Merge branch 'develop' into verify_date_on_new_candle_on_get_signal

.gitignore

@@ -6,7 +6,6 @@ user_data/*
 !user_data/strategy/sample_strategy.py
 !user_data/notebooks
 user_data/notebooks/*
-!user_data/notebooks/*example.ipynb
 freqtrade-plot.html
 freqtrade-profit-plot.html
 

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.8.2-slim-buster
+FROM python:3.8.3-slim-buster
 
 RUN apt-get update \
     && apt-get -y install curl build-essential libssl-dev \

MANIFEST.in

@@ -2,3 +2,4 @@ include LICENSE
 include README.md
 include config.json.example
 recursive-include freqtrade *.py
+recursive-include freqtrade/templates/ *.j2 *.ipynb

bin/freqtrade

@@ -1,11 +0,0 @@
-#!/usr/bin/env python3
-
-import sys
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-logger.error("DEPRECATED installation detected, please run `pip install -e .` again.")
-
-sys.exit(2)

build_helpers/install_windows.ps1

@@ -7,10 +7,10 @@ python -m pip install --upgrade pip
 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
 
 if ($pyv -eq '3.7') {
-    pip install build_helpers\TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.18-cp37-cp37m-win_amd64.whl
 }
 if ($pyv -eq '3.8') {
-    pip install build_helpers\TA_Lib-0.4.17-cp38-cp38-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.18-cp38-cp38-win_amd64.whl
 }
 
 pip install -r requirements-dev.txt

config.json.example

@@ -6,6 +6,7 @@
     "fiat_display_currency": "USD",
     "ticker_interval": "5m",
     "dry_run": false,
+    "cancel_open_orders_on_exit": false,
     "trailing_stop": false,
     "unfilledtimeout": {
         "buy": 10,

config_binance.json.example

@@ -6,6 +6,7 @@
     "fiat_display_currency": "USD",
     "ticker_interval": "5m",
     "dry_run": true,
+    "cancel_open_orders_on_exit": false,
     "trailing_stop": false,
     "unfilledtimeout": {
         "buy": 10,

config_full.json.example

@@ -8,6 +8,7 @@
     "amend_last_stake_amount": false,
     "last_stake_amount_min_ratio": 0.5,
     "dry_run": false,
+    "cancel_open_orders_on_exit": false,
     "ticker_interval": "5m",
     "trailing_stop": false,
     "trailing_stop_positive": 0.005,
@@ -120,6 +121,7 @@
         "enabled": false,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
+        "jwt_secret_key": "somethingrandom",
         "username": "freqtrader",
         "password": "SuperSecurePassword"
     },

config_kraken.json.example

@@ -6,6 +6,7 @@
     "fiat_display_currency": "EUR",
     "ticker_interval": "5m",
     "dry_run": true,
+    "cancel_open_orders_on_exit": false,
     "trailing_stop": false,
     "unfilledtimeout": {
         "buy": 10,

docker-compose.yml

@@ -3,6 +3,7 @@ version: '3'
 services:
   freqtrade:
     image: freqtradeorg/freqtrade:master
+    # image: freqtradeorg/freqtrade:develop
     # Build step - only needed when additional dependencies are needed
     # build:
     #   context: .
@@ -14,7 +15,7 @@ services:
     # Default command used when running `docker compose up`
     command: >
       trade
-      --logfile /freqtrade/user_data/freqtrade.log
+      --logfile /freqtrade/user_data/logs/freqtrade.log
       --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
       --config /freqtrade/user_data/config.json
       --strategy SampleStrategy

docs/advanced-setup.md

@@ -37,30 +37,30 @@ as the watchdog.
 
 ## Advanced Logging
 
-On many Linux systems the bot can be configured to send its log messages to `syslog` or `journald` system services. Logging to a remote `syslog` server is also available on Windows. The special values for the `--logfilename` command line option can be used for this.
+On many Linux systems the bot can be configured to send its log messages to `syslog` or `journald` system services. Logging to a remote `syslog` server is also available on Windows. The special values for the `--logfile` command line option can be used for this.
 
 ### Logging to syslog
 
-To send Freqtrade log messages to a local or remote `syslog` service use the `--logfilename` command line option with the value in the following format:
+To send Freqtrade log messages to a local or remote `syslog` service use the `--logfile` command line option with the value in the following format:
 
-* `--logfilename syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
+* `--logfile syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
 
 The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
 
 So, the following are the examples of possible usages:
 
-* `--logfilename syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
+* `--logfile syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
-* `--logfilename syslog` -- same as above, the shortcut for `/dev/log`.
+* `--logfile syslog` -- same as above, the shortcut for `/dev/log`.
-* `--logfilename syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
+* `--logfile syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
-* `--logfilename syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
+* `--logfile syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
-* `--logfilename syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
+* `--logfile syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
 
 Log messages are send to `syslog` with the `user` facility. So you can see them with the following commands:
 
 * `tail -f /var/log/user`, or 
 * install a comprehensive graphical viewer (for instance, 'Log File Viewer' for Ubuntu).
 
-On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfilename syslog` or `--logfilename journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
+On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
 
 For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
 ```
@@ -78,9 +78,9 @@ $RepeatedMsgReduction on
 
 This needs the `systemd` python package installed as the dependency, which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
 
-To send Freqtrade log messages to `journald` system service use the `--logfilename` command line option with the value in the following format:
+To send Freqtrade log messages to `journald` system service use the `--logfile` command line option with the value in the following format:
 
-* `--logfilename journald` -- send log messages to `journald`.
+* `--logfile journald` -- send log messages to `journald`.
 
 Log messages are send to `journald` with the `user` facility. So you can see them with the following commands:
 
@@ -89,4 +89,4 @@ Log messages are send to `journald` with the `user` facility. So you can see the
 
 There are many other options in the `journalctl` utility to filter the messages, see manual pages for this utility.
 
-On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfilename syslog` or `--logfilename journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
+On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.

docs/backtesting.md

@@ -11,30 +11,34 @@ Now you have good Buy and Sell strategies and some historic data, you want to te
 real data. This is what we call
 [backtesting](https://en.wikipedia.org/wiki/Backtesting).
 
-Backtesting will use the crypto-currencies (pairs) from your config file and load ticker data from `user_data/data/<exchange>` by default.
+Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
-If no data is available for the exchange / pair / ticker interval combination, backtesting will ask you to download them first using `freqtrade download-data`.
+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`.
 For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
 
 The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
 
-!!! Tip "Using dynamic pairlists for backtesting"
+!!! Warning "Using dynamic pairlists for backtesting"
-    While using dynamic pairlists during backtesting is not possible, a dynamic pairlist using current data can be generated via the [`test-pairlist`](utils.md#test-pairlist) command, and needs to be specified as `"pair_whitelist"` attribute in the configuration.
+    Using dynamic pairlists is possible, however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
+    Also, when using pairlists other than StaticPairlist, reproducability of backtesting-results cannot be guaranteed.
+    Please read the [pairlists documentation](configuration.md#pairlists) for more information.
+
+    To achieve reproducible results, best generate a pairlist via the [`test-pairlist`](utils.md#test-pairlist) command and use that as static pairlist.
 
 ### Run a backtesting against the currencies listed in your config file
 
-#### With 5 min tickers (Per default)
+#### With 5 min candle (OHLCV) data (per default)
 
 ```bash
 freqtrade backtesting
 ```
 
-#### With 1 min tickers
+#### With 1 min candle (OHLCV) data
 
