user is asking for every third data point in the dataframe to be used for
|
||||
testing; the other points are used for training.
|
||||
|
||||
The test data is used to evaluate the performance of the model after training. If the test score is high, the model is able to capture the behavior of the data well. If the test score is low, either the model either does not capture the complexity of the data, the test data is significantly different from the train data, or a different model should be used.
|
||||
|
||||
### Using the `inlier_metric`
|
||||
|
||||
The `inlier_metric` is a metric aimed at quantifying how different a prediction data point is from the most recent historic data points.
|
||||
|
||||
User can set `inlier_metric_window` to set the look back window. FreqAI will compute the distance between the present prediction point and each of the previous data points (total of `inlier_metric_window` points).
|
||||
|
||||
This function goes one step further - during training, it computes the `inlier_metric` for all training data points and builds weibull distributions for each each lookback point. The cumulative distribution function for the weibull distribution is used to produce a quantile for each of the data points. The quantiles for each lookback point are averaged to create the `inlier_metric`.
|
||||
|
||||
FreqAI adds this `inlier_metric` score to the training features! In other words, your model is trained to recognize how this temporal inlier metric is related to the user set labels.
|
||||
|
||||
This function does **not** remove outliers from the data set.
|
||||
|
||||
### Controlling the model learning process
|
||||
|
||||
Model training parameters are unique to the machine learning library selected by the user. FreqAI allows the user to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration file (found in `config_examples/config_freqai.example.json`) show some of the example parameters associated with `Catboost` and `LightGBM`, but the user can add any parameters available in those libraries.
|
||||
|
||||
Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function.
|
||||
|
||||
FreqAI includes some additional parameters such as `weight_factor`, which allows the user to weight more recent data more strongly
|
||||
than past data via an exponential function:
|
||||
|
||||
$$ W_i = \exp(\frac{-i}{\alpha*n}) $$
|
||||
|
||||
where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points (candles) in a feature set.
|
||||
|
||||
|
||||
|
||||
`train_test_split()` has a parameters called `shuffle` that allows the user to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data.
|
||||
|
||||
Finally, `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented example config,
|
||||
the user is asking for `labels` that are 24 candles in the future.
|
||||
|
||||
### Outlier removal
|
||||
|
||||
#### Removing outliers with the Dissimilarity Index
|
||||
|
||||
The user can tell FreqAI to remove outlier data points from the training/test data sets using a Dissimilarity Index by including the following statement in the config:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"DI_threshold": 1
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty.
|
||||
|
||||
To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points:
|
||||
|
||||
$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$
|
||||
|
||||
where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean of the average distances:
|
||||
|
||||
$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$
|
||||
|
||||
$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data:
|
||||
|
||||
$$ d_k = \arg \min d_{k,i} $$
|
||||
|
||||
which enables the estimation of the Dissimilarity Index as:
|
||||
|
||||
$$ DI_k = d_k/\overline{d} $$
|
||||
|
||||
The user can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model.
|
||||
|
||||
Below is a figure that describes the DI for a 3D data set.
|
||||
|
||||
|
||||
|
||||
#### Removing outliers using a Support Vector Machine (SVM)
|
||||
|
||||
The user can tell FreqAI to remove outlier data points from the training/test data sets using a SVM by setting:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"use_SVM_to_remove_outliers": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
FreqAI will train an SVM on the training data (or components of it if the user activated
|
||||
`principal_component_analysis`) and remove any data point that the SVM deems to be beyond the feature space.
|
||||
|
||||
The parameter `shuffle` is by default set to `False` to ensure consistent results. If it is set to `True`, running the SVM multiple times on the same data set might result in different outcomes due to `max_iter` being to low for the algorithm to reach the demanded `tol`. Increasing `max_iter` solves this issue but causes the procedure to take longer time.
|
||||
|
||||
The parameter `nu`, *very* broadly, is the amount of data points that should be considered outliers.
