Merge branch 'develop' into pr/Antreasgr/4838
This commit is contained in:
Matthias
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@@ -237,29 +237,29 @@ The most important in the backtesting is to understand the result.
|
||||
A backtesting result will look like that:
|
||||
|
||||
```
|
||||
========================================================= BACKTESTING REPORT ========================================================
|
||||
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |
|
||||
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
|
||||
| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 | 0 | 21 |
|
||||
| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 | 0 | 8 |
|
||||
| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 | 0 | 14 |
|
||||
| DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 | 0 | 7 |
|
||||
| ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 | 0 | 10 |
|
||||
| EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 | 0 | 20 |
|
||||
| ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 | 0 | 15 |
|
||||
| ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 | 0 | 17 |
|
||||
| IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 | 0 | 18 |
|
||||
| LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 | 0 | 9 |
|
||||
| LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 | 0 | 21 |
|
||||
| NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 | 0 | 7 |
|
||||
| NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 | 0 | 13 |
|
||||
| REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 | 0 | 5 |
|
||||
| XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 | 0 | 9 |
|
||||
| XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 | 0 | 11 |
|
||||
| XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 | 0 | 23 |
|
||||
| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 | 0 | 15 |
|
||||
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 |
|
||||
========================================================= SELL REASON STATS =========================================================
|
||||
========================================================= BACKTESTING REPORT ==========================================================
|
||||
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins Draws Loss Win% |
|
||||
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:-------------|-------------------------:|
|
||||
| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 0 21 40.0 |
|
||||
| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 0 8 27.3 |
|
||||
| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 0 14 56.2 |
|
||||
| DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 0 7 46.2 |
|
||||
| ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 0 10 44.4 |
|
||||
| EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 0 20 44.4 |
|
||||
| ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 0 15 42.3 |
|
||||
| ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 0 17 48.5 |
|
||||
| IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 0 18 43.8 |
|
||||
| LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 0 9 40.0 |
|
||||
| LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 0 21 34.4 |
|
||||
| NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 0 7 58.5 |
|
||||
| NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 0 13 43.5 |
|
||||
| REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 0 5 44.4 |
|
||||
| XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 0 9 43.8 |
|
||||
| XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 0 11 52.2 |
|
||||
| XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 0 23 34.3 |
|
||||
| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 0 15 31.8 |
|
||||
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 0 243 43.4 |
|
||||
========================================================= SELL REASON STATS ==========================================================
|
||||
| Sell Reason | Sells | Wins | Draws | Losses |
|
||||
|:-------------------|--------:|------:|-------:|--------:|
|
||||
| trailing_stop_loss | 205 | 150 | 0 | 55 |
|
||||
@@ -267,11 +267,11 @@ A backtesting result will look like that:
|
||||
| sell_signal | 56 | 36 | 0 | 20 |
|
||||
| force_sell | 2 | 0 | 0 | 2 |
|
||||
====================================================== LEFT OPEN TRADES REPORT ======================================================
|
||||
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |
|
||||
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
|
||||
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 | 0 | 0 |
|
||||
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 | 0 | 0 |
|
||||
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 | 0 | 0 |
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||||
| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Win Draw Loss Win% |
|
||||
|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
|
||||
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 0 0 100 |
|
||||
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 0 0 100 |
|
||||
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 0 0 100 |
|
||||
=============== SUMMARY METRICS ===============
|
||||
| Metric | Value |
|
||||
|-----------------------+---------------------|
|
||||
@@ -297,6 +297,8 @@ A backtesting result will look like that:
|
||||
| Days win/draw/lose | 12 / 82 / 25 |
|
||||
| Avg. Duration Winners | 4:23:00 |
|
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| Avg. Duration Loser | 6:55:00 |
|
||||
| Zero Duration Trades | 4.6% (20) |
|
||||
| Rejected Buy signals | 3089 |
|
||||
| | |
|
||||
| Min balance | 0.00945123 BTC |
|
||||
| Max balance | 0.01846651 BTC |
|
||||
@@ -318,7 +320,7 @@ The last line will give you the overall performance of your strategy,
|
||||
here:
|
||||
|
||||
```
|
||||
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 |
|
||||
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 0 243 43.4 |
|
||||
```
|
||||
|
||||
The bot has made `429` trades for an average duration of `4:12:00`, with a performance of `76.20%` (profit), that means it has
|
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@@ -384,6 +386,8 @@ It contains some useful key metrics about performance of your strategy on backte
|
||||
| Days win/draw/lose | 12 / 82 / 25 |
|
||||
| Avg. Duration Winners | 4:23:00 |
|
||||
| Avg. Duration Loser | 6:55:00 |
|
||||
| Zero Duration Trades | 4.6% (20) |
|
||||
| Rejected Buy signals | 3089 |
|
||||
| | |
|
||||
| Min balance | 0.00945123 BTC |
|
||||
| Max balance | 0.01846651 BTC |
|
||||
@@ -413,6 +417,8 @@ It contains some useful key metrics about performance of your strategy on backte
|
||||
- `Best day` / `Worst day`: Best and worst day based on daily profit.