 ```bash
 freqtrade backtesting --ticker-interval 1m
 ```
 
-#### Using a different on-disk ticker-data source
+#### Using a different on-disk historical candle (OHLCV) data source
 
 Assume you downloaded the history data from the Bittrex exchange and kept it in the `user_data/data/bittrex-20180101` directory. 
 You can then use this data for backtesting as follows:
@@ -198,7 +202,7 @@ Since backtesting lacks some detailed information about what happens within a ca
 
 - Buys happen at open-price
 - Sell signal sells happen at open-price of the following candle
-- Low happens before high for stoploss, protecting capital first.
+- Low happens before high for stoploss, protecting capital first
 - ROI
   - sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
   - sells are never "below the candle", so a ROI of 2% may result in a sell at 2.4% if low was at 2.4% profit
@@ -208,6 +212,7 @@ Since backtesting lacks some detailed information about what happens within a ca
   - High happens first - adjusting stoploss
   - Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
 - Sell-reason does not explain if a trade was positive or negative, just what triggered the sell (this can look odd if negative ROI values are used)
+- Stoploss (and trailing stoploss) is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` and/or `trailing_stop` sell reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes.
 
 Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.
 Also, keep in mind that past results don't guarantee future success.
@@ -223,7 +228,7 @@ You can then load the trades to perform further analysis as shown in our [data a
 
 To compare multiple strategies, a list of Strategies can be provided to backtesting.
 
-This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple
+This is limited to 1 timeframe (ticker interval) value per run. However, data is only loaded once from disk so if you have multiple
 strategies you'd like to compare, this will give a nice runtime boost.
 
 All listed Strategies need to be in the same directory.

docs/bot-usage.md

@@ -144,10 +144,10 @@ It is recommended to use version control to keep track of changes to your strate
 ### How to use **--strategy**?
 
 This parameter will allow you to load your custom strategy class.
-Per default without `--strategy` or `-s` the bot will load the
+To test the bot installation, you can use the `SampleStrategy` installed by the `create-userdir` subcommand (usually `user_data/strategy/sample_strategy.py`).
-`DefaultStrategy` included with the bot (`freqtrade/strategy/default_strategy.py`).
 
-The bot will search your strategy file within `user_data/strategies` and `freqtrade/strategy`.
+The bot will search your strategy file within `user_data/strategies`.
+To use other directories, please read the next section about `--strategy-path`.
 
 To load a strategy, simply pass the class name (e.g.: `CustomStrategy`) in this parameter.
 

docs/configuration.md

@@ -34,23 +34,24 @@ The prevelance for all Options is as follows:
 
 - CLI arguments override any other option
 - Configuration files are used in sequence (last file wins), and override Strategy configurations.
-- Strategy configurations are only used if they are not set via configuration or via command line arguments. These options are market with [Strategy Override](#parameters-in-the-strategy) in the below table.
+- Strategy configurations are only used if they are not set via configuration or via command line arguments. These options are marked with [Strategy Override](#parameters-in-the-strategy) in the below table.
 
 Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
 
 |  Parameter | Description |
 |------------|-------------|
-| `max_open_trades` | **Required.** Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
+| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation which can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
 | `stake_currency` | **Required.** Crypto-currency used for trading. [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
 | `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Positive float or `"unlimited"`.
 | `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> **Datatype:** Positive float between `0.1` and `1.0`.
 | `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
 | `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)
 | `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.
-| `ticker_interval` | The ticker interval to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
+| `ticker_interval` | The timeframe (ticker interval) to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** String
 | `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
 | `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
 | `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
+| `cancel_open_orders_on_exit` | Cancel open orders when the `/stop` RPC command is issued, `Ctrl+C` is pressed or the bot dies unexpectedly. When set to `true`, this allows you to use `/stop` to cancel unfilled and partially filled orders in the event of a market crash. It does not impact open positions. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
 | `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> **Datatype:** Boolean
 | `minimal_roi` | **Required.** Set the threshold in percent the bot will use to sell a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
 | `stoploss` |  **Required.** Value of the stoploss in percent used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).  <br> **Datatype:** Float (as ratio)
@@ -80,14 +81,14 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.secret` | API secret to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `exchange.password` | API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
-| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#dynamic-pairlists)). <br> **Datatype:** List
+| `exchange.pair_whitelist` | List of pairs to use by the bot for trading and to check for potential trades during backtesting. Not used by VolumePairList (see [below](#pairlists-and-pairlist-handlers)). <br> **Datatype:** List
-| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#dynamic-pairlists)). <br> **Datatype:** List
+| `exchange.pair_blacklist` | List of pairs the bot must absolutely avoid for trading and backtesting (see [below](#pairlists-and-pairlist-handlers)). <br> **Datatype:** List
 | `exchange.ccxt_config` | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
 | `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) <br> **Datatype:** Dict
 | `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
 | `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation.
 | `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
-| `pairlists` | Define one or more pairlists to be used. [More information below](#dynamic-pairlists). <br>*Defaults to `StaticPairList`.*  <br> **Datatype:** List of Dicts
+| `pairlists` | Define one or more pairlists to be used. [More information below](#pairlists-and-pairlist-handlers). <br>*Defaults to `StaticPairList`.*  <br> **Datatype:** List of Dicts
 | `telegram.enabled` | Enable the usage of Telegram. <br> **Datatype:** Boolean
 | `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
@@ -108,13 +109,13 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `forcebuy_enable` | Enables the RPC Commands to force a buy. More information below. <br> **Datatype:** Boolean
 | `strategy` | **Required** Defines Strategy class to use. Recommended to be set via `--strategy NAME`. <br> **Datatype:** ClassName
 | `strategy_path` | Adds an additional strategy lookup path (must be a directory). <br> **Datatype:** String
-| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Intege
+| `internals.process_throttle_secs` | Set the process throttle. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
 | `internals.heartbeat_interval` | Print heartbeat message every N seconds. Set to 0 to disable heartbeat messages. <br>*Defaults to `60` seconds.* <br> **Datatype:** Positive Integer or 0
 | `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
 | `logfile` | Specifies logfile name. Uses a rolling strategy for log file rotation for 10 files with the 1MB limit per file. <br> **Datatype:** String
 | `user_data_dir` | Directory containing user data. <br> *Defaults to `./user_data/`*. <br> **Datatype:** String
-| `dataformat_ohlcv` | Data format to use to store OHLCV historic data. <br> *Defaults to `json`*. <br> **Datatype:** String
+| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
-| `dataformat_trades` | Data format to use to store trades historic data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
+| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
 
 ### Parameters in the strategy
 
@@ -413,7 +414,7 @@ Advanced options can be configured using the `_ft_has_params` setting, which wil
 
 Available options are listed in the exchange-class as `_ft_has_default`.
 
-For example, to test the order type `FOK` with Kraken, and modify candle_limit to 200 (so you only get 200 candles per call):
+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):
 
 ```json
 "exchange": {
@@ -544,32 +545,33 @@ A fixed slot (mirroring `bid_strategy.order_book_top`) can be defined by setting
     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 price at the `ask_strategy.price_side` side (defaults to `"ask"`) from the ticker will be used as the sell price.
 
-## Pairlists
+## Pairlists and Pairlist Handlers
 
-Pairlists define the list of pairs that the bot should trade.
+Pairlist Handlers define the list of pairs (pairlist) that the bot should trade. They are configured in the `pairlists` section of the configuration settings.
-There are [`StaticPairList`](#static-pair-list) and dynamic Whitelists available.
 
-[`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter) act as filters, removing low-value pairs.
+In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list) Pairlist Handler).
 
-All pairlists can be chained, and a combination of all pairlists will become your new whitelist. Pairlists are executed in the sequence they are configured. You should always configure either `StaticPairList` or `DynamicPairList` as starting pairlists.
+Additionaly, [`PrecisionFilter`](#precisionfilter), [`PriceFilter`](#pricefilter), [`ShuffleFilter`](#shufflefilter) and [`SpreadFilter`](#spreadfilter) act as Pairlist Filters, removing certain pairs and/or moving their positions in the pairlist.
 
-Inactive markets and blacklisted pairs are always removed from the resulting `pair_whitelist`.
+If multiple Pairlist Handlers are used, they are chained and a combination of all Pairlist Handlers forms the resulting pairlist the bot uses for trading and backtesting. Pairlist Handlers are executed in the sequence they are configured. You should always configure either `StaticPairList` or `VolumePairList` as the starting Pairlist Handler.
 
-### Available Pairlists
+Inactive markets are always removed from the resulting pairlist. Explicitly blacklisted pairs (those in the `pair_blacklist` configuration setting) are also always removed from the resulting pairlist.
+
+### Available Pairlist Handlers
 
 * [`StaticPairList`](#static-pair-list) (default, if not configured differently)
 * [`VolumePairList`](#volume-pair-list)
-* [`PrecisionFilter`](#precision-filter)
+* [`PrecisionFilter`](#precisionfilter)
-* [`PriceFilter`](#price-pair-filter)
+* [`PriceFilter`](#pricefilter)
-* [`SpreadFilter`](#spread-filter)
+* [`ShuffleFilter`](#shufflefilter)
+* [`SpreadFilter`](#spreadfilter)
 
 !!! Tip "Testing pairlists"
-    Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) subcommand to test your configuration quickly.
+    Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) utility subcommand to test your configuration quickly.
 
 #### Static Pair List
 
@@ -585,16 +587,16 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
 
 #### Volume Pair List
 
-`VolumePairList` selects `number_assets` top pairs based on `sort_key`, which can be one of `askVolume`, `bidVolume` and `quoteVolume` and defaults to `quoteVolume`.
+`VolumePairList` employs sorting/filtering of pairs by their trading volume. I selects `number_assets` top pairs with sorting based on the `sort_key` (which can only be `quoteVolume`).
 
-`VolumePairList` considers outputs of previous pairlists unless  it's the first configured pairlist, it does not consider `pair_whitelist`, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
+When used in the chain of Pairlist Handlers in a non-leading position (after StaticPairList and other Pairlist Filters), `VolumePairList` considers outputs of previous Pairlist Handlers, adding its sorting/selection of the pairs by the trading volume.
 
-`refresh_period` allows setting the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
+When used on the leading position of the chain of Pairlist Handlers, it does not consider `pair_whitelist` configuration setting, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
 
-`VolumePairList` is based on the ticker data, as reported by the ccxt library:
+The `refresh_period` setting allows to define the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
+
+`VolumePairList` is based on the ticker data from exchange, 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
@@ -606,29 +608,41 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
 ],
 ```
 
-#### Precision Filter
+#### PrecisionFilter
 
-Filters low-value coins which would not allow setting a stoploss.
+Filters low-value coins which would not allow setting stoplosses.
 
-#### Price Pair Filter
+#### PriceFilter
 
 The `PriceFilter` allows filtering of pairs by price.
-Currently, only `low_price_ratio` is implemented, where a raise of 1 price unit (pip) is below the `low_price_ratio` ratio.
+
+Currently, only `low_price_ratio` setting is implemented, where a raise of 1 price unit (pip) is below the `low_price_ratio` ratio.
 This option is disabled by default, and will only apply if set to <> 0.
 
-Calculation example:  
+Calculation example:
-Min price precision is 8 decimals. If price is 0.00000011 - one step would be 0.00000012 - which is almost 10% higher than the previous value. 
 
-These pairs are dangerous since it may be impossible to place the desired stoploss - and often result in high losses.
+Min price precision is 8 decimals. If price is 0.00000011 - one step would be 0.00000012 - which is almost 10% higher than the previous value.
+
+These pairs are dangerous since it may be impossible to place the desired stoploss - and often result in high losses. Here is what the PriceFilters takes over.
+
+#### ShuffleFilter
+
+Shuffles (randomizes) pairs in the pairlist. It can be used for preventing the bot from trading some of the pairs more frequently then others when you want all pairs be treated with the same priority.
+
+!!! Tip
+    You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order.
+
+#### SpreadFilter
+
+Removes pairs that have a difference between asks and bids above the specified ratio, `max_spread_ratio` (defaults to `0.005`).
 
-#### Spread Filter
-Removes pairs that have a difference between asks and bids above the specified ratio (default `0.005`).
 Example:
-If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027 the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005` 
 
-### Full Pairlist example
+If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027, the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005` and this pair will be filtered out.
 
-The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting by `quoteVolume` and applies both [`PrecisionFilter`](#precision-filter) and [`PriceFilter`](#price-pair-filter), filtering all assets where 1 priceunit is > 1%.
+### Full example of Pairlist Handlers
+
+The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies both [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#price-filter), filtering all assets where 1 priceunit is > 1%. Then the `SpreadFilter` is applied and pairs are finally shuffled with the random seed set to some predefined value.
 