|
||||
|
||||
#### Removing outliers with DBSCAN
|
||||
|
||||
The user can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config:
|
||||
|
||||
```json
|
||||
"freqai": {
|
||||
"feature_parameters" : {
|
||||
"use_DBSCAN_to_remove_outliers": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be.
|
||||
|
||||
Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$.
|
||||
|
||||
|
||||
|
||||
FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](#https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html)) with `min_samples` ($N$) taken as double the no. of user-defined features, and `eps` ($\varepsilon$) taken as the longest distance in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set.
|
||||
|
||||
## Additional information
|
||||
|
||||
### Common pitfalls
|
||||
|
||||
FreqAI cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
|
||||
This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively,
|
||||
it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends
|
||||
new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShuffleFilter` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume).
|
||||
`FreqAI` cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
|
||||
This is for performance reasons - `FreqAI` relies on making quick predictions/retrains. To do this effectively,
|
||||
it needs to download all the training data at the beginning of a dry/live instance. `FreqAI` stores and appends
|
||||
new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, `FreqAI` does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume).
|
||||
|
||||
## Credits
|
||||
|
||||
FreqAI was developed by a group of individuals who all contributed specific skillsets to the project.
|
||||
`FreqAI` is developed by a group of individuals who all contribute specific skillsets to the project.
|
||||
|
||||
Conception and software development:
|
||||
Robert Caulk @robcaulk
|
||||
|
||||
Theoretical brainstorming, data analysis:
|
||||
Theoretical brainstorming and data analysis:
|
||||
Elin Törnquist @th0rntwig
|
||||
|
||||
Code review, software architecture brainstorming:
|
||||
Code review and software architecture brainstorming:
|
||||
@xmatthias
|
||||
|
||||
Software development:
|
||||
Wagner Costa @wagnercosta
|
||||
|
||||
Beta testing and bug reporting:
|
||||
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm,
|
||||
Juha Nykänen @suikula, Wagner Costa @wagnercosta
|
||||
|
||||
|
||||
## Reinforcement Learning
|
||||
|
||||
Setting up and running a Reinforcement Learning model is as quick and simple as running a Regressor. Users can start training and trading live from example files using:
|
||||
|
||||
```bash
|
||||
freqtrade trade --freqaimodel ReinforcementLearner --strategy ReinforcementLearningExample5ac --strategy-path freqtrade/freqai/example_strats --config config_examples/config_freqai-rl.example.json
|
||||
```
|
||||
|
||||
As users begin to modify the strategy and the prediction model, they will quickly realize some important differences between the Reinforcement Learner and the Regressors/Classifiers. Firstly, the strategy does not set a target value (no labels!). Instead, the user sets a `calculate_reward()` function inside their custom `ReinforcementLearner.py` file. A default `calculate_reward()` is provided inside `prediction_models/ReinforcementLearner.py` to give users the necessary building blocks to start their own models. It is inside the `calculate_reward()` where users express their creative theories about the market. For example, the user wants to reward their agent when it makes a winning trade, and penalize the agent when it makes a losing trade. Or perhaps, the user wishes to reward the agnet for entering trades, and penalize the agent for sitting in trades too long. Below we show examples of how these rewards are all calculated:
|
||||
|
||||
```python
|
||||
class MyRLEnv(Base5ActionRLEnv):
|
||||
"""
|
||||
User made custom environment. This class inherits from BaseEnvironment and gym.env.
|
||||
Users can override any functions from those parent classes. Here is an example
|
||||
of a user customized `calculate_reward()` function.