|
||||
- `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
|
||||
- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
|
||||
- `Zero Duration Trades`: A number of trades that completed within same candle as they opened and had `trailing_stop_loss` sell reason. A significant amount of such trades may indicate that strategy is exploiting trailing stoploss behavior in backtesting and produces unrealistic results.
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- `Rejected Buy signals`: Buy signals that could not be acted upon due to max_open_trades being reached.
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- `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
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- `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
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- `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
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||||
@@ -472,11 +478,11 @@ There will be an additional table comparing win/losses of the different strategi
|
||||
Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
|
||||
|
||||
```
|
||||
=========================================================== STRATEGY SUMMARY ===========================================================
|
||||
| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |
|
||||
|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|
|
||||
| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 |
|
||||
| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 825 |
|
||||
=========================================================== STRATEGY SUMMARY =========================================================================
|
||||
| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses | Drawdown % |
|
||||
|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|-----------:|
|
||||
| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 0 | 243 | 45.2 |
|
||||
| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 0 | 825 | 241.68 |
|
||||
```
|
||||
|
||||
## Next step
|
||||
|
||||
@@ -68,8 +68,9 @@ Mandatory parameters are marked as **Required**, which means that they are requi
|
||||
| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
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||||
| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
|
||||
| `fee` | Fee used during backtesting / dry-runs. Should normally not be configured, which has freqtrade fall back to the exchange default fee. Set as ratio (e.g. 0.001 = 0.1%). Fee is applied twice for each trade, once when buying, once when selling. <br> **Datatype:** Float (as ratio)
|
||||
| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
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||||
| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
|
||||
| `unfilledtimeout.buy` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
|
||||
| `unfilledtimeout.sell` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
|
||||
| `unfilledtimeout.unit` | Unit to use in unfilledtimeout setting. Note: If you set unfilledtimeout.unit to "seconds", "internals.process_throttle_secs" must be inferior or equal to timeout [Strategy Override](#parameters-in-the-strategy). <br> *Defaults to `minutes`.* <br> **Datatype:** String
|
||||
| `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
|
||||
| `bid_strategy.ask_last_balance` | **Required.** Interpolate the bidding price. More information [below](#buy-price-without-orderbook-enabled).
|
||||
| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
|
||||
|
||||
@@ -48,6 +48,8 @@ Create a new directory and place the [docker-compose file](https://raw.githubuse
|
||||
# Download the docker-compose file from the repository
|
||||
curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml
|
||||
|
||||
# Edit the compose file to use an image named `*_pi` (stable_pi or develop_pi)
|
||||
|
||||
# Pull the freqtrade image
|
||||
docker-compose pull
|
||||
|
||||
@@ -65,6 +67,40 @@ Create a new directory and place the [docker-compose file](https://raw.githubuse
|
||||
# image: freqtradeorg/freqtrade:develop_pi
|
||||
```
|
||||
|
||||
=== "ARM 64 Systenms (Mac M1, Raspberry Pi 4, Jetson Nano)"
|
||||
In case of a Mac M1, make sure that your docker installation is running in native mode
|
||||
Arm64 images are not yet provided via Docker Hub and need to be build locally first.
|
||||
Depending on the device, this may take a few minutes (Apple M1) or multiple hours (Raspberry Pi)
|
||||
|
||||
``` bash
|
||||
# Clone Freqtrade repository
|
||||
git clone https://github.com/freqtrade/freqtrade.git
|
||||
cd freqtrade
|
||||
# Optionally switch to the stable version
|
||||
git checkout stable
|
||||
|
||||
# Modify your docker-compose file to enable building and change the image name
|
||||
# (see the Note Box below for necessary changes)
|
||||
|
||||
# Build image
|
||||
docker-compose build
|
||||
|
||||
# Create user directory structure
|
||||
docker-compose run --rm freqtrade create-userdir --userdir user_data
|
||||
|
||||
# Create configuration - Requires answering interactive questions
|
||||
docker-compose run --rm freqtrade new-config --config user_data/config.json
|
||||
```
|
||||
|
||||
!!! Note "Change your docker Image"
|
||||
You have to change the docker image in the docker-compose file for your arm64 build to work properly.
