Merge branch 'develop' into feat/new_args_system
This commit is contained in:
Matthias
@@ -39,7 +39,7 @@ Assume you downloaded the history data from the Bittrex exchange and kept it in
|
||||
You can then use this data for backtesting as follows:
|
||||
|
||||
```bash
|
||||
freqtrade backtesting --datadir user_data/data/bittrex-20180101
|
||||
freqtrade --datadir user_data/data/bittrex-20180101 backtesting
|
||||
```
|
||||
|
||||
#### With a (custom) strategy file
|
||||
@@ -72,6 +72,17 @@ The exported trades can be used for [further analysis](#further-backtest-result-
|
||||
freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json
|
||||
```
|
||||
|
||||
#### Supplying custom fee value
|
||||
|
||||
Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.
|
||||
To account for this in backtesting, you can use `--fee 0.001` to supply this value to backtesting.
|
||||
This fee must be a percentage, and will be applied twice (once for trade entry, and once for trade exit).
|
||||
|
||||
```bash
|
||||
freqtrade backtesting --fee 0.001
|
||||
```
|
||||
|
||||
|
||||
#### Running backtest with smaller testset by using timerange
|
||||
|
||||
Use the `--timerange` argument to change how much of the testset you want to use.
|
||||
@@ -92,12 +103,6 @@ The full timerange specification:
|
||||
- Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
|
||||
- Use tickframes between POSIX timestamps 1527595200 1527618600:
|
||||
`--timerange=1527595200-1527618600`
|
||||
- Use last 123 tickframes of data: `--timerange=-123`
|
||||
- Use first 123 tickframes of data: `--timerange=123-`
|
||||
- Use tickframes from line 123 through 456: `--timerange=123-456`
|
||||
|
||||
!!! warning
|
||||
Be carefull when using non-date functions - these do not allow you to specify precise dates, so if you updated the test-data it will probably use a different dataset.
|
||||
|
||||
## Understand the backtesting result
|
||||
|
||||
@@ -184,6 +189,7 @@ Hence, keep in mind that your performance is an integral mix of all different el
|
||||
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
|
||||
|
||||
- Buys happen at open-price
|
||||
- Sell signal sells happen at open-price of the following candle
|
||||
- Low happens before high for stoploss, protecting capital first.
|
||||
- ROI sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
|
||||
- Stoploss sells happen exactly at stoploss price, even if low was lower
|
||||
@@ -192,6 +198,9 @@ Since backtesting lacks some detailed information about what happens within a ca
|
||||
- Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
|
||||
- Sell-reason does not explain if a trade was positive or negative, just what triggered the sell (this can look odd if negative ROI values are used)
|
||||
|
||||
Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.
|
||||
Also, keep in mind that past results don't guarantee future success.
|
||||
|
||||
### Further backtest-result analysis
|
||||
|
||||
To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
|
||||
|
||||
@@ -119,7 +119,7 @@ user_data/
|
||||
├── backtest_results
|
||||
├── data
|
||||
├── hyperopts
|
||||
├── hyperopts_results
|
||||
├── hyperopt_results
|
||||
├── plot
|
||||
└── strategies
|
||||
```
|
||||
@@ -205,6 +205,8 @@ optional arguments:
|
||||
Specify max_open_trades to use.
|
||||
--stake_amount STAKE_AMOUNT
|
||||
Specify stake_amount.
|
||||
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
|
||||
entry and exit).
|
||||
--eps, --enable-position-stacking
|
||||
Allow buying the same pair multiple times (position
|
||||
stacking).
|
||||
@@ -370,6 +372,8 @@ optional arguments:
|
||||
Specify max_open_trades to use.
|
||||
--stake_amount STAKE_AMOUNT
|
||||
Specify stake_amount.
|
||||
--fee FLOAT Specify fee ratio. Will be applied twice (on trade
|
||||
entry and exit).
|
||||
--stoplosses STOPLOSS_RANGE
|
||||
Defines a range of stoploss values against which edge
|
||||
will assess the strategy. The format is "min,max,step"
|
||||
|
||||
@@ -59,12 +59,15 @@ Mandatory parameters are marked as **Required**, which means that they are requi
|
||||
| `unfilledtimeout.sell` | 10 | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled.
|
||||
| `bid_strategy.ask_last_balance` | 0.0 | **Required.** Set the bidding price. More information [below](#understand-ask_last_balance).
|
||||
| `bid_strategy.use_order_book` | false | Allows buying of pair using the rates in Order Book Bids.