|
||||
"""
|
||||
def calculate_reward(self, action):
|
||||
# first, penalize if the action is not valid
|
||||
if not self._is_valid(action):
|
||||
return -2
|
||||
pnl = self.get_unrealized_profit()
|
||||
rew = np.sign(pnl) * (pnl + 1)
|
||||
factor = 100
|
||||
# reward agent for entering trades
|
||||
if action in (Actions.Long_enter.value, Actions.Short_enter.value) \
|
||||
and self._position == Positions.Neutral:
|
||||
return 25
|
||||
# discourage agent from not entering trades
|
||||
if action == Actions.Neutral.value and self._position == Positions.Neutral:
|
||||
return -1
|
||||
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
|
||||
trade_duration = self._current_tick - self._last_trade_tick
|
||||
if trade_duration <= max_trade_duration:
|
||||
factor *= 1.5
|
||||
elif trade_duration > max_trade_duration:
|
||||
factor *= 0.5
|
||||
# discourage sitting in position
|
||||
if self._position in (Positions.Short, Positions.Long) and \
|
||||
action == Actions.Neutral.value:
|
||||
return -1 * trade_duration / max_trade_duration
|
||||
# close long
|
||||
if action == Actions.Long_exit.value and self._position == Positions.Long:
|
||||
if pnl > self.profit_aim * self.rr:
|
||||
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
|
||||
return float(rew * factor)
|
||||
# close short
|
||||
if action == Actions.Short_exit.value and self._position == Positions.Short:
|
||||
if pnl > self.profit_aim * self.rr:
|
||||
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
|
||||
return float(rew * factor)
|
||||
return 0.
|
||||
```
|
||||
|
||||
After users realize there are no labels to set, they will soon understand that the agent is making its "own" entry and exit decisions. This makes strategy construction rather simple. The entry and exit signals come from the agent in the form of an integer - which are used directly to decide entries and exits in the strategy:
|
||||
|
||||
```python
|
||||
def populate_any_indicators(
|
||||
self, pair, df, tf, informative=None, set_generalized_indicators=False
|
||||
):
|
||||
...
|
||||
|
||||
if set_generalized_indicators:
|
||||
# For RL, there are no direct targets to set. This sets the base action to neutral
|
||||
# until the agent sends an action.
|
||||
df["&-action"] = 0
|
||||
|
||||
return df
|
||||
|
||||
```
|
||||
|
||||
and then the `&-action` will be used in `populate_entry/exit` functions:
|
||||
|
||||
```python
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]
|
||||
|
||||
if enter_long_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
|
||||
] = (1, "long")
|
||||
|
||||
enter_short_conditions = [df["do_predict"] == 1, df["&-action"] == 3]
|
||||
|
||||
if enter_short_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
|
||||
] = (1, "short")
|
||||
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long_conditions = [df["do_predict"] == 1, df["&-action"] == 2]
|
||||
if exit_long_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
|
||||
|
||||
exit_short_conditions = [df["do_predict"] == 1, df["&-action"] == 4]
|
||||
if exit_short_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
|
||||
|
||||
return df
|
||||
```
|
||||
|
||||
Users should be careful to consider that `&-action` depends on which environment they choose to use. The example above shows 5 actions, where 0 is neutral, 1 is enter long, 2 is exit long, 3 is enter short and 4 is exit short.
|
||||
|
||||
### Creating a custom agent
|
||||
|
||||
Users can inherit from `stable_baselines3` and customize anything they wish about their agent. Doing this is for advanced users only, an example is presented in `freqai/RL/ReinforcementLearnerCustomAgent.py`
|
||||
|
||||
### Using Tensorboard
|
||||
|
||||
Reinforcement Learning models benefit from tracking training metrics. FreqAI has integrated Tensorboard to allow users to track training and evaluation performance across all coins and across all retrainings. To start, the user should ensure Tensorboard is installed on their computer:
|
||||
|
||||
```bash
|
||||
pip3 install tensorboard
|
||||
```
|
||||
|
||||
Next, the user can activate Tensorboard with the following command:
|
||||
|
||||
```bash
|
||||
cd freqtrade
|
||||
tensorboard --logdir user_data/models/unique-id
|
||||
```
|
||||
|
||||
where `unique-id` is the `identifier` set in the `freqai` configuration file.