 ```json
 "exchange": {
@@ -642,7 +656,9 @@ The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets,
         "sort_key": "quoteVolume",
     },
     {"method": "PrecisionFilter"},
-    {"method": "PriceFilter", "low_price_ratio": 0.01}
+    {"method": "PriceFilter", "low_price_ratio": 0.01},
+    {"method": "SpreadFilter", "max_spread_ratio": 0.005},
+    {"method": "ShuffleFilter", "seed": 42}
     ],
 ```
 

docs/data-download.md

@@ -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).
+- To download historical candle (OHLCV) 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.
+- 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

docs/deprecated.md

@@ -24,3 +24,13 @@ and in freqtrade 2019.7 (master branch).
 `--live` in the context of backtesting allowed to download the latest tick data for backtesting.
 Did only download the latest 500 candles, so was ineffective in getting good backtest data.
 Removed in 2019-7-dev (develop branch) and in freqtrade 2019-8 (master branch)
+
+### Allow running multiple pairlists in sequence
+
+The former `"pairlist"` section in the configuration has been removed, and is replaced by `"pairlists"` - being a list to specify a sequence of pairlists.
+
+The old section of configuration parameters (`"pairlist"`) has been deprecated in 2019.11 and has been removed in 2020.4.
+
+### deprecation of bidVolume and askVolume from volumepairlist
+
+Since only quoteVolume can be compared between assets, the other options (bidVolume, askVolume) have been deprecated in 2020.4.

docs/developer.md

@@ -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())

docs/docker.md

@@ -22,6 +22,9 @@ Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.co
 !!! Note
     All below comands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
 
+!!! Note "Docker on Raspberry"
+    If you're running freqtrade on a Raspberry PI, you must change the image from `freqtradeorg/freqtrade:master` to `freqtradeorg/freqtrade:master_pi` or `freqtradeorg/freqtrade:develop_pi`, otherwise the image will not work.
+
 ### Docker quick start
 
 Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
@@ -65,7 +68,7 @@ docker-compose up -d
 
 #### Docker-compose logs
 
-Logs will be written to `user_data/freqtrade.log`. 
+Logs will be written to `user_data/logs/freqtrade.log`. 
 Alternatively, you can check the latest logs using `docker-compose logs -f`.
 
 #### Database

docs/edge.md

@@ -79,7 +79,7 @@ So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
 Expectancy = (5 X 0.28) – 0.72 = 0.68
 ```
 
-Superficially, this means that on average you expect this strategy’s trades to return .68 times the size of your loses. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
+Superficially, this means that on average you expect this strategy’s trades to return 1.68 times the size of your loses. Said another way, you can expect to win $1.68 for every $1 you lose. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
 
 It is important to remember that any system with an expectancy greater than 0 is profitable using past data. The key is finding one that will be profitable in the future.
 
@@ -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

docs/exchanges.md

@@ -74,23 +74,13 @@ Should you experience constant errors with Nonce (like `InvalidNonce`), it is be
 $ pip3 install web3
 ```
 
-### Send incomplete candles to the strategy
+### Getting latest price / Incomplete candles
 
-Most exchanges return incomplete candles via their ohlcv / klines interface.
+Most exchanges return current incomplete candle via their OHLCV/klines API interface.
-By default, Freqtrade assumes that incomplete candles are returned and removes the last candle assuming it's an incomplete candle.
+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.
 
-If the exchange does return incomplete candles and you would like to have incomplete candles in your strategy, you can set the following parameter in the configuration file.
+Due to the danger of repainting, Freqtrade does not allow you to use this incomplete candle.
 
-``` json
+However, if it is based on the need for the latest price for your strategy - then this requirement can be acquired using the [data provider](strategy-customization.md#possible-options-for-dataprovider) from within the strategy.
-{
-
-    "exchange": {
-        "_ft_has_params": {"ohlcv_partial_candle": false}
-    }
-}
-```
-
-!!! Warning "Danger of repainting"
-    Changing this parameter makes the strategy responsible to avoid repainting and handle this accordingly. Doing this is therefore not recommended, and should only be performed by experienced users who are fully aware of the impact this setting has.

docs/faq.md

@@ -100,7 +100,7 @@ $ tail -f /path/to/mylogfile.log | grep 'something'
 ```
 from a separate terminal window.
 
-On Windows, the `--logfilename` option is also supported by Freqtrade and you can use the `findstr` command to search the log for the string of interest:
+On Windows, the `--logfile` option is also supported by Freqtrade and you can use the `findstr` command to search the log for the string of interest:
 ```
 > type \path\to\mylogfile.log | findstr "something"
 ```

docs/hyperopt.md

@@ -6,9 +6,7 @@ algorithms included in the `scikit-optimize` package to accomplish this. The
 search will burn all your CPU cores, make your laptop sound like a fighter jet
 and still take a long time.
 
-In general, the search for best parameters starts with a few random combinations and then uses Bayesian search with a
+In general, the search for best parameters starts with a few random combinations (see [below](#reproducible-results) for more details) and then uses Bayesian search with a ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the [loss function](#loss-functions).
-ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace
-that minimizes the value of the [loss function](#loss-functions).
 
 Hyperopt requires historic data to be available, just as backtesting does.
 To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
@@ -16,6 +14,24 @@ To learn how to get data for the pairs and exchange you're interested in, head o
 !!! Bug
     Hyperopt can crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
 
+## Install hyperopt dependencies
+
+Since Hyperopt dependencies are not needed to run the bot itself, are heavy, can not be easily built on some platforms (like Raspberry PI), they are not installed by default. Before you run Hyperopt, you need to install the corresponding dependencies, as described in this section below.
+
+!!! Note
+    Since Hyperopt is a resource intensive process, running it on a Raspberry Pi is not recommended nor supported.
+
+### Docker
+
+The docker-image includes hyperopt dependencies, no further action needed.
+
+### Easy installation script (setup.sh) / Manual installation
+
+```bash
+source .env/bin/activate
+pip install -r requirements-hyperopt.txt
+```
+
 ## Prepare Hyperopting
 
 Before we start digging into Hyperopt, we recommend you to take a look at
@@ -47,6 +63,9 @@ Optional - can also be loaded from a strategy:
 !!! Note
     Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
 
+!!! Note
+    You always have to provide a strategy to Hyperopt, even if your custom Hyperopt class contains all methods.
+
 Rarely you may also need to override:
 
 * `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
@@ -103,9 +122,10 @@ 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 +179,9 @@ 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),
@@ -222,11 +245,11 @@ 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
+If you would like to hyperopt parameters using an alternate historical data set that
-you have on-disk, use the `--datadir PATH` option. Default hyperopt will
+you have on-disk, use the `--datadir PATH` option. By default, hyperopt
-use data from directory `user_data/data`.
+uses data from directory `user_data/data`.
 
 ### Running Hyperopt with Smaller Testset
 
@@ -289,7 +312,7 @@ You can also enable position stacking in the configuration file by explicitly se
 
 ### Reproducible results
 
-The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with a leading asterisk sign at the Hyperopt output.
+The search for optimal parameters starts with a few (currently 30) random combinations in the hyperspace of parameters, random Hyperopt epochs. These random epochs are marked with an asterisk character (`*`) in the first column in the Hyperopt output.
 
 The initial state for generation of these random values (random state) is controlled by the value of the `--random-state` command line option. You can set it to some arbitrary value of your choice to obtain reproducible results.
 
@@ -380,7 +403,7 @@ 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           |                   |
 | ------ | ------ | ----------------- | -------- | ----------- | ---------- | ----------------- | ------------ | ----------------- |
@@ -389,7 +412,7 @@ 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.
 

docs/installation.md

@@ -248,14 +248,14 @@ 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‑0.4.17‑cp36‑cp36m‑win32.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 precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version)
 
 ```cmd
 >cd \path\freqtrade-develop
 >python -m venv .env
 >.env\Scripts\activate.bat
 REM optionally install ta-lib from wheel
-REM >pip install TA_Lib‑0.4.17‑cp36‑cp36m‑win32.whl
+REM >pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl
 >pip install -r requirements.txt
 >pip install -e .
 >freqtrade

docs/plotting.md

@@ -23,44 +23,64 @@ The `freqtrade plot-dataframe` subcommand shows an interactive graph with three
 Possible arguments:
 