|
||||
``` yml
|
||||
image: freqtradeorg/freqtrade:custom_arm64
|
||||
build:
|
||||
context: .
|
||||
dockerfile: "./docker/Dockerfile.aarch64"
|
||||
```
|
||||
|
||||
The above snippet creates a new directory called `ft_userdata`, downloads the latest compose file and pulls the freqtrade image.
|
||||
The last 2 steps in the snippet create the directory with `user_data`, as well as (interactively) the default configuration based on your selections.
|
||||
|
||||
|
||||
@@ -249,19 +249,24 @@ We continue to define hyperoptable parameters:
|
||||
|
||||
```python
|
||||
class MyAwesomeStrategy(IStrategy):
|
||||
buy_adx = IntParameter(20, 40, default=30)
|
||||
buy_rsi = IntParameter(20, 40, default=30)
|
||||
buy_adx_enabled = CategoricalParameter([True, False]),
|
||||
buy_rsi_enabled = CategoricalParameter([True, False]),
|
||||
buy_trigger = CategoricalParameter(['bb_lower', 'macd_cross_signal']),
|
||||
buy_adx = DecimalParameter(20, 40, decimals=1, default=30.1, space="buy")
|
||||
buy_rsi = IntParameter(20, 40, default=30, space="buy")
|
||||
buy_adx_enabled = CategoricalParameter([True, False], default=True, space="buy")
|
||||
buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy")
|
||||
buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy")
|
||||
```
|
||||
|
||||
Above definition says: I have five parameters I want to randomly combine to find the best combination.
|
||||
Two of them are integer values (`buy_adx` and `buy_rsi`) and I want you test in the range of values 20 to 40.
|
||||
The above definition says: I have five parameters I want to randomly combine to find the best combination.
|
||||
`buy_rsi` is an integer parameter, which will be tested between 20 and 40. This space has a size of 20.
|
||||
`buy_adx` is a decimal parameter, which will be evaluated between 20 and 40 with 1 decimal place (so values are 20.1, 20.2, ...). This space has a size of 200.
|
||||
Then we have three category variables. First two are either `True` or `False`.
|
||||
We use these to either enable or disable the ADX and RSI guards.
|
||||
The last one we call `trigger` and use it to decide which buy trigger we want to use.
|
||||
|
||||
!!! Note "Parameter space assignment"
|
||||
Parameters must either be assigned to a variable named `buy_*` or `sell_*` - or contain `space='buy'` | `space='sell'` to be assigned to a space correctly.
|
||||
If no parameter is available for a space, you'll receive the error that no space was found when running hyperopt.
|
||||
|
||||
So let's write the buy strategy using these values:
|
||||
|
||||
```python
|
||||
|
||||
@@ -112,6 +112,7 @@ The `PriceFilter` allows filtering of pairs by price. Currently the following pr
|
||||
|
||||
* `min_price`
|
||||
* `max_price`
|
||||
* `max_value`
|
||||
* `low_price_ratio`
|
||||
|
||||
The `min_price` setting removes pairs where the price is below the specified price. This is useful if you wish to avoid trading very low-priced pairs.
|
||||
@@ -120,6 +121,11 @@ This option is disabled by default, and will only apply if set to > 0.
|
||||
The `max_price` setting removes pairs where the price is above the specified price. This is useful if you wish to trade only low-priced pairs.
|
||||
This option is disabled by default, and will only apply if set to > 0.
|
||||
|
||||
The `max_value` setting removes pairs where the minimum value change is above a specified value.
|
||||
This is useful when an exchange has unbalanced limits. For example, if step-size = 1 (so you can only buy 1, or 2, or 3, but not 1.1 Coins) - and the price is pretty high (like 20$) as the coin has risen sharply since the last limit adaption.
|
||||
As a result of the above, you can only buy for 20$, or 40$ - but not for 25$.
|
||||
On exchanges that deduct fees from the receiving currency (e.g. FTX) - this can result in high value coins / amounts that are unsellable as the amount is slightly below the limit.