|
||||
| `bid_strategy.order_book_top` | 0 | Bot will use the top N rate in Order Book Bids. Ie. a value of 2 will allow the bot to pick the 2nd bid rate in Order Book Bids.
|
||||
| `bid_strategy.order_book_top` | 0 | Bot will use the top N rate in Order Book Bids. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in Order Book Bids.
|
||||
| `bid_strategy. check_depth_of_market.enabled` | false | Does not buy if the % difference of buy orders and sell orders is met in Order Book.
|
||||
| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | 0 | The % difference of buy orders and sell orders found in Order Book. A value lesser than 1 means sell orders is greater, while value greater than 1 means buy orders is higher.
|
||||
| `ask_strategy.use_order_book` | false | Allows selling of open traded pair using the rates in Order Book Asks.
|
||||
| `ask_strategy.order_book_min` | 0 | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
|
||||
| `ask_strategy.order_book_max` | 0 | Bot will scan from the top min to max Order Book Asks searching for a profitable rate.
|
||||
| `ask_strategy.use_sell_signal` | true | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy).
|
||||
| `ask_strategy.sell_profit_only` | false | Wait until the bot makes a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy).
|
||||
| `ask_strategy.ignore_roi_if_buy_signal` | false | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy).
|
||||
| `order_types` | None | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).
|
||||
| `order_time_in_force` | None | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy).
|
||||
| `exchange.name` | | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename).
|
||||
@@ -78,9 +81,6 @@ Mandatory parameters are marked as **Required**, which means that they are requi
|
||||
| `exchange.ccxt_async_config` | None | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation)
|
||||
| `exchange.markets_refresh_interval` | 60 | The interval in minutes in which markets are reloaded.
|
||||
| `edge` | false | Please refer to [edge configuration document](edge.md) for detailed explanation.
|
||||
| `experimental.use_sell_signal` | false | Use your sell strategy in addition of the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy).
|
||||
| `experimental.sell_profit_only` | false | Waits until you have made a positive profit before taking a sell decision. [Strategy Override](#parameters-in-the-strategy).
|
||||
| `experimental.ignore_roi_if_buy_signal` | false | Does not sell if the buy-signal is still active. Takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy).
|
||||
| `experimental.block_bad_exchanges` | true | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now.
|
||||
| `pairlist.method` | StaticPairList | Use static or dynamic volume-based pairlist. [More information below](#dynamic-pairlists).
|
||||
| `pairlist.config` | None | Additional configuration for dynamic pairlists. [More information below](#dynamic-pairlists).
|
||||
@@ -116,9 +116,9 @@ Values set in the configuration file always overwrite values set in the strategy
|
||||
* `process_only_new_candles`
|
||||
* `order_types`
|
||||
* `order_time_in_force`
|
||||
* `use_sell_signal` (experimental)
|
||||
* `sell_profit_only` (experimental)
|
||||
* `ignore_roi_if_buy_signal` (experimental)
|
||||
* `use_sell_signal` (ask_strategy)
|
||||
* `sell_profit_only` (ask_strategy)
|
||||
* `ignore_roi_if_buy_signal` (ask_strategy)
|
||||
|
||||
### Understand stake_amount
|
||||
|
||||
|
||||
@@ -61,34 +61,6 @@ except:
|
||||
print(Path.cwd())
|
||||
```
|
||||
|
||||
## Load existing objects into a Jupyter notebook
|
||||
|
||||
These examples assume that you have already generated data using the cli. They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload.
|
||||
|
||||
### Load backtest results into a pandas dataframe
|
||||
|
||||
```python
|
||||
from freqtrade.data.btanalysis import load_backtest_data
|
||||
|
||||
# Load backtest results
|
||||
df = load_backtest_data("user_data/backtest_results/backtest-result.json")
|
||||
|
||||
# Show value-counts per pair
|
||||
df.groupby("pair")["sell_reason"].value_counts()
|
||||
```
|
||||
|
||||
### Load live trading results into a pandas dataframe
|
||||
|
||||
``` python
|
||||
from freqtrade.data.btanalysis import load_trades_from_db
|
||||
|
||||
# Fetch trades from database
|
||||
df = load_trades_from_db("sqlite:///tradesv3.sqlite")
|
||||
|
||||
# Display results
|
||||
df.groupby("pair")["sell_reason"].value_counts()
|
||||
```
|
||||
|
||||
### Load multiple configuration files
|
||||
|
||||
This option can be useful to inspect the results of passing in multiple configs.
