Merge branch 'develop' into interface_ordertimeoutcallback
This commit is contained in:
Matthias
@@ -11,8 +11,8 @@ Now you have good Buy and Sell strategies and some historic data, you want to te
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real data. This is what we call
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[backtesting](https://en.wikipedia.org/wiki/Backtesting).
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Backtesting will use the crypto-currencies (pairs) from your config file and load ticker data from `user_data/data/<exchange>` by default.
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If no data is available for the exchange / pair / ticker interval combination, backtesting will ask you to download them first using `freqtrade download-data`.
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Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
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If no data is available for the exchange / pair / timeframe (ticker interval) combination, backtesting will ask you to download them first using `freqtrade download-data`.
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For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
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The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
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@@ -22,19 +22,19 @@ The result of backtesting will confirm if your bot has better odds of making a p
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### Run a backtesting against the currencies listed in your config file
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#### With 5 min tickers (Per default)
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#### With 5 min candle (OHLCV) data (per default)
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```bash
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freqtrade backtesting
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```
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#### With 1 min tickers
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#### With 1 min candle (OHLCV) data
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```bash
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freqtrade backtesting --ticker-interval 1m
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```
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#### Using a different on-disk ticker-data source
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#### Using a different on-disk historical candle (OHLCV) data source
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Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory.
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You can then use this data for backtesting as follows:
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@@ -223,7 +223,7 @@ You can then load the trades to perform further analysis as shown in our [data a
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To compare multiple strategies, a list of Strategies can be provided to backtesting.
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This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple
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This is limited to 1 timeframe (ticker interval) value per run. However, data is only loaded once from disk so if you have multiple
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strategies you'd like to compare, this will give a nice runtime boost.
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All listed Strategies need to be in the same directory.
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@@ -275,7 +275,7 @@ Check the corresponding [Data Downloading](data-download.md) section for more de
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## Hyperopt commands
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To optimize your strategy, you can use hyperopt parameter hyperoptimization
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to find optimal parameter values for your stategy.
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to find optimal parameter values for your strategy.
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```
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usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
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@@ -323,7 +323,7 @@ optional arguments:
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--print-all Print all results, not only the best ones.
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--no-color Disable colorization of hyperopt results. May be
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useful if you are redirecting output to a file.
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--print-json Print best result detailization in JSON format.
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--print-json Print best results in JSON format.
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-j JOBS, --job-workers JOBS
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The number of concurrently running jobs for
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hyperoptimization (hyperopt worker processes). If -1
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@@ -341,10 +341,11 @@ optional arguments:
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class (IHyperOptLoss). Different functions can
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generate completely different results, since the
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target for optimization is different. Built-in
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Hyperopt-loss-functions are: DefaultHyperOptLoss,
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OnlyProfitHyperOptLoss, SharpeHyperOptLoss,
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SharpeHyperOptLossDaily.(default:
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`DefaultHyperOptLoss`).
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Hyperopt-loss-functions are:
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DefaultHyperOptLoss, OnlyProfitHyperOptLoss,
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SharpeHyperOptLoss, SharpeHyperOptLossDaily,
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SortinoHyperOptLoss, SortinoHyperOptLossDaily.
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(default: `DefaultHyperOptLoss`).
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Common arguments:
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-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
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@@ -47,7 +47,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
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| `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
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| `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.5`.* <br> **Datatype:** Float (as ratio)
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| `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals. <br>*Defaults to `0.05` (5%).* <br> **Datatype:** Positive Float as ratio.
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| `ticker_interval` | The ticker interval to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
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| `ticker_interval` | The timeframe (ticker interval) to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
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| `fiat_display_currency` | Fiat currency used to show your profits. [More information below](#what-values-can-be-used-for-fiat_display_currency). <br> **Datatype:** String
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| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
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| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in the Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
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@@ -60,11 +60,13 @@ Mandatory parameters are marked as **Required**, which means that they are requi
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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
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| `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. [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. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
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| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook).
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| `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`).
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| `bid_strategy.ask_last_balance` | **Required.** Set the bidding price. More information [below](#buy-price-without-orderbook-enabled).
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| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
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| `bid_strategy.order_book_top` | Bot will use the top N rate in Order Book Bids to buy. I.e. a value of 2 will allow the bot to pick the 2nd bid rate in [Order Book Bids](#buy-price-with-orderbook-enabled). <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
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| `bid_strategy. check_depth_of_market.enabled` | Do not buy if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
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| `bid_strategy. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market) <br> *Defaults to `0`.* <br> **Datatype:** Float (as ratio)
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| `ask_strategy.price_side` | Select the side of the spread the bot should look at to get the sell rate. [More information below](#sell-price-side).<br> *Defaults to `ask`.* <br> **Datatype:** String (either `ask` or `bid`).