|
||||
|
||||
The `low_price_ratio` setting removes pairs where a raise of 1 price unit (pip) is above the `low_price_ratio` ratio.
|
||||
This option is disabled by default, and will only apply if set to > 0.
|
||||
|
||||
@@ -193,7 +199,7 @@ If the volatility over the last 10 days is not in the range of 0.05-0.50, remove
|
||||
|
||||
### Full example of Pairlist Handlers
|
||||
|
||||
The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#price-filter), filtering all assets where 1 price unit is > 1%. Then the [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) is applied and pairs are finally shuffled with the random seed set to some predefined value.
|
||||
The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#pricefilter), filtering all assets where 1 price unit is > 1%. Then the [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) is applied and pairs are finally shuffled with the random seed set to some predefined value.
|
||||
|
||||
```json
|
||||
"exchange": {
|
||||
@@ -204,7 +210,7 @@ The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets,
|
||||
{
|
||||
"method": "VolumePairList",
|
||||
"number_assets": 20,
|
||||
"sort_key": "quoteVolume",
|
||||
"sort_key": "quoteVolume"
|
||||
},
|
||||
{"method": "AgeFilter", "min_days_listed": 10},
|
||||
{"method": "PrecisionFilter"},
|
||||
|
||||
@@ -60,7 +60,7 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
|
||||
sudo apt-get update
|
||||
|
||||
# install packages
|
||||
sudo apt install -y python3-pip python3-venv python3-pandas python3-pip git
|
||||
sudo apt install -y python3-pip python3-venv python3-pandas git
|
||||
```
|
||||
|
||||
=== "RaspberryPi/Raspbian"
|
||||
@@ -269,7 +269,7 @@ git clone https://github.com/freqtrade/freqtrade.git
|
||||
cd freqtrade
|
||||
```
|
||||
|
||||
#### Freqtrade instal: Conda Environment
|
||||
#### Freqtrade install: Conda Environment
|
||||
|
||||
Prepare conda-freqtrade environment, using file `environment.yml`, which exist in main freqtrade directory
|
||||
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
mkdocs-material==7.1.3
|
||||
mkdocs-material==7.1.5
|
||||
mdx_truly_sane_lists==1.2
|
||||
pymdown-extensions==8.1.1
|
||||
pymdown-extensions==8.2
|
||||
|
||||
@@ -71,7 +71,10 @@ If you run your bot using docker, you'll need to have the bot listen to incoming
|
||||
"api_server": {
|
||||
"enabled": true,
|
||||
"listen_ip_address": "0.0.0.0",
|
||||
"listen_port": 8080
|
||||
"listen_port": 8080,
|
||||
"username": "Freqtrader",
|
||||
"password": "SuperSecret1!",
|
||||
//...
|
||||
},
|
||||
```
|
||||
|
||||
@@ -106,7 +109,10 @@ By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be use
|
||||
"api_server": {
|
||||
"enabled": true,
|
||||
"listen_ip_address": "0.0.0.0",
|
||||
"listen_port": 8080
|
||||
"listen_port": 8080,
|
||||
"username": "Freqtrader",
|
||||
"password": "SuperSecret1!",
|
||||
//...
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
@@ -19,7 +19,7 @@ The freqtrade docker image does contain sqlite3, so you can edit the database wi
|
||||
|
||||
``` bash
|
||||
docker-compose exec freqtrade /bin/bash
|
||||
sqlite3 <databasefile>.sqlite
|
||||
sqlite3 <database-file>.sqlite
|
||||
```
|
||||
|
||||
## Open the DB
|
||||
@@ -99,3 +99,32 @@ DELETE FROM trades WHERE id = 31;
|
||||
|
||||
!!! Warning
|
||||
This will remove this trade from the database. Please make sure you got the correct id and **NEVER** run this query without the `where` clause.
|
||||
|
||||
## Use a different database system
|
||||
|
||||
!!! Warning
|
||||
By using one of the below database systems, you acknowledge that you know how to manage such a system. Freqtrade will not provide any support with setup or maintenance (or backups) of the below database systems.
|
||||
|
||||
### PostgreSQL
|
||||
|
||||
Freqtrade supports PostgreSQL by using SQLAlchemy, which supports multiple different database systems.
|
||||
|
||||
Installation:
|
||||
`pip install psycopg2`
|
||||
|
||||
Usage:
|
||||
`... --db-url postgresql+psycopg2://<username>:<password>@localhost:5432/<database>`
|
||||
|
||||
Freqtrade will automatically create the tables necessary upon startup.