|
||||
@@ -114,99 +86,9 @@ Best avoid relative paths, since this starts at the storage location of the jupy
|
||||
}
|
||||
```
|
||||
|
||||
### Load exchange data to a pandas dataframe
|
||||
### Further Data analysis documentation
|
||||
|
||||
This loads candle data to a dataframe
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from freqtrade.data.history import load_pair_history
|
||||
|
||||
# Load data using values passed to function
|
||||
ticker_interval = "5m"
|
||||
data_location = Path('user_data', 'data', 'bitrex')
|
||||
pair = "BTC_USDT"
|
||||
candles = load_pair_history(datadir=data_location,
|
||||
ticker_interval=ticker_interval,
|
||||
pair=pair)
|
||||
|
||||
# Confirm success
|
||||
print(f"Loaded len(candles) rows of data for {pair} from {data_location}")
|
||||
candles.head()
|
||||
```
|
||||
|
||||
## Strategy debugging example
|
||||
|
||||
Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
|
||||
|
||||
### Define variables used in analyses
|
||||
|
||||
You can override strategy settings as demonstrated below.
|
||||
|
||||
```python
|
||||
# Customize these according to your needs.
|
||||
|
||||
# Define some constants
|
||||
ticker_interval = "5m"
|
||||
# Name of the strategy class
|
||||
strategy_name = 'SampleStrategy'
|
||||
# Path to user data
|
||||
user_data_dir = 'user_data'
|
||||
# Location of the strategy
|
||||
strategy_location = Path(user_data_dir, 'strategies')
|
||||
# Location of the data
|
||||
data_location = Path(user_data_dir, 'data', 'binance')
|
||||
# Pair to analyze - Only use one pair here
|
||||
pair = "BTC_USDT"
|
||||
```
|
||||
|
||||
### Load exchange data
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from freqtrade.data.history import load_pair_history
|
||||
|
||||
# Load data using values set above
|
||||
candles = load_pair_history(datadir=data_location,
|
||||
ticker_interval=ticker_interval,
|
||||
pair=pair)
|
||||
|
||||
# Confirm success
|
||||
print(f"Loaded {len(candles)} rows of data for {pair} from {data_location}")
|
||||
candles.head()
|
||||
```
|
||||
|
||||
### Load and run strategy
|
||||
|
||||
* Rerun each time the strategy file is changed
|
||||
|
||||
```python
|
||||
from freqtrade.resolvers import StrategyResolver
|
||||
|
||||
# Load strategy using values set above
|
||||
strategy = StrategyResolver({'strategy': strategy_name,
|
||||
'user_data_dir': user_data_dir,
|
||||
'strategy_path': strategy_location}).strategy
|
||||
|
||||
# Generate buy/sell signals using strategy
|
||||
df = strategy.analyze_ticker(candles, {'pair': pair})
|
||||
```
|
||||
|
||||
### Display the trade details
|
||||
|
||||
* Note that using `data.tail()` is preferable to `data.head()` as most indicators have some "startup" data at the top of the dataframe.
|
||||
* Some possible problems
|
||||
* Columns with NaN values at the end of the dataframe
|
||||
* Columns used in `crossed*()` functions with completely different units
|
||||
* Comparison with full backtest
|
||||
* having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.
|
||||
* Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available.
|
||||
|
||||
```python
|
||||
# Report results
|
||||
print(f"Generated {df['buy'].sum()} buy signals")
|
||||
data = df.set_index('date', drop=True)
|
||||
data.tail()
|
||||
```
|
||||
* [Strategy debugging](strategy_analysis_example.md) - also available as Jupyter notebook (`user_data/notebooks/strategy_analysis_example.ipynb`)
|
||||
* [Plotting](plotting.md)
|
||||
|
||||
Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
|
||||
|
||||
@@ -38,7 +38,7 @@ Mixing different stake-currencies is allowed for this file, since it's only used
|
||||
]
|
||||
```
|
||||
|
||||
### start download
|
||||
### Start download
|
||||
|
||||
Then run:
|
||||
|
||||
@@ -57,6 +57,32 @@ This will download ticker data for all the currency pairs you defined in `pairs.
|
||||
- Use `--timeframes` to specify which tickers to download. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute tickers.
|
||||
- To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
|
||||
|
||||
### Trades (tick) data
|
||||
|
||||
By default, `download-data` subcommand downloads Candles (OHLCV) data. Some exchanges also provide historic trade-data via their API.
|
||||
This data can be useful if you need many different timeframes, since it is only downloaded once, and then resampled locally to the desired timeframes.