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| `ask_strategy.use_order_book` | Enable selling of open trades using [Order Book Asks](#sell-price-with-orderbook-enabled). <br> **Datatype:** Boolean
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| `ask_strategy.order_book_min` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
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| `ask_strategy.order_book_max` | Bot will scan from the top min to max Order Book Asks searching for a profitable rate. <br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
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@@ -111,8 +113,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
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| `internals.sd_notify` | Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See [here](installation.md#7-optional-configure-freqtrade-as-a-systemd-service) for more details. <br> **Datatype:** Boolean
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| `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
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| `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
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| `dataformat_ohlcv` | Data format to use to store OHLCV historic data. <br> *Defaults to `json`*. <br> **Datatype:** String
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| `dataformat_trades` | Data format to use to store trades historic data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
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| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
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| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
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### Parameters in the strategy
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@@ -340,7 +342,7 @@ This is most of the time the default time in force. It means the order will rema
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on exchange till it is canceled by user. It can be fully or partially fulfilled.
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If partially fulfilled, the remaining will stay on the exchange till cancelled.
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**FOK (Full Or Kill):**
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**FOK (Fill Or Kill):**
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It means if the order is not executed immediately AND fully then it is canceled by the exchange.
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@@ -370,16 +372,18 @@ The possible values are: `gtc` (default), `fok` or `ioc`.
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Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports over 100 cryptocurrency
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exchange markets and trading APIs. The complete up-to-date list can be found in the
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[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python). However, the bot was tested
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with only Bittrex and Binance.
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The bot was tested with the following exchanges:
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[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
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However, the bot was tested by the development team with only Bittrex, Binance and Kraken,
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so the these are the only officially supported exhanges:
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- [Bittrex](https://bittrex.com/): "bittrex"
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- [Binance](https://www.binance.com/): "binance"
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- [Kraken](https://kraken.com/): "kraken"
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Feel free to test other exchanges and submit your PR to improve the bot.
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Some exchanges require special configuration, which can be found on the [Exchange-specific Notes](exchanges.md) documentation page.
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#### Sample exchange configuration
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A exchange configuration for "binance" would look as follows:
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@@ -409,7 +413,7 @@ Advanced options can be configured using the `_ft_has_params` setting, which wil
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Available options are listed in the exchange-class as `_ft_has_default`.
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For example, to test the order type `FOK` with Kraken, and modify candle_limit to 200 (so you only get 200 candles per call):
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For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
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||||
|
||||
```json
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"exchange": {
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@@ -461,34 +465,89 @@ Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) s
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!!! Note
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A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
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#### Buy price side
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The configuration setting `bid_strategy.price_side` defines the side of the spread the bot looks for when buying.
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The following displays an orderbook.
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``` explanation
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...
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103
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102
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101 # ask
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-------------Current spread
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99 # bid
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98
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97
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...
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```
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If `bid_strategy.price_side` is set to `"bid"`, then the bot will use 99 as buying price.
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In line with that, if `bid_strategy.price_side` is set to `"ask"`, then the bot will use 101 as buying price.
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Using `ask` price often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
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Taker fees instead of maker fees will most likely apply even when using limit buy orders.
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Also, prices at the "ask" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
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#### Buy price with Orderbook enabled
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||||
|
||||
When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the `bid` (buy) side of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
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When buying with the orderbook enabled (`bid_strategy.use_order_book=True`), Freqtrade fetches the `bid_strategy.order_book_top` entries from the orderbook and then uses the entry specified as `bid_strategy.order_book_top` on the configured side (`bid_strategy.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
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||||
|
||||
#### Buy price without Orderbook enabled
|
||||
|
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When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `ask` (sell) price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `ask` price is not below the `last` price), it calculates a rate between `ask` and `last` price.
|
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The following section uses `side` as the configured `bid_strategy.price_side`.
|
||||
|
||||
The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `ask` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
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When not using orderbook (`bid_strategy.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price.
|
||||
|
||||
Using `ask` price often guarantees quicker success in the bid, but the bot can also end up paying more than what would have been necessary.
|
||||
The `bid_strategy.ask_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
|
||||
|
||||
### Sell price
|
||||
|
||||
#### Sell price side
|
||||
|
||||
The configuration setting `ask_strategy.price_side` defines the side of the spread the bot looks for when selling.
|
||||
|
||||
The following displays an orderbook:
|
||||
|
||||
``` explanation
|
||||
...