|
||||
|
||||
If you're running different instances of Freqtrade, you must either setup one database per Instance or use different users / schemas for your connections.
|
||||
|
||||
### MariaDB / MySQL
|
||||
|
||||
Freqtrade supports MariaDB by using SQLAlchemy, which supports multiple different database systems.
|
||||
|
||||
Installation:
|
||||
`pip install pymysql`
|
||||
|
||||
Usage:
|
||||
`... --db-url mysql+pymysql://<username>:<password>@localhost:3306/<database>`
|
||||
|
||||
@@ -40,31 +40,71 @@ class AwesomeStrategy(IStrategy):
|
||||
!!! Note
|
||||
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
|
||||
|
||||
## Dataframe access
|
||||
|
||||
You may access dataframe in various strategy functions by querying it from dataprovider.
|
||||
|
||||
``` python
|
||||
from freqtrade.exchange import timeframe_to_prev_date
|
||||
|
||||
class AwesomeStrategy(IStrategy):
|
||||
def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
|
||||
rate: float, time_in_force: str, sell_reason: str,
|
||||
current_time: 'datetime', **kwargs) -> bool:
|
||||
# Obtain pair dataframe.
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
|
||||
# Obtain last available candle. Do not use current_time to look up latest candle, because
|
||||
# current_time points to curret incomplete candle whose data is not available.
|
||||
last_candle = dataframe.iloc[-1].squeeze()
|
||||
# <...>
|
||||
|
||||
# In dry/live runs trade open date will not match candle open date therefore it must be
|
||||
# rounded.
|
||||
trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
|
||||
# Look up trade candle.
|
||||
trade_candle = dataframe.loc[dataframe['date'] == trade_date]
|
||||
# trade_candle may be empty for trades that just opened as it is still incomplete.
|
||||
if not trade_candle.empty:
|
||||
trade_candle = trade_candle.squeeze()
|
||||
# <...>
|
||||
```
|
||||
|
||||
!!! Warning "Using .iloc[-1]"
|
||||
You can use `.iloc[-1]` here because `get_analyzed_dataframe()` only returns candles that backtesting is allowed to see.
|
||||
This will not work in `populate_*` methods, so make sure to not use `.iloc[]` in that area.
|
||||
Also, this will only work starting with version 2021.5.
|
||||
|
||||
***
|
||||
|
||||
## Custom sell signal
|
||||
|
||||
It is possible to define custom sell signals. This is very useful when we need to customize sell conditions for each individual trade, or if you need the trade profit to take the sell decision.
|
||||
It is possible to define custom sell signals, indicating that specified position should be sold. This is very useful when we need to customize sell conditions for each individual trade, or if you need the trade profit to take the sell decision.
|
||||
|
||||
For example you could implement a 1:2 risk-reward ROI with `custom_sell()`.
|
||||
|
||||
Using custom_sell() signals in place of stoplosses though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
|
||||
|
||||
!!! Note
|
||||
Returning a `string` or `True` from this method is equal to setting sell signal on a candle at specified time. This method is not called when sell signal is set already, or if sell signals are disabled (`use_sell_signal=False` or `sell_profit_only=True` while profit is below `sell_profit_offset`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
|
||||
|
||||
An example of how we can use different indicators depending on the current profit and also sell trades that were open longer than one day:
|
||||
|
||||
``` python
|
||||
from freqtrade.strategy import IStrategy, timeframe_to_prev_date
|
||||
|
||||
class AwesomeStrategy(IStrategy):
|
||||
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
|
||||
current_profit: float, dataframe: DataFrame, **kwargs):
|
||||
trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
|
||||
trade_row = dataframe.loc[dataframe['date'] == trade_open_date].squeeze()
|
||||
current_profit: float, **kwargs):
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = dataframe.iloc[-1].squeeze()
|
||||
|
||||
# Above 20% profit, sell when rsi < 80
|
||||
if current_profit > 0.2:
|
||||
if trade_row['rsi'] < 80:
|
||||
if last_candle['rsi'] < 80:
|
||||
return 'rsi_below_80'
|
||||
|
||||
# Between 2% and 10%, sell if EMA-long above EMA-short
|
||||
if 0.02 < current_profit < 0.1:
|
||||
if trade_row['emalong'] > trade_row['emashort']:
|
||||
if last_candle['emalong'] > last_candle['emashort']:
|
||||
return 'ema_long_below_80'
|
||||
|
||||
# Sell any positions at a loss if they are held for more than one day.
|
||||
@@ -72,7 +112,7 @@ class AwesomeStrategy(IStrategy):
|
||||
return 'unclog'
|
||||
```
|
||||
|
||||
See [Custom stoploss using an indicator from dataframe example](#custom-stoploss-using-an-indicator-from-dataframe-example) for explanation on how to use `dataframe` parameter.