|
||||
|
||||
Since this data is large by default, the files use gzip by default. They are stored in your data-directory with the naming convention of `<pair>-trades.json.gz` (`ETH_BTC-trades.json.gz`). Incremental mode is also supported, as for historic OHLCV data, so downloading the data once per week with `--days 8` will create an incremental data-repository.
|
||||
|
||||
To use this mode, simply add `--dl-trades` to your call. This will swap the download method to download trades, and resamples the data locally.
|
||||
|
||||
Example call:
|
||||
|
||||
```bash
|
||||
freqtrade download-data --exchange binance --pairs XRP/ETH ETH/BTC --days 20 --dl-trades
|
||||
```
|
||||
|
||||
!!! Note
|
||||
While this method uses async calls, it will be slow, since it requires the result of the previous call to generate the next request to the exchange.
|
||||
|
||||
!!! Warning
|
||||
The historic trades are not available during Freqtrade dry-run and live trade modes because all exchanges tested provide this data with a delay of few 100 candles, so it's not suitable for real-time trading.
|
||||
|
||||
### Historic Kraken data
|
||||
|
||||
The Kraken API does only provide 720 historic candles, which is sufficient for FreqTrade dry-run and live trade modes, but is a problem for backtesting.
|
||||
To download data for the Kraken exchange, using `--dl-trades` is mandatory, otherwise the bot will download the same 720 candles over and over, and you'll not have enough backtest data.
|
||||
|
||||
## Next step
|
||||
|
||||
Great, you now have backtest data downloaded, so you can now start [backtesting](backtesting.md) your strategy.
|
||||
|
||||
@@ -38,8 +38,48 @@ def test_method_to_test(caplog):
|
||||
assert log_has("This event happened", caplog)
|
||||
# Check regex with trailing number ...
|
||||
assert log_has_re(r"This dynamic event happened and produced \d+", caplog)
|
||||
|
||||
```
|
||||
|
||||
### Local docker usage
|
||||
|
||||
The fastest and easiest way to start up is to use docker-compose.develop which gives developers the ability to start the bot up with all the required dependencies, *without* needing to install any freqtrade specific dependencies on your local machine.
|
||||
|
||||
#### Install
|
||||
* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
|
||||
* [docker](https://docs.docker.com/install/)
|
||||
* [docker-compose](https://docs.docker.com/compose/install/)
|
||||
|
||||
#### Starting the bot
|
||||
##### Use the develop dockerfile
|
||||
``` bash
|
||||
rm docker-compose.yml && mv docker-compose.develop.yml docker-compose.yml
|
||||
```
|
||||
#### Docker Compose
|
||||
|
||||
##### Starting
|
||||
|
||||
``` bash
|
||||
docker-compose up
|
||||
```
|
||||
|
||||
|
||||
##### Rebuilding
|
||||
``` bash
|
||||
docker-compose build
|
||||
```
|
||||
|
||||
##### Execing (effectively SSH into the container)
|
||||
|
||||
The `exec` command requires that the container already be running, if you want to start it
|
||||
that can be effected by `docker-compose up` or `docker-compose run freqtrade_develop`
|
||||
|
||||
``` bash
|
||||
docker-compose exec freqtrade_develop /bin/bash
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Modules
|
||||
|
||||
### Dynamic Pairlist
|
||||
@@ -149,6 +189,15 @@ print(datetime.utcnow())
|
||||
The output will show the last entry from the Exchange as well as the current UTC date.
|
||||
If the day shows the same day, then the last candle can be assumed as incomplete and should be dropped (leave the setting `"ohlcv_partial_candle"` from the exchange-class untouched / True). Otherwise, set `"ohlcv_partial_candle"` to `False` to not drop Candles (shown in the example above).
|
||||
|
||||
## Updating example notebooks
|
||||
|
||||
To keep the jupyter notebooks aligned with the documentation, the following should be ran after updating a example notebook.
|
||||
|
||||
``` bash
|
||||
jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace user_data/notebooks/strategy_analysis_example.ipynb
|
||||
jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown user_data/notebooks/strategy_analysis_example.ipynb --stdout > docs/strategy_analysis_example.md
|
||||
```
|
||||
|
||||
## Creating a release
|
||||
|
||||
This part of the documentation is aimed at maintainers, and shows how to create a release.
|
||||
@@ -182,7 +231,7 @@ git log --oneline --no-decorate --no-merges master..develop
|
||||
Once the PR against master is merged (best right after merging):
|
||||
|
||||
* Use the button "Draft a new release" in the Github UI (subsection releases)
|
||||
* Use the version-number specified as tag.
|
||||
* Use the version-number specified as tag.
|
||||
* Use "master" as reference (this step comes after the above PR is merged).