 ```
-usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-s NAME]
+usage: freqtrade plot-dataframe [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                                [--strategy-path PATH] [-p PAIRS [PAIRS ...]] [--indicators1 INDICATORS1 [INDICATORS1 ...]]
+                                [-d PATH] [--userdir PATH] [-s NAME]
-                                [--indicators2 INDICATORS2 [INDICATORS2 ...]] [--plot-limit INT] [--db-url PATH]
+                                [--strategy-path PATH] [-p PAIRS [PAIRS ...]]
-                                [--trade-source {DB,file}] [--export EXPORT] [--export-filename PATH] [--timerange TIMERANGE]
+                                [--indicators1 INDICATORS1 [INDICATORS1 ...]]
-                                [-i TICKER_INTERVAL]
+                                [--indicators2 INDICATORS2 [INDICATORS2 ...]]
+                                [--plot-limit INT] [--db-url PATH]
+                                [--trade-source {DB,file}] [--export EXPORT]
+                                [--export-filename PATH]
+                                [--timerange TIMERANGE] [-i TICKER_INTERVAL]
+                                [--no-trades]
 
 optional arguments:
   -h, --help            show this help message and exit
   -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
-                        Show profits for only these pairs. Pairs are space-separated.
+                        Show profits for only these pairs. Pairs are space-
+                        separated.
   --indicators1 INDICATORS1 [INDICATORS1 ...]
-                        Set indicators from your strategy you want in the first row of the graph. Space-separated list. Example:
+                        Set indicators from your strategy you want in the
+                        first row of the graph. Space-separated list. Example:
                         `ema3 ema5`. Default: `['sma', 'ema3', 'ema5']`.
   --indicators2 INDICATORS2 [INDICATORS2 ...]
-                        Set indicators from your strategy you want in the third row of the graph. Space-separated list. Example:
+                        Set indicators from your strategy you want in the
+                        third row of the graph. Space-separated list. Example:
                         `fastd fastk`. Default: `['macd', 'macdsignal']`.
-  --plot-limit INT      Specify tick limit for plotting. Notice: too high values cause huge files. Default: 750.
+  --plot-limit INT      Specify tick limit for plotting. Notice: too high
-  --db-url PATH         Override trades database URL, this is useful in custom deployments (default: `sqlite:///tradesv3.sqlite`
+                        values cause huge files. Default: 750.
-                        for Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for Dry Run).
+  --db-url PATH         Override trades database URL, this is useful in custom
+                        deployments (default: `sqlite:///tradesv3.sqlite` for
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
   --trade-source {DB,file}
-                        Specify the source for trades (Can be DB or file (backtest file)) Default: file
+                        Specify the source for trades (Can be DB or file
-  --export EXPORT       Export backtest results, argument are: trades. Example: `--export=trades`
+                        (backtest file)) Default: file
+  --export EXPORT       Export backtest results, argument are: trades.
+                        Example: `--export=trades`
   --export-filename PATH
-                        Save backtest results to the file with this filename. Requires `--export` to be set as well. Example:
+                        Save backtest results to the file with this filename.
-                        `--export-filename=user_data/backtest_results/backtest_today.json`
+                        Requires `--export` to be set as well. Example:
+                        `--export-filename=user_data/backtest_results/backtest
+                        _today.json`
   --timerange TIMERANGE
                         Specify what timerange of data to use.
   -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
-                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`, `1d`).
+                        Specify ticker interval (`1m`, `5m`, `30m`, `1h`,
+                        `1d`).
+  --no-trades           Skip using trades from backtesting file and DB.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
                         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
+                        Specify configuration file (default:
-                        `-` to read config from stdin.
+                        `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
@@ -68,9 +88,9 @@ Common arguments:
 
 Strategy arguments:
   -s NAME, --strategy NAME
-                        Specify strategy class name which will be used by the bot.
+                        Specify strategy class name which will be used by the
+                        bot.
   --strategy-path PATH  Specify additional strategy lookup path.
-
 ```
 
 Example:

docs/requirements-docs.txt

@@ -1,2 +1,2 @@
-mkdocs-material==4.6.3
+mkdocs-material==5.1.7
 mdx_truly_sane_lists==1.2

docs/rest-api.md

@@ -11,6 +11,7 @@ Sample configuration:
         "enabled": true,
         "listen_ip_address": "127.0.0.1",
         "listen_port": 8080,
+        "jwt_secret_key": "somethingrandom",
         "username": "Freqtrader",
         "password": "SuperSecret1!"
     },
@@ -29,7 +30,7 @@ This should return the response:
 {"status":"pong"}
 ```
 
-All other endpoints return sensitive info and require authentication, so are not available through a web browser.
+All other endpoints return sensitive info and require authentication and are therefore not available through a web browser.
 
 To generate a secure password, either use a password manager, or use the below code snipped.
 
@@ -38,6 +39,9 @@ import secrets
 secrets.token_hex()
 ```
 
+!!! Hint
+    Use the same method to also generate a JWT secret key (`jwt_secret_key`).
+
 ### Configuration with docker
 
 If you run your bot using docker, you'll need to have the bot listen to incomming connections. The security is then handled by docker.
@@ -202,3 +206,28 @@ whitelist
         Show the current whitelist
         :returns: json object
 ```
+
+## Advanced API usage using JWT tokens
+
+!!! Note
+    The below should be done in an application (a Freqtrade REST API client, which fetches info via API), and is not intended to be used on a regular basis.
+
+Freqtrade's REST API also offers JWT (JSON Web Tokens).
+You can login using the following command, and subsequently use the resulting access_token.
+
+``` bash
+> curl -X POST --user Freqtrader http://localhost:8080/api/v1/token/login
+{"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk2ODEsIm5iZiI6MTU4OTExOTY4MSwianRpIjoiMmEwYmY0NWUtMjhmOS00YTUzLTlmNzItMmM5ZWVlYThkNzc2IiwiZXhwIjoxNTg5MTIwNTgxLCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.qt6MAXYIa-l556OM7arBvYJ0SDI9J8bIk3_glDujF5g","refresh_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk2ODEsIm5iZiI6MTU4OTExOTY4MSwianRpIjoiZWQ1ZWI3YjAtYjMwMy00YzAyLTg2N2MtNWViMjIxNWQ2YTMxIiwiZXhwIjoxNTkxNzExNjgxLCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJ0eXBlIjoicmVmcmVzaCJ9.d1AT_jYICyTAjD0fiQAr52rkRqtxCjUGEMwlNuuzgNQ"}
+
+> access_token="eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk2ODEsIm5iZiI6MTU4OTExOTY4MSwianRpIjoiMmEwYmY0NWUtMjhmOS00YTUzLTlmNzItMmM5ZWVlYThkNzc2IiwiZXhwIjoxNTg5MTIwNTgxLCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.qt6MAXYIa-l556OM7arBvYJ0SDI9J8bIk3_glDujF5g"
+# Use access_token for authentication
+> curl -X GET --header "Authorization: Bearer ${access_token}" http://localhost:8080/api/v1/count
+
+```
+
+Since the access token has a short timeout (15 min) - the `token/refresh` request should be used periodically to get a fresh access token:
+
+``` bash
+> curl -X POST --header "Authorization: Bearer ${refresh_token}"http://localhost:8080/api/v1/token/refresh
+{"access_token":"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE1ODkxMTk5NzQsIm5iZiI6MTU4OTExOTk3NCwianRpIjoiMDBjNTlhMWUtMjBmYS00ZTk0LTliZjAtNWQwNTg2MTdiZDIyIiwiZXhwIjoxNTg5MTIwODc0LCJpZGVudGl0eSI6eyJ1IjoiRnJlcXRyYWRlciJ9LCJmcmVzaCI6ZmFsc2UsInR5cGUiOiJhY2Nlc3MifQ.1seHlII3WprjjclY6DpRhen0rqdF4j6jbvxIhUFaSbs"}
+```

docs/sql_cheatsheet.md

@@ -1,13 +1,20 @@
 # SQL Helper
+
 This page contains some help if you want to edit your sqlite db.
 
 ## Install sqlite3
-**Ubuntu/Debian installation**
+
+Sqlite3 is a terminal based sqlite application.
+Feel free to use a visual Database editor like SqliteBrowser if you feel more comfortable with that.
+
+### Ubuntu/Debian installation
+
 ```bash
 sudo apt-get install sqlite3
 ```
 
 ## Open the DB
+
 ```bash
 sqlite3
 .open <filepath>
@@ -16,45 +23,61 @@ sqlite3
 ## Table structure
 
 ### List tables
+
 ```bash
 .tables
 ```
 
 ### Display table structure
+
 ```bash
 .schema <table_name>
 ```
 
 ### Trade table structure
+
 ```sql
-CREATE TABLE trades (
+CREATE TABLE trades
-	id INTEGER NOT NULL,
+    id INTEGER NOT NULL,
-	exchange VARCHAR NOT NULL,
+    exchange VARCHAR NOT NULL,
-	pair VARCHAR NOT NULL,
+    pair VARCHAR NOT NULL,
-	is_open BOOLEAN NOT NULL,
+    is_open BOOLEAN NOT NULL,
-	fee_open FLOAT NOT NULL,
+    fee_open FLOAT NOT NULL,
-	fee_close FLOAT NOT NULL,
+    fee_open_cost FLOAT,
-	open_rate FLOAT,
+    fee_open_currency VARCHAR,
-	open_rate_requested FLOAT,
+    fee_close FLOAT NOT NULL,
-	close_rate FLOAT,
+    fee_close_cost FLOAT,
-	close_rate_requested FLOAT,
+    fee_close_currency VARCHAR,
-	close_profit FLOAT,
+    open_rate FLOAT,
-	stake_amount FLOAT NOT NULL,
+    open_rate_requested FLOAT,
-	amount FLOAT,
+    open_trade_price FLOAT,
-	open_date DATETIME NOT NULL,
+    close_rate FLOAT,
-	close_date DATETIME,
+    close_rate_requested FLOAT,
-	open_order_id VARCHAR,
+    close_profit FLOAT,
-	stop_loss FLOAT,
+    close_profit_abs FLOAT,
-	initial_stop_loss FLOAT,
+    stake_amount FLOAT NOT NULL,
-	stoploss_order_id VARCHAR,
+    amount FLOAT,
-	stoploss_last_update DATETIME,
+    open_date DATETIME NOT NULL,
-	max_rate FLOAT,
+    close_date DATETIME,
-	sell_reason VARCHAR,
+    open_order_id VARCHAR,
-	strategy VARCHAR,
+    stop_loss FLOAT,
-	ticker_interval INTEGER,
+    stop_loss_pct FLOAT,
-	PRIMARY KEY (id),
+    initial_stop_loss FLOAT,
-	CHECK (is_open IN (0, 1))
+    initial_stop_loss_pct FLOAT,
+    stoploss_order_id VARCHAR,
+    stoploss_last_update DATETIME,
+    max_rate FLOAT,
+    min_rate FLOAT,
+    sell_reason VARCHAR,
+    strategy VARCHAR,
+    ticker_interval INTEGER,
+    PRIMARY KEY (id),
+    CHECK (is_open IN (0, 1))
 );
+CREATE INDEX ix_trades_stoploss_order_id ON trades (stoploss_order_id);
+CREATE INDEX ix_trades_pair ON trades (pair);
+CREATE INDEX ix_trades_is_open ON trades (is_open);
+
 ```
 
 ## Get all trades in the table
@@ -67,22 +90,32 @@ SELECT * FROM trades;
 
 !!! Warning
   	Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, forcesell <tradeid> should be used to accomplish the same thing.  
-		It is strongly advised to backup your database file before making any manual changes.
+	It is strongly advised to backup your database file before making any manual changes.
 
 !!! Note
   	This should not be necessary after /forcesell, as forcesell orders are closed automatically by the bot on the next iteration.
 