|
||||
See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
|
||||
|
||||
## Custom stoploss
|
||||
|
||||
@@ -98,8 +138,7 @@ class AwesomeStrategy(IStrategy):
|
||||
use_custom_stoploss = True
|
||||
|
||||
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
|
||||
current_rate: float, current_profit: float, dataframe: DataFrame,
|
||||
**kwargs) -> float:
|
||||
current_rate: float, current_profit: float, **kwargs) -> float:
|
||||
"""
|
||||
Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
|
||||
e.g. returning -0.05 would create a stoploss 5% below current_rate.
|
||||
@@ -149,8 +188,7 @@ class AwesomeStrategy(IStrategy):
|
||||
use_custom_stoploss = True
|
||||
|
||||
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
|
||||
current_rate: float, current_profit: float, dataframe: DataFrame,
|
||||
**kwargs) -> float:
|
||||
current_rate: float, current_profit: float, **kwargs) -> float:
|
||||
|
||||
# Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
|
||||
if current_time - timedelta(minutes=120) > trade.open_date_utc:
|
||||
@@ -176,8 +214,7 @@ class AwesomeStrategy(IStrategy):
|
||||
use_custom_stoploss = True
|
||||
|
||||
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
|
||||
current_rate: float, current_profit: float, dataframe: DataFrame,
|
||||
**kwargs) -> float:
|
||||
current_rate: float, current_profit: float, **kwargs) -> float:
|
||||
|
||||
if pair in ('ETH/BTC', 'XRP/BTC'):
|
||||
return -0.10
|
||||
@@ -203,8 +240,7 @@ class AwesomeStrategy(IStrategy):
|
||||
use_custom_stoploss = True
|
||||
|
||||
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
|
||||
current_rate: float, current_profit: float, dataframe: DataFrame,
|
||||
**kwargs) -> float:
|
||||
current_rate: float, current_profit: float, **kwargs) -> float:
|
||||
|
||||
if current_profit < 0.04:
|
||||
return -1 # return a value bigger than the inital stoploss to keep using the inital stoploss
|
||||
@@ -243,8 +279,7 @@ class AwesomeStrategy(IStrategy):
|
||||
use_custom_stoploss = True
|
||||
|
||||
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
|
||||
current_rate: float, current_profit: float, dataframe: DataFrame,
|
||||
**kwargs) -> float:
|
||||
current_rate: float, current_profit: float, **kwargs) -> float:
|
||||
|
||||
# evaluate highest to lowest, so that highest possible stop is used
|
||||
if current_profit > 0.40:
|
||||
@@ -260,59 +295,43 @@ class AwesomeStrategy(IStrategy):
|
||||
|
||||
#### Custom stoploss using an indicator from dataframe example
|
||||
|
||||
Imagine you want to use `custom_stoploss()` to use a trailing indicator like e.g. "ATR"
|
||||
|
||||
!!! Warning
|
||||
Only use `dataframe` values up until and including `current_time` value. Reading past
|
||||
`current_time` you will look into the future, which will produce incorrect backtesting results
|
||||
and throw an exception in dry/live runs.
|
||||
see [Common mistakes when developing strategies](strategy-customization.md#common-mistakes-when-developing-strategies) for more info.
|
||||
|
||||
!!! Note
|
||||
`dataframe['date']` contains the candle's open date. During dry/live runs `current_time` and
|
||||
`trade.open_date_utc` will not match the candle date precisely and using them directly will throw
|
||||
an error. Use `date = timeframe_to_prev_date(self.timeframe, date)` to round a date to the candle's open date
|
||||
before using it to access `dataframe`.