|
||||
* Use the above changelog as release comment (as codeblock)
|
||||
|
||||
|
||||
@@ -249,13 +249,10 @@ freqtrade edge --stoplosses=-0.01,-0.1,-0.001 #min,max,step
|
||||
freqtrade edge --timerange=20181110-20181113
|
||||
```
|
||||
|
||||
Doing `--timerange=-200` will get the last 200 timeframes from your inputdata. You can also specify specific dates, or a range span indexed by start and stop.
|
||||
Doing `--timerange=-20190901` will get all available data until September 1st (excluding September 1st 2019).
|
||||
|
||||
The full timerange specification:
|
||||
|
||||
* Use last 123 tickframes of data: `--timerange=-123`
|
||||
* Use first 123 tickframes of data: `--timerange=123-`
|
||||
* Use tickframes from line 123 through 456: `--timerange=123-456`
|
||||
* Use tickframes till 2018/01/31: `--timerange=-20180131`
|
||||
* Use tickframes since 2018/01/31: `--timerange=20180131-`
|
||||
* Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
|
||||
|
||||
@@ -38,7 +38,7 @@ like pauses. You can stop your bot, adjust settings and start it again.
|
||||
|
||||
### I want to improve the bot with a new strategy
|
||||
|
||||
That's great. We have a nice backtesting and hyperoptimizing setup. See
|
||||
That's great. We have a nice backtesting and hyperoptimization setup. See
|
||||
the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-commands).
|
||||
|
||||
### Is there a setting to only SELL the coins being held and not perform anymore BUYS?
|
||||
@@ -59,7 +59,7 @@ If you're a US customer, the bot will fail to create orders for these pairs, and
|
||||
|
||||
### How many epoch do I need to get a good Hyperopt result?
|
||||
|
||||
Per default Hyperopts without `-e` or `--epochs` parameter will only
|
||||
Per default Hyperopt called without the `-e`/`--epochs` command line option will only
|
||||
run 100 epochs, means 100 evals of your triggers, guards, ... Too few
|
||||
to find a great result (unless if you are very lucky), so you probably
|
||||
have to run it for 10.000 or more. But it will take an eternity to
|
||||
|
||||
@@ -10,12 +10,12 @@ Hyperopt requires historic data to be available, just as backtesting does.
|
||||
To learn how to get data for the pairs and exchange you're interrested in, head over to the [Data Downloading](data-download.md) section of the documentation.
|
||||
|
||||
!!! Bug
|
||||
Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
|
||||
Hyperopt can crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
|
||||
|
||||
## Prepare Hyperopting
|
||||
|
||||
Before we start digging into Hyperopt, we recommend you to take a look at
|
||||
an example hyperopt file located into [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt.py)
|
||||
the sample hyperopt file located in [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt.py).
|
||||
|
||||
Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar and a lot of code can be copied across from the strategy.
|
||||
|
||||
@@ -64,9 +64,9 @@ multiple guards. The constructed strategy will be something like
|
||||
"*buy exactly when close price touches lower bollinger band, BUT only if
|
||||
ADX > 10*".
|
||||
|
||||
If you have updated the buy strategy, ie. changed the contents of
|
||||
`populate_buy_trend()` method you have to update the `guards` and
|
||||
`triggers` hyperopts must use.
|
||||
If you have updated the buy strategy, i.e. changed the contents of
|
||||
`populate_buy_trend()` method, you have to update the `guards` and
|
||||
`triggers` your hyperopt must use correspondingly.
|
||||
|
||||
#### Sell optimization
|
||||
|
||||
@@ -82,7 +82,7 @@ To avoid naming collisions in the search-space, please prefix all sell-spaces wi
|
||||
#### Using ticker-interval as part of the Strategy
|
||||
|
||||
The Strategy exposes the ticker-interval as `self.ticker_interval`. The same value is available as class-attribute `HyperoptName.ticker_interval`.
|
||||
In the case of the linked sample-value this would be `SampleHyperOpts.ticker_interval`.
|
||||
In the case of the linked sample-value this would be `SampleHyperOpt.ticker_interval`.