 ```sql
 UPDATE trades
-SET is_open=0, close_date=<close_date>, close_rate=<close_rate>, close_profit=close_rate/open_rate-1, sell_reason=<sell_reason>  
+SET is_open=0,
+  close_date=<close_date>,
+  close_rate=<close_rate>,
+  close_profit=close_rate/open_rate-1,
+  close_profit_abs = (amount * <close_rate> * (1 - fee_close) - (amount * open_rate * 1 - fee_open),
+  sell_reason=<sell_reason>
 WHERE id=<trade_ID_to_update>;
 ```
 
-##### Example
+### Example
 
 ```sql
 UPDATE trades
-SET is_open=0, close_date='2017-12-20 03:08:45.103418', close_rate=0.19638016, close_profit=0.0496, sell_reason='force_sell'  
+SET is_open=0,
+  close_date='2017-12-20 03:08:45.103418',
+  close_rate=0.19638016,
+  close_profit=0.0496,
+  close_profit_abs = (amount * 0.19638016 * (1 - fee_close) - (amount * open_rate * 1 - fee_open)
+  sell_reason='force_sell'  
 WHERE id=31;
 ```
 
@@ -99,10 +132,3 @@ VALUES ('bittrex', 'ETH/BTC', 1, 0.0025, 0.0025, <open_rate>, <stake_amount>, <a
 INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date)
 VALUES ('bittrex', 'ETH/BTC', 1, 0.0025, 0.0025, 0.00258580, 0.002, 0.7715262081, '2017-11-28 12:44:24.000000')
 ```
-
-## Fix wrong fees in the table
-If your DB was created before [PR#200](https://github.com/freqtrade/freqtrade/pull/200) was merged (before 12/23/17).
-
-```sql
-UPDATE trades SET fee=0.0025 WHERE fee=0.005;
-```

docs/strategy-advanced.md

@@ -0,0 +1,91 @@
+# Advanced Strategies
+
+This page explains some advanced concepts available for strategies.
+If you're just getting started, please be familiar with the methods described in the [Strategy Customization](strategy-customization.md) documentation first.
+
+## Custom order timeout rules
+
+Simple, timebased 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 ordertypes, which allows you to decide based on custom criteria if a 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.
+
+### Custom order timeout example
+
+A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below.
+It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
+
+The function must return either `True` (cancel order) or `False` (keep order alive).
+
+``` python
+from datetime import datetime, timedelta
+from freqtrade.persistence import Trade
+
+class Awesomestrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
+    unfilledtimeout = {
+        'buy': 60 * 25,
+        'sell': 60 * 25
+    }
+
+    def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
+           return True
+        return False
+
+
+    def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
+        if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
+            return True
+        elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
+            return True
+        elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
+           return True
+        return False
+```
+
+!!! Note
+    For the above example, `unfilledtimeout` must be set to something bigger than 24h, otherwise that type of timeout will apply first.
+
+### Custom order timeout example (using additional data)
+
+``` python
+from datetime import datetime
+from freqtrade.persistence import Trade
+
+class Awesomestrategy(IStrategy):
+
+    # ... populate_* methods
+
+    # Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
+    unfilledtimeout = {
+        'buy': 60 * 25,
+        'sell': 60 * 25
+    }
+
+    def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+        ob = self.dp.orderbook(pair, 1)
+        current_price = ob['bids'][0][0]
+        # Cancel buy order if price is more than 2% above the order.
+        if current_price > order['price'] * 1.02:
+            return True
+        return False
+
+
+    def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+        ob = self.dp.orderbook(pair, 1)
+        current_price = ob['asks'][0][0]
+        # Cancel sell order if price is more than 2% below the order.
+        if current_price < order['price'] * 0.98:
+            return True
+        return False
+```

docs/strategy-customization.md

@@ -1,7 +1,6 @@
 # Strategy Customization
 
-This page explains where to customize your strategies, and add new
+This page explains where to customize your strategies, and add new indicators.
-indicators.
 
 ## Install a custom strategy file
 
@@ -84,7 +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
     """
@@ -284,13 +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.
+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 by using `self.ticker_interval`.
+
+This setting is accessible within the strategy methods as the `self.ticker_interval` attribute.
 
 ### Metadata dict
 
@@ -324,67 +324,14 @@ class Awesomestrategy(IStrategy):
 !!! Note
     If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
 
-### Additional data (DataProvider)
+***
 
-The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
+### Additional data (informative_pairs)
-
-All methods return `None` in case of failure (do not raise an exception).
-
-Please always check the mode of operation to select the correct method to get data (samples see below).
-
-#### 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.
-- `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
-
-``` python
-if self.dp:
-    inf_pair, inf_timeframe = self.informative_pairs()[0]
-    informative = self.dp.get_pair_dataframe(pair=inf_pair,
-                                             timeframe=inf_timeframe)
-```
-
-!!! Warning "Warning about backtesting"
-    Be carefull when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
-    for the backtesting runmode) provides the full time-range in one go,
-    so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).
-
-!!! Warning "Warning in hyperopt"
-    This option cannot currently be used during hyperopt.
-
-#### Orderbook
-
-``` python
-if self.dp:
-    if self.dp.runmode.value in ('live', 'dry_run'):
-        ob = self.dp.orderbook(metadata['pair'], 1)
-        dataframe['best_bid'] = ob['bids'][0][0]
-        dataframe['best_ask'] = ob['asks'][0][0]
-```
-
-!!! Warning
-    The order book is not part of the historic data which means backtesting and hyperopt will not work if this
-    method is used.
-
-#### Available Pairs
-
-``` python
-if self.dp:
-    for pair, ticker in self.dp.available_pairs:
-        print(f"available {pair}, {ticker}")
-```
 
 #### Get data for non-tradeable pairs
 
 Data for additional, informative pairs (reference pairs) can be beneficial for some strategies.
-Ohlcv data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see above).
+Ohlcv data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via `DataProvider` just as other pairs (see below).
 These parts will **not** be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting.
 
 The pairs need to be specified as tuples in the format `("pair", "interval")`, with pair as the first and time interval as the second argument.
@@ -404,6 +351,125 @@ def informative_pairs(self):
     It is however better to use resampling to longer time-intervals when possible
     to avoid hammering the exchange with too many requests and risk being blocked.
 
+***
+
+### Additional data (DataProvider)
+
+The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
+
+All methods return `None` in case of failure (do not raise an exception).
+
+Please always check the mode of operation to select the correct method to get data (samples see below).
+
+#### Possible options for DataProvider
+
+- [`available_pairs`](#available_pairs) - Property with tuples listing cached pairs with their intervals (pair, interval).
+- [`current_whitelist()`](#current_whitelist) - Returns a current list of whitelisted pairs. Useful for accessing dynamic whitelists (ie. VolumePairlist)
+- [`get_pair_dataframe(pair, timeframe)`](#get_pair_dataframepair-timeframe) - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
+- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
+- `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 the Market data structure.
+- `ohlcv(pair, timeframe)` - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
+- [`orderbook(pair, maximum)`](#orderbookpair-maximum) - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
+- [`ticker(pair)`](#tickerpair) - Returns current ticker data for the pair. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#price-tickers) for more details on the Ticker data structure.
+- `runmode` - Property containing the current runmode.
+
+#### Example Usages:
+
+#### *available_pairs*
+
+``` python
+if self.dp:
+    for pair, timeframe in self.dp.available_pairs:
+        print(f"available {pair}, {timeframe}")
+```
+
+#### *current_whitelist()*
+Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume. 
+
+The strategy might look something like this:
+
+*Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day ATR to buy and sell.*
+
+Due to the limited available data, it's very difficult to resample our `5m` candles into daily candles for use in a 14 day ATR. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
+
+Since we can't resample our data we will have to use an informative pair; and since our whitelist will be dynamic we don't know which pair(s) to use.
+
+This is where calling `self.dp.current_whitelist()` comes in handy.
+
+```python
+class SampleStrategy(IStrategy):
+    # strategy init stuff...
+
+    ticker_interval = '5m'
+
+    # more strategy init stuff..
+
+    def informative_pairs(self):
+
+        # get access to all pairs available in whitelist. 
+        pairs = self.dp.current_whitelist()
+        # Assign tf to each pair so they can be downloaded and cached for strategy.
+        informative_pairs = [(pair, '1d') for pair in pairs]
+        return informative_pairs
+
+    def populate_indicators(self, dataframe, metadata):
+        # Get the informative pair
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
+        # Get the 14 day ATR.
+        atr = ta.ATR(informative, timeperiod=14)
+        # Do other stuff
+```
+
+#### *get_pair_dataframe(pair, timeframe)*
+
+``` python
+# fetch live / historical candle (OHLCV) data for the first informative pair
+if self.dp:
+    inf_pair, inf_timeframe = self.informative_pairs()[0]
+    informative = self.dp.get_pair_dataframe(pair=inf_pair,
+                                             timeframe=inf_timeframe)
+```
+
+!!! Warning "Warning about backtesting"
+    Be carefull when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
+    for the backtesting runmode) provides the full time-range in one go,
+    so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).
+
+!!! Warning "Warning in hyperopt"
+    This option cannot currently be used during hyperopt.
+
+#### *orderbook(pair, maximum)*
+
+``` python
+if self.dp:
+    if self.dp.runmode.value in ('live', 'dry_run'):
+        ob = self.dp.orderbook(metadata['pair'], 1)
+        dataframe['best_bid'] = ob['bids'][0][0]
+        dataframe['best_ask'] = ob['asks'][0][0]
+```
+
+!!! Warning
+    The order book is not part of the historic data which means backtesting and hyperopt will not work if this
+    method is used.
+
+#### *ticker(pair)*
+
+``` python
+if self.dp:
+    if self.dp.runmode.value in ('live', 'dry_run'):
+        ticker = self.dp.ticker(metadata['pair'])
+        dataframe['last_price'] = ticker['last']
+        dataframe['volume24h'] = ticker['quoteVolume']
+        dataframe['vwap'] = ticker['vwap']
+```
+
+!!! Warning
+    Although the ticker data structure is a part of the ccxt Unified Interface, the values returned by this method can
+    vary for different exchanges. For instance, many exchanges do not return `vwap` values, the FTX exchange
+    does not always fills in the `last` field (so it can be None), etc. So you need to carefully verify the ticker
+    data returned from the exchange and add appropriate error handling / defaults.
+
+***
 ### Additional data (Wallets)
 