|
||||
103
|
||||
102
|
||||
101 # ask
|
||||
-------------Current spread
|
||||
99 # bid
|
||||
98
|
||||
97
|
||||
...
|
||||
```
|
||||
|
||||
If `ask_strategy.price_side` is set to `"ask"`, then the bot will use 101 as selling price.
|
||||
In line with that, if `ask_strategy.price_side` is set to `"bid"`, then the bot will use 99 as selling price.
|
||||
|
||||
#### Sell price with Orderbook enabled
|
||||
|
||||
When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the `ask` orderbook side are validated for a profitable sell-possibility based on the strategy configuration and the sell order is placed at the first profitable spot.
|
||||
When selling with the orderbook enabled (`ask_strategy.use_order_book=True`), Freqtrade fetches the `ask_strategy.order_book_max` entries in the orderbook. Then each of the orderbook steps between `ask_strategy.order_book_min` and `ask_strategy.order_book_max` on the configured orderbook side are validated for a profitable sell-possibility based on the strategy configuration (`minimal_roi` conditions) and the sell order is placed at the first profitable spot.
|
||||
|
||||
!!! Note
|
||||
Using `order_book_max` higher than `order_book_min` only makes sense when ask_strategy.price_side is set to `"ask"`.
|
||||
|
||||
The idea here is to place the sell order early, to be ahead in the queue.
|
||||
|
||||
A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting `ask_strategy.order_book_min` and `ask_strategy.order_book_max` to the same number.
|
||||
|
||||
!!! Warning "Orderbook and stoploss_on_exchange"
|
||||
Using `ask_strategy.order_book_max` higher than 1 may increase the risk, since an eventual [stoploss on exchange](#understand-order_types) will be needed to be cancelled as soon as the order is placed.
|
||||
!!! Warning "Order_book_max > 1 - increased risks for stoplosses!"
|
||||
Using `ask_strategy.order_book_max` higher than 1 will increase the risk the stoploss on exchange is cancelled too early, since an eventual [stoploss on exchange](#understand-order_types) will be cancelled as soon as the order is placed.
|
||||
Also, the sell order will remain on the exchange for `unfilledtimeout.sell` (or until it's filled) - which can lead to missed stoplosses (with or without using stoploss on exchange).
|
||||
|
||||
!!! Warning "Order_book_max > 1 in dry-run"
|
||||
Using `ask_strategy.order_book_max` higher than 1 will result in improper dry-run results (significantly better than real orders executed on exchange), since dry-run assumes orders to be filled almost instantly.
|
||||
It is therefore advised to not use this setting for dry-runs.
|
||||
|
||||
|
||||
#### Sell price without Orderbook enabled
|
||||
|
||||
When not using orderbook (`ask_strategy.use_order_book=False`), the `bid` price from the ticker will be used as the sell price.
|
||||
When not using orderbook (`ask_strategy.use_order_book=False`), the price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
|
||||
|
||||
## Pairlists
|
||||
|
||||
@@ -532,6 +591,12 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
|
||||
|
||||
`refresh_period` allows setting the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
|
||||
|
||||
`VolumePairList` is based on the ticker data, as reported by the ccxt library:
|
||||
|
||||
* The `bidVolume` is the volume (amount) of current best bid in the orderbook.
|
||||
* The `askVolume` is the volume (amount) of current best ask in the orderbook.
|
||||
* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
|
||||
|
||||
```json
|
||||
"pairlists": [{
|
||||
"method": "VolumePairList",
|
||||
@@ -626,6 +691,11 @@ In production mode, the bot will engage your money. Be careful, since a wrong
|
||||
strategy can lose all your money. Be aware of what you are doing when
|
||||
you run it in production mode.
|
||||
|
||||
### Setup your exchange account
|
||||
|
||||
You will need to create API Keys (usually you get `key` and `secret`, some exchanges require an additional `password`) from the Exchange website and you'll need to insert this into the appropriate fields in the configuration or when asked by the `freqtrade new-config` command.
|
||||
API Keys are usually only required for live trading (trading for real money, bot running in "production mode", executing real orders on the exchange) and are not required for the bot running in dry-run (trade simulation) mode. When you setup the bot in dry-run mode, you may fill these fields with empty values.
|
||||
|
||||
### To switch your bot in production mode
|
||||
|
||||
**Edit your `config.json` file.**
|
||||
@@ -647,9 +717,6 @@ you run it in production mode.
|
||||
}
|
||||
```
|
||||
|
||||
!!! Note
|
||||
If you have an exchange API key yet, [see our tutorial](installation.md#setup-your-exchange-account).
|
||||
|
||||
You should also make sure to read the [Exchanges](exchanges.md) section of the documentation to be aware of potential configuration details specific to your exchange.