|
||||
Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
|
||||
|
||||
``` python
|
||||
from freqtrade.exchange import timeframe_to_prev_date
|
||||
from freqtrade.persistence import Trade
|
||||
from freqtrade.state import RunMode
|
||||
|
||||
class AwesomeStrategy(IStrategy):
|
||||
|
||||
# ... populate_* methods
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# <...>
|
||||
dataframe['sar'] = ta.SAR(dataframe)
|
||||
|
||||
use_custom_stoploss = True
|
||||
|
||||
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
|
||||
current_rate: float, current_profit: float, dataframe: DataFrame,
|
||||
**kwargs) -> float:
|
||||
current_rate: float, current_profit: float, **kwargs) -> float:
|
||||
|
||||
# Default return value
|
||||
result = 1
|
||||
if trade:
|
||||
# Using current_time directly would only work in backtesting. Live/dry runs need time to
|
||||
# be rounded to previous candle to be used as dataframe index. Rounding must also be
|
||||
# applied to `trade.open_date(_utc)` if it is used for `dataframe` indexing.
|
||||
current_time = timeframe_to_prev_date(self.timeframe, current_time)
|
||||
current_row = dataframe.loc[dataframe['date'] == current_time].squeeze()
|
||||
if 'atr' in current_row:
|
||||
# new stoploss relative to current_rate
|
||||
new_stoploss = (current_rate - current_row['atr']) / current_rate
|
||||
# turn into relative negative offset required by `custom_stoploss` return implementation
|
||||
result = new_stoploss - 1
|
||||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||||
last_candle = dataframe.iloc[-1].squeeze()
|
||||
|
||||
return result
|
||||
# Use parabolic sar as absolute stoploss price
|
||||
stoploss_price = last_candle['sar']
|
||||
|
||||
# Convert absolute price to percentage relative to current_rate
|
||||
if stoploss_price < current_rate:
|
||||
return (stoploss_price / current_rate) - 1
|
||||
|
||||
# return maximum stoploss value, keeping current stoploss price unchanged
|
||||
return 1
|
||||
```
|
||||
|
||||
See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
|
||||
|
||||
---
|
||||
|
||||
## Custom order timeout rules
|
||||
|
||||
Simple, time-based order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
|
||||
|
||||
However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if a order did time out or not.
|
||||
However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if an order did time out or not.
|
||||
|
||||
!!! Note
|
||||
Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
|
||||
@@ -538,7 +557,7 @@ Both attributes and methods may be overridden, altering behavior of the original
|
||||
|
||||
## Embedding Strategies
|
||||
|
||||
Freqtrade provides you with with an easy way to embed the strategy into your configuration file.
|
||||
Freqtrade provides you with an easy way to embed the strategy into your configuration file.
|
||||
This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
|
||||
in your chosen config file.
|
||||
|
||||
|
||||
@@ -159,7 +159,7 @@ Edit the method `populate_buy_trend()` in your strategy file to update your buy
|
||||
|
||||
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
|
||||
|
||||
This will method will also define a new column, `"buy"`, which needs to contain 1 for buys, and 0 for "no action".
|
||||
This method will also define a new column, `"buy"`, which needs to contain 1 for buys, and 0 for "no action".
|
||||
|
||||
Sample from `user_data/strategies/sample_strategy.py`:
|
||||
|
||||
@@ -193,7 +193,7 @@ Please note that the sell-signal is only used if `use_sell_signal` is set to tru
|
||||
|
||||
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
|
||||
|
||||
This will method will also define a new column, `"sell"`, which needs to contain 1 for sells, and 0 for "no action".
|
||||
This method will also define a new column, `"sell"`, which needs to contain 1 for sells, and 0 for "no action".
|
||||
|
||||
Sample from `user_data/strategies/sample_strategy.py`:
|
||||
|
||||
@@ -422,10 +422,6 @@ if self.dp:
|
||||
Returns an empty dataframe if the requested pair was not cached.
|
||||
This should not happen when using whitelisted pairs.
|
||||
|
||||
|
||||
!!! Warning "Warning about backtesting"
|
||||
This method will return an empty dataframe during backtesting.