 The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
@@ -426,6 +492,7 @@ if self.wallets:
 - `get_used(asset)` - currently tied up balance (open orders)
 - `get_total(asset)` - total available balance - sum of the 2 above
 
+***
 ### Additional data (Trades)
 
 A history of Trades can be retrieved in the strategy by querying the database.

docs/utils.md

@@ -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 max_open_trades (Integer or 'unlimited'): 3
-? Please insert your ticker interval: 15m
+? 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
@@ -77,7 +77,7 @@ Results will be located in `user_data/strategies/<strategyclassname>.py`.
 
 ``` output
 usage: freqtrade new-strategy [-h] [--userdir PATH] [-s NAME]
-                              [--template {full,minimal}]
+                              [--template {full,minimal,advanced}]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -86,10 +86,10 @@ optional arguments:
   -s NAME, --strategy NAME
                         Specify strategy class name which will be used by the
                         bot.
-  --template {full,minimal}
+  --template {full,minimal,advanced}
-                        Use a template which is either `minimal` or `full`
+                        Use a template which is either `minimal`, `full`
-                        (containing multiple sample indicators). Default:
+                        (containing multiple sample indicators) or `advanced`.
-                        `full`.
+                        Default: `full`.
 
 ```
 
@@ -105,6 +105,12 @@ With custom user directory
 freqtrade new-strategy --userdir ~/.freqtrade/ --strategy AwesomeStrategy
 ```
 
+Using the advanced template (populates all optional functions and methods)
+
+```bash
+freqtrade new-strategy --strategy AwesomeStrategy --template advanced
+```
+
 ## Create new hyperopt
 
 Creates a new hyperopt from a template similar to SampleHyperopt.
@@ -114,7 +120,7 @@ Results will be located in `user_data/hyperopts/<classname>.py`.
 