|
||||
|
||||
## Solving a Mystery
|
||||
|
||||
|
||||
@@ -49,4 +49,6 @@
|
||||
</nav>
|
||||
<!-- Place this tag in your head or just before your close body tag. -->
|
||||
<script async defer src="https://buttons.github.io/buttons.js"></script>
|
||||
</header>
|
||||
<script src="https://code.jquery.com/jquery-3.4.1.min.js"
|
||||
integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
|
||||
</header>
|
||||
|
||||
@@ -220,5 +220,5 @@ freqtrade plot-profit -p LTC/BTC --db-url sqlite:///tradesv3.sqlite --trade-sou
|
||||
```
|
||||
|
||||
``` bash
|
||||
freqtrade plot-profit --datadir user_data/data/binance_save/ -p LTC/BTC
|
||||
freqtrade --datadir user_data/data/binance_save/ plot-profit -p LTC/BTC
|
||||
```
|
||||
|
||||
@@ -1 +1,2 @@
|
||||
mkdocs-material==4.4.2
|
||||
mkdocs-material==4.4.3
|
||||
mdx_truly_sane_lists==1.2
|
||||
|
||||
@@ -100,7 +100,6 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
|
||||
| `stopbuy` | | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
|
||||
| `reload_conf` | | Reloads the configuration file
|
||||
| `status` | | Lists all open trades
|
||||
| `status table` | | List all open trades in a table format
|
||||
| `count` | | Displays number of trades used and available
|
||||
| `profit` | | Display a summary of your profit/loss from close trades and some stats about your performance
|
||||
| `forcesell <trade_id>` | | Instantly sells the given trade (Ignoring `minimum_roi`).
|
||||
|
||||
@@ -138,15 +138,19 @@ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
(dataframe['adx'] > 30) &
|
||||
(dataframe['tema'] <= dataframe['bb_middleband']) &
|
||||
(dataframe['tema'] > dataframe['tema'].shift(1))
|
||||
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
|
||||
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard
|
||||
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
'buy'] = 1
|
||||
|
||||
return dataframe
|
||||
```
|
||||
|
||||
!!! Note
|
||||
Buying requires sellers to buy from - therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods.
|
||||
|
||||
### Sell signal rules
|
||||
|
||||
Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
|
||||
@@ -168,9 +172,10 @@ def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
(dataframe['adx'] > 70) &
|
||||
(dataframe['tema'] > dataframe['bb_middleband']) &
|
||||
(dataframe['tema'] < dataframe['tema'].shift(1))
|
||||
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
|
||||
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard
|
||||
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard
|
||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||||
),
|
||||
'sell'] = 1
|
||||
return dataframe
|
||||
@@ -246,9 +251,9 @@ Instead, have a look at the section [Storing information](#Storing-information)
|
||||
|
||||
### Storing information
|
||||
|
||||
Storing information can be accomplished by crating a new dictionary within the strategy class.
|
||||
Storing information can be accomplished by creating a new dictionary within the strategy class.
|
||||
|
||||
The name of the variable can be choosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
|
||||
The name of the variable can be chosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
|
||||
|
||||
```python
|
||||
class Awesomestrategy(IStrategy):
|
||||
@@ -282,6 +287,8 @@ Please always check the mode of operation to select the correct method to get da
|
||||
- `ohlcv(pair, ticker_interval)` - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.
|
||||
- `historic_ohlcv(pair, ticker_interval)` - Returns historical data stored on disk.
|
||||
- `get_pair_dataframe(pair, ticker_interval)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
|
||||
- `orderbook(pair, maximum)` - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
|
||||
- `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on Market data structure.
|
||||
- `runmode` - Property containing the current runmode.
|
||||
|
||||
#### Example: fetch live ohlcv / historic data for the first informative pair
|
||||
@@ -344,9 +351,9 @@ def informative_pairs(self):
|
||||
As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short.
|
||||
All intervals and all pairs can be specified as long as they are available (and active) on the used exchange.
|
||||
It is however better to use resampling to longer time-intervals when possible
|
||||
to avoid hammering the exchange with too many requests and risk beeing blocked.
|
||||
to avoid hammering the exchange with too many requests and risk being blocked.