|
||||
|
||||
|
||||
Stefan Gehring @bloodhunter4rc, @longyu, Andrew Robert Lawless @paranoidandy, Pascal Schmidt @smidelis, Ryan McMullan @smarmau,
|
||||
Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza
|
||||
|
||||
@@ -22,6 +22,7 @@ You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged
|
||||
|
||||
* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
|
||||
* [`VolumePairList`](#volume-pair-list)
|
||||
* [`ProducerPairList`](#producerpairlist)
|
||||
* [`AgeFilter`](#agefilter)
|
||||
* [`OffsetFilter`](#offsetfilter)
|
||||
* [`PerformanceFilter`](#performancefilter)
|
||||
@@ -84,7 +85,7 @@ Filtering instances (not the first position in the list) will not apply any cach
|
||||
|
||||
You can define a minimum volume with `min_value` - which will filter out pairs with a volume lower than the specified value in the specified timerange.
|
||||
|
||||
### VolumePairList Advanced mode
|
||||
##### VolumePairList Advanced mode
|
||||
|
||||
`VolumePairList` can also operate in an advanced mode to build volume over a given timerange of specified candle size. It utilizes exchange historical candle data, builds a typical price (calculated by (open+high+low)/3) and multiplies the typical price with every candle's volume. The sum is the `quoteVolume` over the given range. This allows different scenarios, for a more smoothened volume, when using longer ranges with larger candle sizes, or the opposite when using a short range with small candles.
|
||||
|
||||
@@ -146,6 +147,32 @@ More sophisticated approach can be used, by using `lookback_timeframe` for candl
|
||||
!!! Note
|
||||
`VolumePairList` does not support backtesting mode.
|
||||
|
||||
#### ProducerPairList
|
||||
|
||||
With `ProducerPairList`, you can reuse the pairlist from a [Producer](producer-consumer.md) without explicitly defining the pairlist on each consumer.
|
||||
|
||||
[Consumer mode](producer-consumer.md) is required for this pairlist to work.
|
||||
|
||||
The pairlist will perform a check on active pairs against the current exchange configuration to avoid attempting to trade on invalid markets.
|
||||
|
||||
You can limit the length of the pairlist with the optional parameter `number_assets`. Using `"number_assets"=0` or omitting this key will result in the reuse of all producer pairs valid for the current setup.
|
||||
|
||||
```json
|
||||
"pairlists": [
|
||||
{
|
||||
"method": "ProducerPairList",
|
||||
"number_assets": 5,
|
||||
"producer_name": "default",
|
||||
}
|
||||
],
|
||||
```
|
||||
|
||||
|
||||
!!! Tip "Combining pairlists"
|
||||
This pairlist can be combined with all other pairlists and filters for further pairlist reduction, and can also act as an "additional" pairlist, on top of already defined pairs.
|
||||
`ProducerPairList` can also be used multiple times in sequence, combining the pairs from multiple producers.
|
||||
Obviously in complex such configurations, the Producer may not provide data for all pairs, so the strategy must be fit for this.
|
||||
|
||||
#### AgeFilter
|
||||
|
||||
Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`) or more than `max_days_listed` days (defaults `None` mean infinity).
|
||||
|
||||
163
docs/producer-consumer.md
Normal file
163
docs/producer-consumer.md
Normal file
@@ -0,0 +1,163 @@
|
||||
# Producer / Consumer mode
|
||||
|
||||
freqtrade provides a mechanism whereby an instance (also called `consumer`) may listen to messages from an upstream freqtrade instance (also called `producer`) using the message websocket. Mainly, `analyzed_df` and `whitelist` messages. This allows the reuse of computed indicators (and signals) for pairs in multiple bots without needing to compute them multiple times.
|
||||
|
||||
See [Message Websocket](rest-api.md#message-websocket) in the Rest API docs for setting up the `api_server` configuration for your message websocket (this will be your producer).