|
||||
|
||||
### Using proxy with Freqtrade
|
||||
|
||||
@@ -33,7 +33,7 @@ optional arguments:
|
||||
Specify which tickers to download. Space-separated list. Default: `1m 5m`.
|
||||
--erase Clean all existing data for the selected exchange/pairs/timeframes.
|
||||
--data-format-ohlcv {json,jsongz}
|
||||
Storage format for downloaded ohlcv data. (default: `json`).
|
||||
Storage format for downloaded candle (OHLCV) data. (default: `json`).
|
||||
--data-format-trades {json,jsongz}
|
||||
Storage format for downloaded trades data. (default: `jsongz`).
|
||||
|
||||
@@ -105,7 +105,7 @@ Common arguments:
|
||||
|
||||
##### Example converting data
|
||||
|
||||
The following command will convert all ohlcv (candle) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
|
||||
The following command will convert all candle (OHLCV) data available in `~/.freqtrade/data/binance` from json to jsongz, saving diskspace in the process.
|
||||
It'll also remove original json data files (`--erase` parameter).
|
||||
|
||||
``` bash
|
||||
@@ -192,15 +192,15 @@ Then run:
|
||||
freqtrade download-data --exchange binance
|
||||
```
|
||||
|
||||
This will download ticker data for all the currency pairs you defined in `pairs.json`.
|
||||
This will download historical candle (OHLCV) data for all the currency pairs you defined in `pairs.json`.
|
||||
|
||||
### Other Notes
|
||||
|
||||
- To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
|
||||
- To change the exchange used to download the tickers, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
|
||||
- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
|
||||
- To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
|
||||
- To download ticker data for only 10 days, use `--days 10` (defaults to 30 days).
|
||||
- Use `--timeframes` to specify which tickers to download. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute tickers.
|
||||
- To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
|
||||
- Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
|
||||
- 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
|
||||
|
||||
@@ -165,7 +165,7 @@ Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need
|
||||
|
||||
### Incomplete candles
|
||||
|
||||
While fetching OHLCV data, we're may end up getting incomplete candles (Depending on the exchange).
|
||||
While fetching candle (OHLCV) data, we may end up getting incomplete candles (depending on the exchange).
|
||||
To demonstrate this, we'll use daily candles (`"1d"`) to keep things simple.
|
||||
We query the api (`ct.fetch_ohlcv()`) for the timeframe and look at the date of the last entry. If this entry changes or shows the date of a "incomplete" candle, then we should drop this since having incomplete candles is problematic because indicators assume that only complete candles are passed to them, and will generate a lot of false buy signals. By default, we're therefore removing the last candle assuming it's incomplete.
|
||||
|
||||
@@ -174,14 +174,14 @@ To check how the new exchange behaves, you can use the following snippet:
|
||||
``` python
|
||||
import ccxt
|
||||
from datetime import datetime
|
||||
from freqtrade.data.converter import parse_ticker_dataframe
|
||||
from freqtrade.data.converter import ohlcv_to_dataframe
|
||||
ct = ccxt.binance()
|
||||
timeframe = "1d"
|
||||
pair = "XLM/BTC" # Make sure to use a pair that exists on that exchange!
|
||||
raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
|
||||
|
||||
# convert to dataframe
|
||||
df1 = parse_ticker_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
|
||||
df1 = ohlcv_to_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
|
||||
|
||||
print(df1.tail(1))
|
||||
print(datetime.utcnow())
|
||||
@@ -234,7 +234,7 @@ git checkout -b new_release <commitid>
|
||||
|
||||
Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
|
||||
|
||||
* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7-1` should we need to do a second release that month.
|
||||
* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
|
||||
* Commit this part
|
||||
* push that branch to the remote and create a PR against the master branch
|
||||
|
||||
@@ -268,11 +268,6 @@ Once the PR against master is merged (best right after merging):
|
||||
* Use "master" as reference (this step comes after the above PR is merged).
|
||||
* Use the above changelog as release comment (as codeblock)
|
||||
|
||||
### After-release
|
||||
|
||||
* Update version in develop by postfixing that with `-dev` (`2019.6 -> 2019.6-dev`).
|
||||
* Create a PR against develop to update that branch.