|
||||
|
||||
### Additional data - Wallets
|
||||
### Additional data (Wallets)
|
||||
|
||||
The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
|
||||
|
||||
|
||||
142
docs/strategy_analysis_example.md
Normal file
142
docs/strategy_analysis_example.md
Normal file
@@ -0,0 +1,142 @@
|
||||
# Strategy analysis example
|
||||
|
||||
Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
|
||||
|
||||
## Setup
|
||||
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
# Customize these according to your needs.
|
||||
|
||||
# Define some constants
|
||||
ticker_interval = "5m"
|
||||
# Name of the strategy class
|
||||
strategy_name = 'SampleStrategy'
|
||||
# Path to user data
|
||||
user_data_dir = Path('user_data')
|
||||
# Location of the strategy
|
||||
strategy_location = user_data_dir / 'strategies'
|
||||
# Location of the data
|
||||
data_location = Path(user_data_dir, 'data', 'binance')
|
||||
# Pair to analyze - Only use one pair here
|
||||
pair = "BTC_USDT"
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
# Load data using values set above
|
||||
from freqtrade.data.history import load_pair_history
|
||||
|
||||
candles = load_pair_history(datadir=data_location,
|
||||
ticker_interval=ticker_interval,
|
||||
pair=pair)
|
||||
|
||||
# Confirm success
|
||||
print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}")
|
||||
candles.head()
|
||||
```
|
||||
|
||||
## Load and run strategy
|
||||
* Rerun each time the strategy file is changed
|
||||
|
||||
|
||||
```python
|
||||
# Load strategy using values set above
|
||||
from freqtrade.resolvers import StrategyResolver
|
||||
strategy = StrategyResolver({'strategy': strategy_name,
|
||||
'user_data_dir': user_data_dir,
|
||||
'strategy_path': strategy_location}).strategy
|
||||
|
||||
# Generate buy/sell signals using strategy
|
||||
df = strategy.analyze_ticker(candles, {'pair': pair})
|
||||
df.tail()
|
||||
```
|
||||
|
||||
### Display the trade details
|
||||
|
||||
* Note that using `data.head()` would also work, however most indicators have some "startup" data at the top of the dataframe.
|
||||
* Some possible problems
|
||||
* Columns with NaN values at the end of the dataframe
|
||||
* Columns used in `crossed*()` functions with completely different units
|
||||
* Comparison with full backtest
|
||||
* having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.
|
||||
* Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available.
|
||||
|
||||
|
||||
|
||||
```python
|
||||
# Report results
|
||||
print(f"Generated {df['buy'].sum()} buy signals")
|
||||
data = df.set_index('date', drop=False)
|
||||
data.tail()
|
||||
```
|
||||
|
||||
## Load existing objects into a Jupyter notebook
|
||||
|
||||
The following cells assume that you have already generated data using the cli.
|
||||
They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload.
|
||||
|
||||
### Load backtest results to pandas dataframe
|
||||
|
||||
Analyze a trades dataframe (also used below for plotting)
|
||||
|
||||
|
||||
```python
|
||||
from freqtrade.data.btanalysis import load_backtest_data
|
||||
|
||||
# Load backtest results
|
||||
trades = load_backtest_data(user_data_dir / "backtest_results/backtest-result.json")
|
||||
|
||||
# Show value-counts per pair
|
||||
trades.groupby("pair")["sell_reason"].value_counts()
|
||||
```
|
||||
|
||||
### Load live trading results into a pandas dataframe
|
||||
|
||||
In case you did already some trading and want to analyze your performance
|
||||
|
||||
|
||||
```python
|
||||
from freqtrade.data.btanalysis import load_trades_from_db
|
||||
|
||||
# Fetch trades from database
|
||||
trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
|
||||
|
||||
# Display results
|
||||
trades.groupby("pair")["sell_reason"].value_counts()
|
||||
```
|
||||
|
||||
## Plot results
|
||||
|
||||
Freqtrade offers interactive plotting capabilities based on plotly.
|
||||
|
||||
|
||||
```python
|
||||
from freqtrade.plot.plotting import generate_candlestick_graph
|
||||
# Limit graph period to keep plotly quick and reactive
|
||||
|
||||
data_red = data['2019-06-01':'2019-06-10']
|
||||
# Generate candlestick graph
|
||||
graph = generate_candlestick_graph(pair=pair,
|
||||
data=data_red,
|
||||
trades=trades,
|
||||
indicators1=['sma20', 'ema50', 'ema55'],
|
||||
indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
|
||||
)
|
||||
|
||||
|
||||
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
# Show graph inline
|
||||
# graph.show()
|
||||
|
||||
# Render graph in a seperate window
|
||||
graph.show(renderer="browser")
|
||||
|
||||
```
|
||||
|
||||
Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
|
||||
13
docs/stylesheets/ft.extra.css
Normal file
13
docs/stylesheets/ft.extra.css
Normal file
@@ -0,0 +1,13 @@
|
||||
.rst-versions {
|
||||
font-size: .7rem;
|
||||
color: white;
|
||||
}
|
||||
|
||||
.rst-versions.rst-badge .rst-current-version {
|
||||
font-size: .7rem;
|
||||
color: white;
|
||||
}
|
||||
|
||||
.rst-versions .rst-other-versions {
|
||||
color: white;
|
||||
}
|
||||
56
docs/utils.md
Normal file
56
docs/utils.md
Normal file
@@ -0,0 +1,56 @@
|
||||
# Utility Subcommands
|
||||
|
||||
Besides the Live-Trade and Dry-Run run modes, the `backtesting`, `edge` and `hyperopt` optimization subcommands, and the `download-data` subcommand which prepares historical data, the bot contains a number of utility subcommands. They are described in this section.