|
||||
|
||||
!!! Note
|
||||
We strongly recommend to set `ws_token` to something random and known only to yourself to avoid unauthorized access to your bot.
|
||||
|
||||
## Configuration
|
||||
|
||||
Enable subscribing to an instance by adding the `external_message_consumer` section to the consumer's config file.
|
||||
|
||||
```json
|
||||
{
|
||||
//...
|
||||
"external_message_consumer": {
|
||||
"enabled": true,
|
||||
"producers": [
|
||||
{
|
||||
"name": "default", // This can be any name you'd like, default is "default"
|
||||
"host": "127.0.0.1", // The host from your producer's api_server config
|
||||
"port": 8080, // The port from your producer's api_server config
|
||||
"ws_token": "sercet_Ws_t0ken" // The ws_token from your producer's api_server config
|
||||
}
|
||||
],
|
||||
// The following configurations are optional, and usually not required
|
||||
// "wait_timeout": 300,
|
||||
// "ping_timeout": 10,
|
||||
// "sleep_time": 10,
|
||||
// "remove_entry_exit_signals": false,
|
||||
// "message_size_limit": 8
|
||||
}
|
||||
//...
|
||||
}
|
||||
```
|
||||
|
||||
| Parameter | Description |
|
||||
|------------|-------------|
|
||||
| `enabled` | **Required.** Enable consumer mode. If set to false, all other settings in this section are ignored.<br>*Defaults to `false`.*<br> **Datatype:** boolean .
|
||||
| `producers` | **Required.** List of producers <br> **Datatype:** Array.
|
||||
| `producers.name` | **Required.** Name of this producer. This name must be used in calls to `get_producer_pairs()` and `get_producer_df()` if more than one producer is used.<br> **Datatype:** string
|
||||
| `producers.host` | **Required.** The hostname or IP address from your producer.<br> **Datatype:** string
|
||||
| `producers.port` | **Required.** The port matching the above host.<br> **Datatype:** string
|
||||
| `producers.ws_token` | **Required.** `ws_token` as configured on the producer.<br> **Datatype:** string
|
||||
| | **Optional settings**
|
||||
| `wait_timeout` | Timeout until we ping again if no message is received. <br>*Defaults to `300`.*<br> **Datatype:** Integer - in seconds.
|
||||
| `wait_timeout` | Ping timeout <br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
|
||||
| `sleep_time` | Sleep time before retrying to connect.<br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
|
||||
| `remove_entry_exit_signals` | Remove signal columns from the dataframe (set them to 0) on dataframe receipt.<br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
|
||||
| `message_size_limit` | Size limit per message<br>*Defaults to `8`.*<br> **Datatype:** Integer - Megabytes.
|
||||
|
||||
Instead of (or as well as) calculating indicators in `populate_indicators()` the follower instance listens on the connection to a producer instance's messages (or multiple producer instances in advanced configurations) and requests the producer's most recently analyzed dataframes for each pair in the active whitelist.
|
||||
|
||||
A consumer instance will then have a full copy of the analyzed dataframes without the need to calculate them itself.
|
||||
|
||||
## Examples
|
||||
|
||||
### Example - Producer Strategy
|
||||
|
||||
A simple strategy with multiple indicators. No special considerations are required in the strategy itself.
|
||||
|
||||
```py
|
||||
class ProducerStrategy(IStrategy):
|
||||
#...