 ``` output
 usage: freqtrade new-hyperopt [-h] [--userdir PATH] [--hyperopt NAME]
-                              [--template {full,minimal}]
+                              [--template {full,minimal,advanced}]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -122,10 +128,10 @@ optional arguments:
                         Path to userdata directory.
   --hyperopt NAME       Specify hyperopt class name which will be used by the
                         bot.
-  --template {full,minimal}
+  --template {full,minimal,advanced}
-                        Use a template which is either `minimal` or `full`
+                        Use a template which is either `minimal`, `full`
-                        (containing multiple sample indicators). Default:
+                        (containing multiple sample indicators) or `advanced`.
-                        `full`.
+                        Default: `full`.
 ```
 
 ### Sample usage of new-hyperopt
@@ -258,7 +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]
@@ -515,3 +521,48 @@ Prints JSON data with details for the last best epoch (i.e., the best of all epo
 ```
 freqtrade hyperopt-show --best -n -1 --print-json --no-header
 ```
+
+## Show trades
+
+Print selected (or all) trades from database to screen.
+
+```
+usage: freqtrade show-trades [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                             [-d PATH] [--userdir PATH] [--db-url PATH]
+                             [--trade-ids TRADE_IDS [TRADE_IDS ...]]
+                             [--print-json]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --db-url PATH         Override trades database URL, this is useful in custom
+                        deployments (default: `sqlite:///tradesv3.sqlite` for
+                        Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
+                        Dry Run).
+  --trade-ids TRADE_IDS [TRADE_IDS ...]
+                        Specify the list of trade ids.
+  --print-json          Print output in JSON format.
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE        Log to the file specified. Special values are:
+                        'syslog', 'journald'. See the documentation for more
+                        details.
+  -V, --version         show program's version number and exit
+  -c PATH, --config PATH
+                        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
+                        Path to userdata directory.
+```
+
+### Examples
+
+Print trades with id 2 and 3 as json
+
+``` bash
+freqtrade show-trades --db-url sqlite:///tradesv3.sqlite --trade-ids 2 3 --print-json
+```

freqtrade/__init__.py

@@ -24,4 +24,11 @@ if __version__ == 'develop':
         #     stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
     except Exception:
         # git not available, ignore
-        pass
+        try:
+            # Try Fallback to freqtrade_commit file (created by CI while building docker image)
+            from pathlib import Path
+            versionfile = Path('./freqtrade_commit')
+            if versionfile.is_file():
+                __version__ = f"docker-{versionfile.read_text()[:8]}"
+        except Exception:
+            pass

freqtrade/commands/__init__.py

@@ -19,7 +19,8 @@ from freqtrade.commands.list_commands import (start_list_exchanges,
                                               start_list_hyperopts,
                                               start_list_markets,
                                               start_list_strategies,
-                                              start_list_timeframes)
+                                              start_list_timeframes,
+                                              start_show_trades)
 from freqtrade.commands.optimize_commands import (start_backtesting,
                                                   start_edge, start_hyperopt)
 from freqtrade.commands.pairlist_commands import start_test_pairlist

freqtrade/commands/arguments.py

@@ -59,11 +59,13 @@ ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchang
 
 ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
                        "db_url", "trade_source", "export", "exportfilename",
-                       "timerange", "ticker_interval"]
+                       "timerange", "ticker_interval", "no_trades"]
 
 ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
                     "trade_source", "ticker_interval"]
 
+ARGS_SHOW_TRADES = ["db_url", "trade_ids", "print_json"]
+
 ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
                       "hyperopt_list_min_trades", "hyperopt_list_max_trades",
                       "hyperopt_list_min_avg_time", "hyperopt_list_max_avg_time",
@@ -78,7 +80,7 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
 NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
                     "list-markets", "list-pairs", "list-strategies",
                     "list-hyperopts", "hyperopt-list", "hyperopt-show",
-                    "plot-dataframe", "plot-profit"]
+                    "plot-dataframe", "plot-profit", "show-trades"]
 
 NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
 
@@ -163,7 +165,7 @@ class Arguments:
                                         start_list_markets, start_list_strategies,
                                         start_list_timeframes, start_new_config,
                                         start_new_hyperopt, start_new_strategy,
-                                        start_plot_dataframe, start_plot_profit,
+                                        start_plot_dataframe, start_plot_profit, start_show_trades,
                                         start_backtesting, start_hyperopt, start_edge,
                                         start_test_pairlist, start_trading)
 
@@ -297,7 +299,7 @@ class Arguments:
         # Add convert-data subcommand
         convert_data_cmd = subparsers.add_parser(
             'convert-data',
-            help='Convert OHLCV data from one format to another.',
+            help='Convert candle (OHLCV) data from one format to another.',
             parents=[_common_parser],
         )
         convert_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=True))
@@ -306,7 +308,7 @@ class Arguments:
         # Add convert-trade-data subcommand
         convert_trade_data_cmd = subparsers.add_parser(
             'convert-trade-data',
-            help='Convert trade-data from one format to another.',
+            help='Convert trade data from one format to another.',
             parents=[_common_parser],
         )
         convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))
@@ -330,6 +332,15 @@ class Arguments:
         plot_profit_cmd.set_defaults(func=start_plot_profit)
         self._build_args(optionlist=ARGS_PLOT_PROFIT, parser=plot_profit_cmd)
 
+        # Add show-trades subcommand
+        show_trades = subparsers.add_parser(
+            'show-trades',
+            help='Show trades.',
+            parents=[_common_parser],
+        )
+        show_trades.set_defaults(func=start_show_trades)
+        self._build_args(optionlist=ARGS_SHOW_TRADES, parser=show_trades)
+
         # Add hyperopt-list subcommand
         hyperopt_list_cmd = subparsers.add_parser(
             'hyperopt-list',

freqtrade/commands/build_config_commands.py

@@ -76,7 +76,7 @@ def ask_user_config() -> Dict[str, Any]:
         {
             "type": "text",
             "name": "ticker_interval",
-            "message": "Please insert your ticker interval:",
+            "message": "Please insert your timeframe (ticker interval):",
             "default": "5m",
         },
         {
@@ -163,7 +163,7 @@ def deploy_new_config(config_path: Path, selections: Dict[str, Any]) -> None:
             )
     except TemplateNotFound:
         selections['exchange'] = render_template(
-            templatefile=f"subtemplates/exchange_generic.j2",
+            templatefile="subtemplates/exchange_generic.j2",
             arguments=selections
         )
 

freqtrade/commands/cli_options.py

@@ -217,7 +217,7 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "print_json": Arg(
         '--print-json',
-        help='Print best result detailization in JSON format.',
+        help='Print output in JSON format.',
         action='store_true',
         default=False,
     ),
@@ -355,7 +355,7 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "dataformat_ohlcv": Arg(
         '--data-format-ohlcv',
-        help='Storage format for downloaded ohlcv data. (default: `%(default)s`).',
+        help='Storage format for downloaded candle (OHLCV) data. (default: `%(default)s`).',
         choices=constants.AVAILABLE_DATAHANDLERS,
         default='json'
     ),
@@ -372,8 +372,8 @@ AVAILABLE_CLI_OPTIONS = {
     ),
     "timeframes": Arg(
         '-t', '--timeframes',
-        help=f'Specify which tickers to download. Space-separated list. '
+        help='Specify which tickers to download. Space-separated list. '
-        f'Default: `1m 5m`.',
+        'Default: `1m 5m`.',
         choices=['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h',
                  '6h', '8h', '12h', '1d', '3d', '1w'],
         default=['1m', '5m'],
@@ -387,9 +387,9 @@ AVAILABLE_CLI_OPTIONS = {
     # Templating options
     "template": Arg(
         '--template',
-        help='Use a template which is either `minimal` or '
+        help='Use a template which is either `minimal`, '
-        '`full` (containing multiple sample indicators). Default: `%(default)s`.',
+        '`full` (containing multiple sample indicators) or `advanced`. Default: `%(default)s`.',
-        choices=['full', 'minimal'],
+        choices=['full', 'minimal', 'advanced'],
         default='full',
     ),
     # Plot dataframe
@@ -413,6 +413,11 @@ AVAILABLE_CLI_OPTIONS = {
         metavar='INT',
         default=750,
     ),
+    "no_trades": Arg(
+        '--no-trades',
+        help='Skip using trades from backtesting file and DB.',
+        action='store_true',
+    ),
     "trade_source": Arg(
         '--trade-source',
         help='Specify the source for trades (Can be DB or file (backtest file)) '
@@ -420,6 +425,11 @@ AVAILABLE_CLI_OPTIONS = {
         choices=["DB", "file"],
         default="file",
     ),
+    "trade_ids": Arg(
+        '--trade-ids',
+        help='Specify the list of trade ids.',
+        nargs='+',
+    ),
     # hyperopt-list, hyperopt-show
     "hyperopt_list_profitable": Arg(
         '--profitable',

freqtrade/commands/deploy_commands.py

@@ -8,7 +8,7 @@ from freqtrade.configuration.directory_operations import (copy_sample_files,
                                                           create_userdata_dir)
 from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
 from freqtrade.exceptions import OperationalException
-from freqtrade.misc import render_template
+from freqtrade.misc import render_template, render_template_with_fallback
 from freqtrade.state import RunMode
 
 logger = logging.getLogger(__name__)
@@ -32,10 +32,27 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
     """
     Deploy new strategy from template to strategy_path
     """
-    indicators = render_template(templatefile=f"subtemplates/indicators_{subtemplate}.j2",)
+    fallback = 'full'
-    buy_trend = render_template(templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",)
+    indicators = render_template_with_fallback(
-    sell_trend = render_template(templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",)
+        templatefile=f"subtemplates/indicators_{subtemplate}.j2",
-    plot_config = render_template(templatefile=f"subtemplates/plot_config_{subtemplate}.j2",)
+        templatefallbackfile=f"subtemplates/indicators_{fallback}.j2",
+        )
+    buy_trend = render_template_with_fallback(
+        templatefile=f"subtemplates/buy_trend_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/buy_trend_{fallback}.j2",
+        )
+    sell_trend = render_template_with_fallback(
+        templatefile=f"subtemplates/sell_trend_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/sell_trend_{fallback}.j2",
+        )
+    plot_config = render_template_with_fallback(
+        templatefile=f"subtemplates/plot_config_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/plot_config_{fallback}.j2",
+    )
+    additional_methods = render_template_with_fallback(
+        templatefile=f"subtemplates/strategy_methods_{subtemplate}.j2",
+        templatefallbackfile="subtemplates/strategy_methods_empty.j2",
+    )
 
     strategy_text = render_template(templatefile='base_strategy.py.j2',
                                     arguments={"strategy": strategy_name,
@@ -43,6 +60,7 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
                                                "buy_trend": buy_trend,
                                                "sell_trend": sell_trend,
                                                "plot_config": plot_config,
+                                               "additional_methods": additional_methods,
                                                })
 
     logger.info(f"Writing strategy to `{strategy_path}`.")
@@ -73,14 +91,23 @@ def deploy_new_hyperopt(hyperopt_name: str, hyperopt_path: Path, subtemplate: st
     """
     Deploys a new hyperopt template to hyperopt_path
     """
-    buy_guards = render_template(
+    fallback = 'full'
-        templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",)
+    buy_guards = render_template_with_fallback(
-    sell_guards = render_template(
+        templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",
-        templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",)
+        templatefallbackfile=f"subtemplates/hyperopt_buy_guards_{fallback}.j2",
-    buy_space = render_template(
+        )
-        templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",)
+    sell_guards = render_template_with_fallback(
-    sell_space = render_template(
+        templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",
-        templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",)
+        templatefallbackfile=f"subtemplates/hyperopt_sell_guards_{fallback}.j2",
+        )
+    buy_space = render_template_with_fallback(
+        templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/hyperopt_buy_space_{fallback}.j2",
+        )
+    sell_space = render_template_with_fallback(
+        templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",
+        templatefallbackfile=f"subtemplates/hyperopt_sell_space_{fallback}.j2",
+        )
 
     strategy_text = render_template(templatefile='base_hyperopt.py.j2',
                                     arguments={"hyperopt": hyperopt_name,

freqtrade/commands/hyperopt_commands.py

@@ -38,33 +38,33 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
         'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
     }
 
-    trials_file = (config['user_data_dir'] /
+    results_file = (config['user_data_dir'] /
-                   'hyperopt_results' / 'hyperopt_results.pickle')
+                    'hyperopt_results' / 'hyperopt_results.pickle')
 
     # Previous evaluations
-    trials = Hyperopt.load_previous_results(trials_file)
+    epochs = Hyperopt.load_previous_results(results_file)
-    total_epochs = len(trials)
+    total_epochs = len(epochs)
 
-    trials = _hyperopt_filter_trials(trials, filteroptions)