|
||||
|
||||
## Releases
|
||||
|
||||
### pypi
|
||||
|
||||
@@ -156,7 +156,7 @@ Edge module has following configuration options:
|
||||
| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br> **Datatype:** Float
|
||||
| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br> **Datatype:** Float
|
||||
| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br> **Datatype:** Integer
|
||||
| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your ticker interval. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
|
||||
| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe (ticker interval). As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br> **Datatype:** Integer
|
||||
| `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br> **Datatype:** Boolean
|
||||
|
||||
## Running Edge independently
|
||||
|
||||
@@ -62,6 +62,11 @@ res = [ f"{x['MarketCurrency']}/{x['BaseCurrency']}" for x in ct.publicGetMarket
|
||||
print(res)
|
||||
```
|
||||
|
||||
## All exchanges
|
||||
|
||||
Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
|
||||
|
||||
|
||||
## Random notes for other exchanges
|
||||
|
||||
* The Ocean (exchange id: `theocean`) exchange uses Web3 functionality and requires `web3` python package to be installed:
|
||||
@@ -71,8 +76,8 @@ $ pip3 install web3
|
||||
|
||||
### Send incomplete candles to the strategy
|
||||
|
||||
Most exchanges return incomplete candles via their ohlcv / klines interface.
|
||||
By default, Freqtrade assumes that incomplete candles are returned and removes the last candle assuming it's an incomplete candle.
|
||||
Most exchanges return current incomplete candle via their OHLCV/klines API interface.
|
||||
By default, Freqtrade assumes that incomplete candle is fetched from the exchange and removes the last candle assuming it's the incomplete candle.
|
||||
|
||||
Whether your exchange returns incomplete candles or not can be checked using [the helper script](developer.md#Incomplete-candles) from the Contributor documentation.
|
||||
|
||||
|
||||
@@ -31,9 +31,9 @@ This will create a new hyperopt file from a template, which will be located unde
|
||||
Depending on the space you want to optimize, only some of the below are required:
|
||||
|
||||
* fill `buy_strategy_generator` - for buy signal optimization
|
||||
* fill `indicator_space` - for buy signal optimzation
|
||||
* fill `indicator_space` - for buy signal optimization
|
||||
* fill `sell_strategy_generator` - for sell signal optimization
|
||||
* fill `sell_indicator_space` - for sell signal optimzation
|
||||
* fill `sell_indicator_space` - for sell signal optimization
|
||||
|
||||
!!! Note
|
||||
`populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
|
||||
@@ -81,11 +81,11 @@ There are two places you need to change in your hyperopt file to add a new buy h
|
||||
There you have two different types of indicators: 1. `guards` and 2. `triggers`.
|
||||
|
||||
1. Guards are conditions like "never buy if ADX < 10", or never buy if current price is over EMA10.
|
||||
2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower bollinger band".
|
||||
2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower Bollinger band".
|
||||
|
||||
Hyperoptimization will, for each eval round, pick one trigger and possibly
|
||||
multiple guards. The constructed strategy will be something like
|
||||
"*buy exactly when close price touches lower bollinger band, BUT only if
|
||||
"*buy exactly when close price touches lower Bollinger band, BUT only if
|
||||
ADX > 10*".
|
||||
|
||||
If you have updated the buy strategy, i.e. changed the contents of
|
||||
@@ -103,9 +103,10 @@ Place the corresponding settings into the following methods
|
||||
The configuration and rules are the same than for buy signals.
|
||||
To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
|
||||
|
||||
#### Using ticker-interval as part of the Strategy
|
||||
#### Using timeframe as a 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`.
|
||||
The Strategy class exposes the timeframe (ticker interval) value as the `self.ticker_interval` attribute.
|
||||
The same value is available as class-attribute `HyperoptName.ticker_interval`.
|
||||
In the case of the linked sample-value this would be `SampleHyperOpt.ticker_interval`.
|
||||
|
||||
## Solving a Mystery
|
||||
@@ -159,6 +160,9 @@ So let's write the buy strategy using these values:
|
||||
dataframe['macd'], dataframe['macdsignal']
|
||||
))
|
||||
|
||||
# Check that volume is not 0
|
||||
conditions.append(dataframe['volume'] > 0)
|
||||
|
||||
if conditions:
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
@@ -172,7 +176,7 @@ So let's write the buy strategy using these values:
|
||||
Hyperopting will now call this `populate_buy_trend` as many times you ask it (`epochs`)
|
||||
with different value combinations. It will then use the given historical data and make
|
||||
buys based on the buy signals generated with the above function and based on the results
|
||||
it will end with telling you which paramter combination produced the best profits.
|
||||
it will end with telling you which parameter combination produced the best profits.