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Calculate indicators in the standard freqtrade way which can then be broadcast to other instances
|
||||
"""
|
||||
dataframe['rsi'] = ta.RSI(dataframe)
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
||||
dataframe['bb_lowerband'] = bollinger['lower']
|
||||
dataframe['bb_middleband'] = bollinger['mid']
|
||||
dataframe['bb_upperband'] = bollinger['upper']
|
||||
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Populates the entry signal for the given dataframe
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
(qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &
|
||||
(dataframe['tema'] <= dataframe['bb_middleband']) &
|
||||
(dataframe['tema'] > dataframe['tema'].shift(1)) &
|
||||
(dataframe['volume'] > 0)
|
||||
),
|
||||
'enter_long'] = 1
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
||||
!!! Tip "FreqAI"
|
||||
You can use this to setup [FreqAI](freqai.md) on a powerful machine, while you run consumers on simple machines like raspberries, which can interpret the signals generated from the producer in different ways.
|
||||
|
||||
|
||||
### Example - Consumer Strategy
|
||||
|
||||
A logically equivalent strategy which calculates no indicators itself, but will have the same analyzed dataframes available to make trading decisions based on the indicators calculated in the producer. In this example the consumer has the same entry criteria, however this is not necessary. The consumer may use different logic to enter/exit trades, and only use the indicators as specified.
|
||||
|
||||
```py
|
||||
class ConsumerStrategy(IStrategy):
|
||||
#...
|
||||
process_only_new_candles = False # required for consumers
|
||||
|
||||
_columns_to_expect = ['rsi_default', 'tema_default', 'bb_middleband_default']
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Use the websocket api to get pre-populated indicators from another freqtrade instance.
|
||||
Use `self.dp.get_producer_df(pair)` to get the dataframe
|
||||
"""
|
||||
pair = metadata['pair']
|
||||
timeframe = self.timeframe
|
||||
|
||||
producer_pairs = self.dp.get_producer_pairs()
|
||||
# You can specify which producer to get pairs from via:
|
||||
# self.dp.get_producer_pairs("my_other_producer")
|
||||
|
||||
# This func returns the analyzed dataframe, and when it was analyzed
|
||||
producer_dataframe, _ = self.dp.get_producer_df(pair)
|
||||
# You can get other data if the producer makes it available:
|
||||
# self.dp.get_producer_df(
|
||||
# pair,
|
||||
# timeframe="1h",
|
||||
# candle_type=CandleType.SPOT,
|
||||
# producer_name="my_other_producer"
|
||||
# )
|
||||
|
||||
if not producer_dataframe.empty:
|
||||
# If you plan on passing the producer's entry/exit signal directly,
|
||||
# specify ffill=False or it will have unintended results
|
||||
merged_dataframe = merge_informative_pair(dataframe, producer_dataframe,
|
||||
timeframe, timeframe,
|
||||
append_timeframe=False,
|
||||
suffix="default")
|
||||
return merged_dataframe
|
||||
else:
|
||||
dataframe[self._columns_to_expect] = 0
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
Populates the entry signal for the given dataframe
|
||||
"""
|
||||
# Use the dataframe columns as if we calculated them ourselves
|
||||
dataframe.loc[
|
||||
(
|
||||
(qtpylib.crossed_above(dataframe['rsi_default'], self.buy_rsi.value)) &
|
||||
(dataframe['tema_default'] <= dataframe['bb_middleband_default']) &
|
||||
(dataframe['tema_default'] > dataframe['tema_default'].shift(1)) &
|
||||
(dataframe['volume'] > 0)
|
||||
),
|
||||
'enter_long'] = 1
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
||||
!!! Tip "Using upstream signals"
|
||||
By setting `remove_entry_exit_signals=false`, you can also use the producer's signals directly. They should be available as `enter_long_default` (assuming `suffix="default"` was used) - and can be used as either signal directly, or as additional indicator.
|
||||
@@ -1,6 +1,6 @@
|
||||
markdown==3.3.7
|
||||
mkdocs==1.3.1
|
||||
mkdocs-material==8.5.2
|
||||
mkdocs-material==8.5.3
|
||||
mdx_truly_sane_lists==1.3
|
||||
pymdown-extensions==9.5
|
||||
jinja2==3.1.2
|
||||
|
||||
@@ -31,7 +31,8 @@ Sample configuration:
|
||||
"jwt_secret_key": "somethingrandom",
|
||||
"CORS_origins": [],
|
||||
"username": "Freqtrader",
|
||||
"password": "SuperSecret1!"