|
||||
|
||||
### *orderbook(pair, maximum)*
|
||||
|
||||
``` python
|
||||
@@ -631,8 +627,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
|
||||
use_custom_stoploss = True
|
||||
|
||||
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
|
||||
current_rate: float, current_profit: float, dataframe: DataFrame,
|
||||
**kwargs) -> float:
|
||||
current_rate: float, current_profit: float, **kwargs) -> float:
|
||||
|
||||
# once the profit has risen above 10%, keep the stoploss at 7% above the open price
|
||||
if current_profit > 0.10:
|
||||
|
||||
@@ -72,22 +72,31 @@ Example configuration showing the different settings:
|
||||
|
||||
``` json
|
||||
"telegram": {
|
||||
"enabled": true,
|
||||
"token": "your_telegram_token",
|
||||
"chat_id": "your_telegram_chat_id",
|
||||
"notification_settings": {
|
||||
"status": "silent",
|
||||
"warning": "on",
|
||||
"startup": "off",
|
||||
"buy": "silent",
|
||||
"sell": "on",
|
||||
"buy_cancel": "silent",
|
||||
"sell_cancel": "on",
|
||||
"buy_fill": "off",
|
||||
"sell_fill": "off"
|
||||
},
|
||||
"balance_dust_level": 0.01
|
||||
},
|
||||
"enabled": true,
|
||||
"token": "your_telegram_token",
|
||||
"chat_id": "your_telegram_chat_id",
|
||||
"notification_settings": {
|
||||
"status": "silent",
|
||||
"warning": "on",
|
||||
"startup": "off",
|
||||
"buy": "silent",
|
||||
"sell": {
|
||||
"roi": "silent",
|
||||
"emergency_sell": "on",
|
||||
"force_sell": "on",
|
||||
"sell_signal": "silent",
|
||||
"trailing_stop_loss": "on",
|
||||
"stop_loss": "on",
|
||||
"stoploss_on_exchange": "on",
|
||||
"custom_sell": "silent"
|
||||
},
|
||||
"buy_cancel": "silent",
|
||||
"sell_cancel": "on",
|
||||
"buy_fill": "off",
|
||||
"sell_fill": "off"
|
||||
},
|
||||
"balance_dust_level": 0.01
|
||||
},
|
||||
```
|
||||
|
||||
`buy` notifications are sent when the order is placed, while `buy_fill` notifications are sent when the order is filled on the exchange.
|
||||
@@ -250,10 +259,14 @@ Return a summary of your profit/loss and performance.
|
||||
|
||||
> **BITTREX:** Selling BTC/LTC with limit `0.01650000 (profit: ~-4.07%, -0.00008168)`
|
||||
|
||||
### /forcebuy <pair>
|
||||
### /forcebuy <pair> [rate]
|
||||
|
||||
> **BITTREX:** Buying ETH/BTC with limit `0.03400000` (`1.000000 ETH`, `225.290 USD`)
|
||||
|
||||
Omitting the pair will open a query asking for the pair to buy (based on the current whitelist).
|
||||
|
||||
|
||||
|
||||
Note that for this to work, `forcebuy_enable` needs to be set to true.
|
||||
|
||||
[More details](configuration.md#understand-forcebuy_enable)
|
||||
@@ -261,12 +274,12 @@ Note that for this to work, `forcebuy_enable` needs to be set to true.
|
||||
### /performance
|
||||
|
||||
Return the performance of each crypto-currency the bot has sold.
|
||||
> Performance:
|
||||
> 1. `RCN/BTC 57.77%`
|
||||
> 2. `PAY/BTC 56.91%`
|
||||
> 3. `VIB/BTC 47.07%`
|
||||
> 4. `SALT/BTC 30.24%`
|
||||
> 5. `STORJ/BTC 27.24%`
|
||||
> Performance:
|
||||
> 1. `RCN/BTC 0.003 BTC (57.77%) (1)`
|
||||
> 2. `PAY/BTC 0.0012 BTC (56.91%) (1)`
|
||||
> 3. `VIB/BTC 0.0011 BTC (47.07%) (1)`
|
||||
> 4. `SALT/BTC 0.0010 BTC (30.24%) (1)`
|
||||
> 5. `STORJ/BTC 0.0009 BTC (27.24%) (1)`
|
||||
> ...
|
||||
|
||||
### /balance
|
||||
|
||||
@@ -1,3 +1,5 @@
|
||||
# Windows installation
|
||||
|
||||
We **strongly** recommend that Windows users use [Docker](docker_quickstart.md) as this will work much easier and smoother (also more secure).
|
||||
|
||||
If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
|
||||
@@ -21,7 +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 precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.19‑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 needs to be downloaded and installed using `pip install TA_Lib‑0.4.20‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version).
|
||||
|
||||
Freqtrade provides these dependencies for the latest 2 Python versions (3.7 and 3.8) and for 64bit Windows.
|
||||
Other versions must be downloaded from the above link.
|
||||
|
||||
Reference in New Issue
Block a user