|
||||
|
||||
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
|
||||
When you want to test an indicator that isn't used by the bot currently, remember to
|
||||
@@ -191,8 +195,10 @@ Currently, the following loss functions are builtin:
|
||||
|
||||
* `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function)
|
||||
* `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
|
||||
* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on the trade returns)
|
||||
* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on daily trade returns)
|
||||
* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on trade returns relative to standard deviation)
|
||||
* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation)
|
||||
* `SortinoHyperOptLoss` (optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation)
|
||||
* `SortinoHyperOptLossDaily` (optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation)
|
||||
|
||||
Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
|
||||
|
||||
@@ -220,11 +226,11 @@ The `--spaces all` option determines that all possible parameters should be opti
|
||||
!!! Warning
|
||||
When switching parameters or changing configuration options, make sure to not use the argument `--continue` so temporary results can be removed.
|
||||
|
||||
### Execute Hyperopt with Different Ticker-Data Source
|
||||
### Execute Hyperopt with different historical data source
|
||||
|
||||
If you would like to hyperopt parameters using an alternate ticker data that
|
||||
you have on-disk, use the `--datadir PATH` option. Default hyperopt will
|
||||
use data from directory `user_data/data`.
|
||||
If you would like to hyperopt parameters using an alternate historical data set that
|
||||
you have on-disk, use the `--datadir PATH` option. By default, hyperopt
|
||||
uses data from directory `user_data/data`.
|
||||
|
||||
### Running Hyperopt with Smaller Testset
|
||||
|
||||
@@ -272,7 +278,7 @@ In some situations, you may need to run Hyperopt (and Backtesting) with the
|
||||
By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
|
||||
open trade is allowed for every traded pair. The total number of trades open for all pairs
|
||||
is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
|
||||
some potential trades to be hidden (or masked) by previosly open trades.
|
||||
some potential trades to be hidden (or masked) by previously open trades.
|
||||
|
||||
The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times,
|
||||
while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades`
|
||||
@@ -378,7 +384,7 @@ As stated in the comment, you can also use it as the value of the `minimal_roi`
|
||||
|
||||
#### Default ROI Search Space
|
||||
|
||||
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
|
||||
If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values vary in the following ranges (for some of the most used timeframes, values are rounded to 5 digits after the decimal point):
|
||||
|
||||
| # step | 1m | | 5m | | 1h | | 1d | |
|
||||
| ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
|
||||
@@ -387,7 +393,7 @@ If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace f
|
||||
| 3 | 4...20 | 0.00387...0.01547 | 20...100 | 0.01...0.04 | 240...1200 | 0.02294...0.09177 | 5760...28800 | 0.04059...0.16237 |
|
||||
| 4 | 6...44 | 0.0 | 30...220 | 0.0 | 360...2640 | 0.0 | 8640...63360 | 0.0 |
|
||||
|
||||
These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the ticker interval used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the ticker interval used.
|
||||
These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe (ticker interval) used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
|
||||
|
||||
If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
|
||||
|
||||
|
||||
@@ -2,6 +2,8 @@
|
||||
|
||||
This page explains how to prepare your environment for running the bot.
|
||||
|
||||
Please consider using the prebuilt [docker images](docker.md) to get started quickly while trying out freqtrade evaluating how it operates.
|
||||
|
||||
## Prerequisite
|
||||
|
||||
### Requirements
|
||||
@@ -14,15 +16,7 @@ Click each one for install guide:
|
||||
* [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
|
||||
* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
|
||||
|
||||
### API keys
|
||||
|
||||
Before running your bot in production you will need to setup few
|
||||
external API. In production mode, the bot will require valid Exchange API
|
||||
credentials. We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot) (optional but recommended).
|
||||
|
||||
### Setup your exchange account
|
||||
|
||||
You will need to create API Keys (Usually you get `key` and `secret`) from the Exchange website and insert this into the appropriate fields in the configuration or when asked by the installation script.
|
||||
We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
|
||||
|
||||
## Quick start
|
||||
|
||||
@@ -65,11 +59,11 @@ usage:
|
||||
|
||||
** --install **
|
||||
|
||||
With this option, the script will install everything you need to run the bot:
|
||||
With this option, the script will install the bot and most dependencies:
|
||||
You will need to have git and python3.6+ installed beforehand for this to work.
|
||||
|
||||
* Mandatory software as: `ta-lib`
|
||||
* Setup your virtualenv
|
||||
* Configure your `config.json` file
|
||||
* Setup your virtualenv under `.env/`
|
||||
|
||||
This option is a combination of installation tasks, `--reset` and `--config`.
|
||||
|
||||
@@ -83,7 +77,7 @@ This option will hard reset your branch (only if you are on either `master` or `
|
||||
|
||||
** --config **
|
||||
|
||||
Use this option to configure the `config.json` configuration file. The script will interactively ask you questions to setup your bot and create your `config.json`.