|
||||
"password": "SuperSecret1!",
|
||||
"ws_token": "sercet_Ws_t0ken"
|
||||
},
|
||||
```
|
||||
|
||||
@@ -66,7 +67,7 @@ secrets.token_hex()
|
||||
|
||||
!!! Danger "Password selection"
|
||||
Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
|
||||
Also change `jwt_secret_key` to something random (no need to remember this, but it'll be used to encrypt your session, so it better be something unique!).
|
||||
Also change `jwt_secret_key` to something random (no need to remember this, but it'll be used to encrypt your session, so it better be something unique!).
|
||||
|
||||
### Configuration with docker
|
||||
|
||||
@@ -93,7 +94,6 @@ Make sure that the following 2 lines are available in your docker-compose file:
|
||||
!!! Danger "Security warning"
|
||||
By using `8080:8080` in the docker port mapping, the API will be available to everyone connecting to the server under the correct port, so others may be able to control your bot.
|
||||
|
||||
|
||||
## Rest API
|
||||
|
||||
### Consuming the API
|
||||
@@ -274,7 +274,7 @@ reload_config
|
||||
Reload configuration.
|
||||
|
||||
show_config
|
||||
|
||||
|
||||
Returns part of the configuration, relevant for trading operations.
|
||||
|
||||
start
|
||||
@@ -322,6 +322,73 @@ whitelist
|
||||
|
||||
```
|
||||
|
||||
### Message WebSocket
|
||||
|
||||
The API Server includes a websocket endpoint for subscribing to RPC messages from the freqtrade Bot.
|
||||
This can be used to consume real-time data from your bot, such as entry/exit fill messages, whitelist changes, populated indicators for pairs, and more.
|
||||
|
||||
This is also used to setup [Producer/Consumer mode](producer-consumer.md) in Freqtrade.
|
||||
|
||||
Assuming your rest API is set to `127.0.0.1` on port `8080`, the endpoint is available at `http://localhost:8080/api/v1/message/ws`.
|
||||
|
||||
To access the websocket endpoint, the `ws_token` is required as a query parameter in the endpoint URL.
|
||||
|
||||
To generate a safe `ws_token` you can run the following code:
|
||||
|
||||
``` python
|
||||
>>> import secrets
|
||||
>>> secrets.token_urlsafe(25)
|
||||
'hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q'
|
||||
```
|
||||
|
||||
You would then add that token under `ws_token` in your `api_server` config. Like so:
|
||||
|
||||
``` json
|
||||
"api_server": {
|
||||
"enabled": true,
|
||||
"listen_ip_address": "127.0.0.1",
|
||||
"listen_port": 8080,
|
||||
"verbosity": "error",
|
||||
"enable_openapi": false,
|
||||
"jwt_secret_key": "somethingrandom",
|
||||
"CORS_origins": [],
|
||||
"username": "Freqtrader",
|
||||
"password": "SuperSecret1!",
|
||||
"ws_token": "hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q" // <-----
|
||||
},
|
||||
```
|
||||
|
||||
You can now connect to the endpoint at `http://localhost:8080/api/v1/message/ws?token=hZ-y58LXyX_HZ8O1cJzVyN6ePWrLpNQv4Q`.
|
||||
|
||||
!!! Danger "Reuse of example tokens"
|
||||
Please do not use the above example token. To make sure you are secure, generate a completely new token.
|
||||
|
||||
#### Using the WebSocket
|
||||
|
||||
Once connected to the WebSocket, the bot will broadcast RPC messages to anyone who is subscribed to them. To subscribe to a list of messages, you must send a JSON request through the WebSocket like the one below. The `data` key must be a list of message type strings.