|
||||
|
||||
## List Exchanges
|
||||
|
||||
Use the `list-exchanges` subcommand to see the exchanges available for the bot.
|
||||
|
||||
```
|
||||
usage: freqtrade list-exchanges [-h] [-1] [-a]
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
-1, --one-column Print output in one column.
|
||||
-a, --all Print all exchanges known to the ccxt library.
|
||||
```
|
||||
|
||||
* Example: see exchanges available for the bot:
|
||||
```
|
||||
$ freqtrade list-exchanges
|
||||
Exchanges available for Freqtrade: _1btcxe, acx, allcoin, bequant, bibox, binance, binanceje, binanceus, bitbank, bitfinex, bitfinex2, bitkk, bitlish, bitmart, bittrex, bitz, bleutrade, btcalpha, btcmarkets, btcturk, buda, cex, cobinhood, coinbaseprime, coinbasepro, coinex, cointiger, coss, crex24, digifinex, dsx, dx, ethfinex, fcoin, fcoinjp, gateio, gdax, gemini, hitbtc2, huobipro, huobiru, idex, kkex, kraken, kucoin, kucoin2, kuna, lbank, mandala, mercado, oceanex, okcoincny, okcoinusd, okex, okex3, poloniex, rightbtc, theocean, tidebit, upbit, zb
|
||||
```
|
||||
|
||||
* Example: see all exchanges supported by the ccxt library (including 'bad' ones, i.e. those that are known to not work with Freqtrade):
|
||||
```
|
||||
$ freqtrade list-exchanges -a
|
||||
All exchanges supported by the ccxt library: _1btcxe, acx, adara, allcoin, anxpro, bcex, bequant, bibox, bigone, binance, binanceje, binanceus, bit2c, bitbank, bitbay, bitfinex, bitfinex2, bitflyer, bitforex, bithumb, bitkk, bitlish, bitmart, bitmex, bitso, bitstamp, bitstamp1, bittrex, bitz, bl3p, bleutrade, braziliex, btcalpha, btcbox, btcchina, btcmarkets, btctradeim, btctradeua, btcturk, buda, bxinth, cex, chilebit, cobinhood, coinbase, coinbaseprime, coinbasepro, coincheck, coinegg, coinex, coinexchange, coinfalcon, coinfloor, coingi, coinmarketcap, coinmate, coinone, coinspot, cointiger, coolcoin, coss, crex24, crypton, deribit, digifinex, dsx, dx, ethfinex, exmo, exx, fcoin, fcoinjp, flowbtc, foxbit, fybse, gateio, gdax, gemini, hitbtc, hitbtc2, huobipro, huobiru, ice3x, idex, independentreserve, indodax, itbit, kkex, kraken, kucoin, kucoin2, kuna, lakebtc, latoken, lbank, liquid, livecoin, luno, lykke, mandala, mercado, mixcoins, negociecoins, nova, oceanex, okcoincny, okcoinusd, okex, okex3, paymium, poloniex, rightbtc, southxchange, stronghold, surbitcoin, theocean, therock, tidebit, tidex, upbit, vaultoro, vbtc, virwox, xbtce, yobit, zaif, zb
|
||||
```
|
||||
|
||||
## List Timeframes
|
||||
|
||||
Use the `list-timeframes` subcommand to see the list of ticker intervals (timeframes) available for the exchange.
|
||||
|
||||
```
|
||||
usage: freqtrade list-timeframes [-h] [--exchange EXCHANGE] [-1]
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
|
||||
config is provided.
|
||||
-1, --one-column Print output in one column.
|
||||
|
||||
```
|
||||
|
||||
* Example: see the timeframes for the 'binance' exchange, set in the configuration file:
|
||||
|
||||
```
|
||||
$ freqtrade -c config_binance.json list-timeframes
|
||||
...
|
||||
Timeframes available for the exchange `binance`: 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d, 3d, 1w, 1M
|
||||
```
|
||||
|
||||
* Example: enumerate exchanges available for Freqtrade and print timeframes supported by each of them:
|
||||
```
|
||||
$ for i in `freqtrade list-exchanges -1`; do freqtrade list-timeframes --exchange $i; done
|
||||
```
|
||||
Reference in New Issue
Block a user