|
||||
DEPRECATED - use `freqtrade new-config -c config.json` instead.
|
||||
|
||||
------
|
||||
|
||||
|
||||
@@ -196,6 +196,7 @@ The first graph is good to get a grip of how the overall market progresses.
|
||||
|
||||
The second graph will show if your algorithm works or doesn't.
|
||||
Perhaps you want an algorithm that steadily makes small profits, or one that acts less often, but makes big swings.
|
||||
This graph will also highlight the start (and end) of the Max drawdown period.
|
||||
|
||||
The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
|
||||
|
||||
|
||||
@@ -83,7 +83,7 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
|
||||
Performance Note: For the best performance be frugal on the number of indicators
|
||||
you are using. Let uncomment only the indicator you are using in your strategies
|
||||
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
|
||||
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
|
||||
:param dataframe: Dataframe with data from the exchange
|
||||
:param metadata: Additional information, like the currently traded pair
|
||||
:return: a Dataframe with all mandatory indicators for the strategies
|
||||
"""
|
||||
@@ -248,6 +248,23 @@ minimal_roi = {
|
||||
|
||||
While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
|
||||
|
||||
To use times based on candle duration (ticker_interval or timeframe), the following snippet can be handy.
|
||||
This will allow you to change the ticket_interval for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
|
||||
|
||||
``` python
|
||||
from freqtrade.exchange import timeframe_to_minutes
|
||||
|
||||
class AwesomeStrategy(IStrategy):
|
||||
|
||||
ticker_interval = "1d"
|
||||
ticker_interval_mins = timeframe_to_minutes(ticker_interval)
|
||||
minimal_roi = {
|
||||
"0": 0.05, # 5% for the first 3 candles
|
||||
str(ticker_interval_mins * 3)): 0.02, # 2% after 3 candles
|
||||
str(ticker_interval_mins * 6)): 0.01, # 1% After 6 candles
|
||||
}
|
||||
```
|
||||
|
||||
### Stoploss
|
||||
|
||||
Setting a stoploss is highly recommended to protect your capital from strong moves against you.
|
||||
@@ -266,13 +283,14 @@ If your exchange supports it, it's recommended to also set `"stoploss_on_exchang
|
||||
|
||||
For more information on order_types please look [here](configuration.md#understand-order_types).
|
||||
|
||||
### Ticker interval
|
||||
### Timeframe (ticker interval)
|
||||
|
||||
This is the set of candles the bot should download and use for the analysis.
|
||||
Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
|
||||
|
||||
Please note that the same buy/sell signals may work with one interval, but not the other.
|
||||
This setting is accessible within the strategy by using `self.ticker_interval`.
|
||||
Please note that the same buy/sell signals may work well with one timeframe, but not with the others.
|
||||
|
||||
This setting is accessible within the strategy methods as the `self.ticker_interval` attribute.
|
||||
|
||||
### Metadata dict
|
||||
|
||||
@@ -317,14 +335,14 @@ Please always check the mode of operation to select the correct method to get da
|
||||
#### Possible options for DataProvider
|
||||
|
||||
- `available_pairs` - Property with tuples listing cached pairs with their intervals (pair, interval).
|
||||
- `ohlcv(pair, timeframe)` - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.
|
||||
- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
|
||||
- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
|
||||
- `get_pair_dataframe(pair, timeframe)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
|
||||
- `orderbook(pair, maximum)` - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
|
||||
- `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on Market data structure.
|
||||
- `runmode` - Property containing the current runmode.
|
||||
|
||||
#### Example: fetch live ohlcv / historic data for the first informative pair
|
||||
#### Example: fetch live / historical candle (OHLCV) data for the first informative pair
|
||||
|
||||
``` python
|
||||
if self.dp:
|
||||
@@ -359,8 +377,8 @@ if self.dp:
|
||||
|
||||
``` python
|
||||
if self.dp:
|
||||
for pair, ticker in self.dp.available_pairs:
|
||||
print(f"available {pair}, {ticker}")
|
||||
for pair, timeframe in self.dp.available_pairs:
|
||||
print(f"available {pair}, {timeframe}")
|
||||
```
|
||||
|
||||
#### Get data for non-tradeable pairs
|
||||
|
||||
@@ -121,7 +121,6 @@ from freqtrade.data.btanalysis import analyze_trade_parallelism
|
||||
# Analyze the above
|
||||
parallel_trades = analyze_trade_parallelism(trades, '5m')
|
||||
|
||||
|
||||
parallel_trades.plot()
|
||||
```
|
||||
|
||||
@@ -134,11 +133,14 @@ Freqtrade offers interactive plotting capabilities based on plotly.