|
||||
|
||||
``` json
|
||||
{
|
||||
"type": "subscribe",
|
||||
"data": ["whitelist", "analyzed_df"] // A list of string message types
|
||||
}
|
||||
```
|
||||
|
||||
For a list of message types, please refer to the RPCMessageType enum in `freqtrade/enums/rpcmessagetype.py`
|
||||
|
||||
Now anytime those types of RPC messages are sent in the bot, you will receive them through the WebSocket as long as the connection is active. They typically take the same form as the request:
|
||||
|
||||
``` json
|
||||
{
|
||||
"type": "analyzed_df",
|
||||
"data": {
|
||||
"key": ["NEO/BTC", "5m", "spot"],
|
||||
"df": {}, // The dataframe
|
||||
"la": "2022-09-08 22:14:41.457786+00:00"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### OpenAPI interface
|
||||
|
||||
To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration.
|
||||
|
||||
@@ -106,6 +106,12 @@ def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_r
|
||||
!!! Note
|
||||
`enter_tag` is limited to 100 characters, remaining data will be truncated.
|
||||
|
||||
!!! Warning
|
||||
There is only one `enter_tag` column, which is used for both long and short trades.
|
||||
As a consequence, this column must be treated as "last write wins" (it's just a dataframe column after all).
|
||||
In fancy situations, where multiple signals collide (or if signals are deactivated again based on different conditions), this can lead to odd results with the wrong tag applied to an entry signal.
|
||||
These results are a consequence of the strategy overwriting prior tags - where the last tag will "stick" and will be the one freqtrade will use.
|
||||
|
||||
## Exit tag
|
||||
|
||||
Similar to [Buy Tagging](#buy-tag), you can also specify a sell tag.
|
||||
|
||||
@@ -643,7 +643,7 @@ This callback is **not** called when there is an open order (either buy or sell)
|
||||
|
||||
Additional Buys are ignored once you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`, but the callback is called anyway looking for partial exits.
|
||||
|
||||
Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position (negative values will decrease your position), no matter if it's a long or short trade. Modifications to leverage are not possible.
|
||||
Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position (negative values will decrease your position), no matter if it's a long or short trade. Modifications to leverage are not possible, and the stake-amount is assumed to be before applying leverage.
|
||||
|
||||
!!! Note "About stake size"
|
||||
Using fixed stake size means it will be the amount used for the first order, just like without position adjustment.
|
||||
|
||||
@@ -37,3 +37,12 @@ pip install -e .
|
||||
# Ensure freqUI is at the latest version
|
||||
freqtrade install-ui
|
||||
```
|
||||
|
||||
### Problems updating
|
||||
|
||||
Update-problems usually come missing dependencies (you didn't follow the above instructions) - or from updated dependencies, which fail to install (for example TA-lib).
|
||||
Please refer to the corresponding installation sections (common problems linked below)
|
||||
|
||||
Common problems and their solutions:
|
||||
|
||||
* [ta-lib update on windows](windows_installation.md#2-install-ta-lib)
|
||||
|
||||
@@ -23,7 +23,7 @@ git clone https://github.com/freqtrade/freqtrade.git
|
||||
|
||||
Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
|
||||
|
||||
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.24-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
|
||||
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.25-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
|
||||
|
||||
Freqtrade provides these dependencies for the latest 3 Python versions (3.8, 3.9 and 3.10) and for 64bit Windows.
|
||||
Other versions must be downloaded from the above link.
|
||||
@@ -34,7 +34,7 @@ python -m venv .env
|
||||
.env\Scripts\activate.ps1
|
||||
# optionally install ta-lib from wheel
|
||||
# Eventually adjust the below filename to match the downloaded wheel
|
||||
pip install build_helpers/TA_Lib-0.4.19-cp38-cp38-win_amd64.whl
|
||||
pip install --find-links build_helpers\ TA-Lib -U
|
||||
pip install -r requirements.txt
|
||||
pip install -e .
|
||||
freqtrade
|
||||
|
||||
Reference in New Issue
Block a user