|
||||
from freqtrade.plot.plotting import generate_candlestick_graph
|
||||
# Limit graph period to keep plotly quick and reactive
|
||||
|
||||
# Filter trades to one pair
|
||||
trades_red = trades.loc[trades['pair'] == pair]
|
||||
|
||||
data_red = data['2019-06-01':'2019-06-10']
|
||||
# Generate candlestick graph
|
||||
graph = generate_candlestick_graph(pair=pair,
|
||||
data=data_red,
|
||||
trades=trades,
|
||||
trades=trades_red,
|
||||
indicators1=['sma20', 'ema50', 'ema55'],
|
||||
indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
|
||||
)
|
||||
|
||||
@@ -61,8 +61,8 @@ $ freqtrade new-config --config config_binance.json
|
||||
? Do you want to enable Dry-run (simulated trades)? Yes
|
||||
? Please insert your stake currency: BTC
|
||||
? Please insert your stake amount: 0.05
|
||||
? Please insert max_open_trades (Integer or 'unlimited'): 5
|
||||
? Please insert your ticker interval: 15m
|
||||
? Please insert max_open_trades (Integer or 'unlimited'): 3
|
||||
? Please insert your timeframe (ticker interval): 5m
|
||||
? Please insert your display Currency (for reporting): USD
|
||||
? Select exchange binance
|
||||
? Do you want to enable Telegram? No
|
||||
@@ -264,7 +264,7 @@ All exchanges supported by the ccxt library: _1btcxe, acx, adara, allcoin, anxpr
|
||||
|
||||
## List Timeframes
|
||||
|
||||
Use the `list-timeframes` subcommand to see the list of ticker intervals (timeframes) available for the exchange.
|
||||
Use the `list-timeframes` subcommand to see the list of timeframes (ticker intervals) available for the exchange.
|
||||
|
||||
```
|
||||
usage: freqtrade list-timeframes [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [--exchange EXCHANGE] [-1]
|
||||
@@ -435,6 +435,7 @@ usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
|
||||
[--min-total-profit FLOAT]
|
||||
[--max-total-profit FLOAT] [--no-color]
|
||||
[--print-json] [--no-details]
|
||||
[--export-csv FILE]
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
@@ -456,6 +457,8 @@ optional arguments:
|
||||
useful if you are redirecting output to a file.
|
||||
--print-json Print best result detailization in JSON format.
|
||||
--no-details Do not print best epoch details.
|
||||
--export-csv FILE Export to CSV-File. This will disable table print.
|
||||
Example: --export-csv hyperopt.csv
|
||||
|
||||
Common arguments:
|
||||
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
|
||||
@@ -464,9 +467,10 @@ Common arguments:
|
||||
details.
|
||||
-V, --version show program's version number and exit
|
||||
-c PATH, --config PATH
|
||||
Specify configuration file (default: `config.json`).
|
||||
Multiple --config options may be used. Can be set to
|
||||
`-` to read config from stdin.
|
||||
Specify configuration file (default:
|
||||
`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
|
||||
Path to directory with historical backtesting data.
|
||||
--userdir PATH, --user-data-dir PATH
|
||||
|
||||
@@ -23,12 +23,12 @@ Sample configuration (tested using IFTTT).
|
||||
"webhooksell": {
|
||||
"value1": "Selling {pair}",
|
||||
"value2": "limit {limit:8f}",
|
||||
"value3": "profit: {profit_amount:8f} {stake_currency}"
|
||||
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
|
||||
},
|
||||
"webhooksellcancel": {
|
||||
"value1": "Cancelling Open Sell Order for {pair}",
|
||||
"value2": "limit {limit:8f}",
|
||||
"value3": "profit: {profit_amount:8f} {stake_currency}"
|
||||
"value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
|
||||
},
|
||||
"webhookstatus": {
|
||||
"value1": "Status: {status}",
|
||||
@@ -87,7 +87,7 @@ Possible parameters are:
|
||||
* `open_rate`
|
||||
* `current_rate`
|
||||
* `profit_amount`
|
||||
* `profit_percent`
|
||||
* `profit_ratio`
|
||||
* `stake_currency`
|
||||
* `fiat_currency`
|
||||
* `sell_reason`
|
||||
@@ -108,7 +108,7 @@ Possible parameters are:
|
||||
* `open_rate`
|
||||
* `current_rate`
|
||||
* `profit_amount`
|
||||
* `profit_percent`
|
||||
* `profit_ratio`
|
||||
* `stake_currency`
|
||||
* `fiat_currency`
|
||||
* `sell_reason`
|
||||
|
||||
Reference in New Issue
Block a user