+    epochs = _hyperopt_filter_epochs(epochs, filteroptions)
 
     if print_colorized:
         colorama_init(autoreset=True)
 
     if not export_csv:
         try:
-            Hyperopt.print_result_table(config, trials, total_epochs,
+            print(Hyperopt.get_result_table(config, epochs, total_epochs,
-                                        not filteroptions['only_best'], print_colorized, 0)
+                                            not filteroptions['only_best'], print_colorized, 0))
         except KeyboardInterrupt:
             print('User interrupted..')
 
-    if trials and not no_details:
+    if epochs and not no_details:
-        sorted_trials = sorted(trials, key=itemgetter('loss'))
+        sorted_epochs = sorted(epochs, key=itemgetter('loss'))
-        results = sorted_trials[0]
+        results = sorted_epochs[0]
         Hyperopt.print_epoch_details(results, total_epochs, print_json, no_header)
 
-    if trials and export_csv:
+    if epochs and export_csv:
         Hyperopt.export_csv_file(
-            config, trials, total_epochs, not filteroptions['only_best'], export_csv
+            config, epochs, total_epochs, not filteroptions['only_best'], export_csv
         )
 
 
@@ -78,8 +78,8 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
 
     print_json = config.get('print_json', False)
     no_header = config.get('hyperopt_show_no_header', False)
-    trials_file = (config['user_data_dir'] /
+    results_file = (config['user_data_dir'] /
-                   'hyperopt_results' / 'hyperopt_results.pickle')
+                    'hyperopt_results' / 'hyperopt_results.pickle')
     n = config.get('hyperopt_show_index', -1)
 
     filteroptions = {
@@ -96,89 +96,87 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
     }
 
     # Previous evaluations
-    trials = Hyperopt.load_previous_results(trials_file)
+    epochs = Hyperopt.load_previous_results(results_file)
-    total_epochs = len(trials)
+    total_epochs = len(epochs)
 
-    trials = _hyperopt_filter_trials(trials, filteroptions)
+    epochs = _hyperopt_filter_epochs(epochs, filteroptions)
-    trials_epochs = len(trials)
+    filtered_epochs = len(epochs)
 
-    if n > trials_epochs:
+    if n > filtered_epochs:
         raise OperationalException(
-            f"The index of the epoch to show should be less than {trials_epochs + 1}.")
+            f"The index of the epoch to show should be less than {filtered_epochs + 1}.")
-    if n < -trials_epochs:
+    if n < -filtered_epochs:
         raise OperationalException(
-            f"The index of the epoch to show should be greater than {-trials_epochs - 1}.")
+            f"The index of the epoch to show should be greater than {-filtered_epochs - 1}.")
 
     # Translate epoch index from human-readable format to pythonic
     if n > 0:
         n -= 1
 
-    if trials:
+    if epochs:
-        val = trials[n]
+        val = epochs[n]
         Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,
                                      header_str="Epoch details")
 
 
-def _hyperopt_filter_trials(trials: List, filteroptions: dict) -> List:
+def _hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
     """
     Filter our items from the list of hyperopt results
     """
     if filteroptions['only_best']:
-        trials = [x for x in trials if x['is_best']]
+        epochs = [x for x in epochs if x['is_best']]
     if filteroptions['only_profitable']:
-        trials = [x for x in trials if x['results_metrics']['profit'] > 0]
+        epochs = [x for x in epochs if x['results_metrics']['profit'] > 0]
     if filteroptions['filter_min_trades'] > 0:
-        trials = [
+        epochs = [
-            x for x in trials
+            x for x in epochs
             if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
         ]
     if filteroptions['filter_max_trades'] > 0:
-        trials = [
+        epochs = [
-            x for x in trials
+            x for x in epochs
             if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
         ]
     if filteroptions['filter_min_avg_time'] is not None:
-        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
-        trials = [
+        epochs = [
-            x for x in trials
+            x for x in epochs
             if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
         ]
     if filteroptions['filter_max_avg_time'] is not None:
-        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
-        trials = [
+        epochs = [
-            x for x in trials
+            x for x in epochs
             if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
         ]
     if filteroptions['filter_min_avg_profit'] is not None:
-        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
-        trials = [
+        epochs = [
-            x for x in trials
+            x for x in epochs
-            if x['results_metrics']['avg_profit']
+            if x['results_metrics']['avg_profit'] > filteroptions['filter_min_avg_profit']
-            > filteroptions['filter_min_avg_profit']
         ]
     if filteroptions['filter_max_avg_profit'] is not None:
-        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
-        trials = [
+        epochs = [
-            x for x in trials
+            x for x in epochs
-            if x['results_metrics']['avg_profit']
+            if x['results_metrics']['avg_profit'] < filteroptions['filter_max_avg_profit']
-            < filteroptions['filter_max_avg_profit']
         ]
     if filteroptions['filter_min_total_profit'] is not None:
-        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
-        trials = [
+        epochs = [
-            x for x in trials
+            x for x in epochs
             if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
         ]
     if filteroptions['filter_max_total_profit'] is not None:
-        trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
+        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
-        trials = [
+        epochs = [
-            x for x in trials
+            x for x in epochs
             if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
         ]
 
-    logger.info(f"{len(trials)} " +
+    logger.info(f"{len(epochs)} " +
                 ("best " if filteroptions['only_best'] else "") +
                 ("profitable " if filteroptions['only_profitable'] else "") +
                 "epochs found.")
 
-    return trials
+    return epochs

freqtrade/commands/list_commands.py

@@ -197,3 +197,30 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
                   args.get('list_pairs_print_json', False) or
                   args.get('print_csv', False)):
             print(f"{summary_str}.")
+
+
+def start_show_trades(args: Dict[str, Any]) -> None:
+    """
+    Show trades
+    """
+    from freqtrade.persistence import init, Trade
+    import json
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    if 'db_url' not in config:
+        raise OperationalException("--db-url is required for this command.")
+
+    logger.info(f'Using DB: "{config["db_url"]}"')
+    init(config['db_url'], clean_open_orders=False)
+    tfilter = []
+
+    if config.get('trade_ids'):
+        tfilter.append(Trade.id.in_(config['trade_ids']))
+
+    trades = Trade.get_trades(tfilter).all()
+    logger.info(f"Printing {len(trades)} Trades: ")
+    if config.get('print_json', False):
+        print(json.dumps([trade.to_json() for trade in trades], indent=4))
+    else:
+        for trade in trades:
+            print(trade)

freqtrade/commands/trade_commands.py

@@ -18,6 +18,9 @@ def start_trading(args: Dict[str, Any]) -> int:
     try:
         worker = Worker(args)
         worker.run()
+    except Exception as e:
+        logger.error(str(e))
+        logger.exception("Fatal exception!")
     except KeyboardInterrupt:
         logger.info('SIGINT received, aborting ...')
     finally:

freqtrade/configuration/configuration.py

@@ -196,6 +196,7 @@ class Configuration:
         if self.args.get('exportfilename'):
             self._args_to_config(config, argname='exportfilename',
                                  logstring='Storing backtest results to {} ...')
+            config['exportfilename'] = Path(config['exportfilename'])
         else:
             config['exportfilename'] = (config['user_data_dir']
                                         / 'backtest_results/backtest-result.json')
@@ -350,14 +351,21 @@ class Configuration:
         self._args_to_config(config, argname='indicators2',
                              logstring='Using indicators2: {}')
 
+        self._args_to_config(config, argname='trade_ids',
+                             logstring='Filtering on trade_ids: {}')
+
         self._args_to_config(config, argname='plot_limit',
                              logstring='Limiting plot to: {}')
+
         self._args_to_config(config, argname='trade_source',
                              logstring='Using trades from: {}')
 
         self._args_to_config(config, argname='erase',
                              logstring='Erase detected. Deleting existing data.')
 
+        self._args_to_config(config, argname='no_trades',
+                             logstring='Parameter --no-trades detected.')
+
         self._args_to_config(config, argname='timeframes',
                              logstring='timeframes --timeframes: {}')
 

freqtrade/configuration/deprecated_settings.py

@@ -58,29 +58,6 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
     process_deprecated_setting(config, 'ask_strategy', 'ignore_roi_if_buy_signal',
                                'experimental', 'ignore_roi_if_buy_signal')
 
-    if not config.get('pairlists') and not config.get('pairlists'):
-        config['pairlists'] = [{'method': 'StaticPairList'}]
-        logger.warning(
-            "DEPRECATED: "
-            "Pairlists must be defined explicitly in the future."
-            "Defaulting to StaticPairList for now.")
-
-    if config.get('pairlist', {}).get("method") == 'VolumePairList':
-        logger.warning(
-            "DEPRECATED: "
-            f"Using VolumePairList in pairlist is deprecated and must be moved to pairlists. "
-            "Please refer to the docs on configuration details")
-        pl = {'method': 'VolumePairList'}
-        pl.update(config.get('pairlist', {}).get('config'))
-        config['pairlists'].append(pl)
-
-    if config.get('pairlist', {}).get('config', {}).get('precision_filter'):
-        logger.warning(
-            "DEPRECATED: "
-            f"Using precision_filter setting is deprecated and has been replaced by"
-            "PrecisionFilter. Please refer to the docs on configuration details")
-        config['pairlists'].append({'method': 'PrecisionFilter'})
-
     if (config.get('edge', {}).get('enabled', False)
        and 'capital_available_percentage' in config.get('edge', {})):
         logger.warning(

freqtrade/configuration/directory_operations.py

@@ -33,8 +33,8 @@ def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
     :param create_dir: Create directory if it does not exist.
     :return: Path object containing the directory
     """
-    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "notebooks",
+    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "logs",
-                "plot", "strategies", ]
+                "notebooks", "plot", "strategies", ]
     folder = Path(directory)
     if not folder.is_dir():
         if create_dir:

freqtrade/configuration/load_config.py

@@ -1,13 +1,15 @@
 """
 This module contain functions to load the configuration file
 """
-import rapidjson
 import logging
+import re
 import sys
+from pathlib import Path
 from typing import Any, Dict
 
-from freqtrade.exceptions import OperationalException
+import rapidjson
 
+from freqtrade.exceptions import OperationalException
 
 logger = logging.getLogger(__name__)
 
@@ -15,6 +17,26 @@ logger = logging.getLogger(__name__)
 CONFIG_PARSE_MODE = rapidjson.PM_COMMENTS | rapidjson.PM_TRAILING_COMMAS
 
 
+def log_config_error_range(path: str, errmsg: str) -> str:
+    """
+    Parses configuration file and prints range around error
+    """
+    if path != '-':
+        offsetlist = re.findall(r'(?<=Parse\serror\sat\soffset\s)\d+', errmsg)
+        if offsetlist:
+            offset = int(offsetlist[0])
+            text = Path(path).read_text()
+            # Fetch an offset of 80 characters around the error line
+            subtext = text[offset-min(80, offset):offset+80]
+            segments = subtext.split('\n')
+            if len(segments) > 3:
+                # Remove first and last lines, to avoid odd truncations
+                return '\n'.join(segments[1:-1])
+            else:
+                return subtext
+    return ''
+
+
 def load_config_file(path: str) -> Dict[str, Any]:
     """
     Loads a config file from the given path
@@ -29,5 +51,12 @@ def load_config_file(path: str) -> Dict[str, Any]:
         raise OperationalException(
             f'Config file "{path}" not found!'
             ' Please create a config file or check whether it exists.')
+    except rapidjson.JSONDecodeError as e:
+        err_range = log_config_error_range(path, str(e))
+        raise OperationalException(
+            f'{e}\n'
+            f'Please verify the following segment of your configuration:\n{err_range}'
+            if err_range else 'Please verify your configuration file for syntax errors.'
+        )
 
     return config

freqtrade/configuration/timerange.py

@@ -45,7 +45,7 @@ class TimeRange:
         """
         Adjust startts by <startup_candles> candles.
         Applies only if no startup-candles have been available.
-        :param timeframe_secs: Ticker timeframe in seconds e.g. `timeframe_to_seconds('5m')`
+        :param timeframe_secs: Timeframe in seconds e.g. `timeframe_to_seconds('5m')`
         :param startup_candles: Number of candles to move start-date forward
         :param min_date: Minimum data date loaded. Key kriterium to decide if start-time
                          has to be moved

freqtrade/constants.py

@@ -19,11 +19,14 @@ ORDERBOOK_SIDES = ['ask', 'bid']
 ORDERTYPE_POSSIBILITIES = ['limit', 'market']
 ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
 AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
-                       'PrecisionFilter', 'PriceFilter', 'SpreadFilter']
+                       'PrecisionFilter', 'PriceFilter', 'ShuffleFilter', 'SpreadFilter']
 AVAILABLE_DATAHANDLERS = ['json', 'jsongz']
 DRY_RUN_WALLET = 1000
 MATH_CLOSE_PREC = 1e-14  # Precision used for float comparisons
 DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
+# Don't modify sequence of DEFAULT_TRADES_COLUMNS
+# it has wide consequences for stored trades files
+DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost']
 
 USERPATH_HYPEROPTS = 'hyperopts'
 USERPATH_STRATEGIES = 'strategies'
@@ -85,6 +88,7 @@ CONF_SCHEMA = {
         'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
         'dry_run': {'type': 'boolean'},
         'dry_run_wallet': {'type': 'number', 'default': DRY_RUN_WALLET},
+        'cancel_open_orders_on_exit': {'type': 'boolean', 'default': False},
         'process_only_new_candles': {'type': 'boolean'},
         'minimal_roi': {
             'type': 'object',
@@ -318,3 +322,10 @@ SCHEMA_MINIMAL_REQUIRED = [
     'dataformat_ohlcv',
     'dataformat_trades',
 ]
+
+CANCEL_REASON = {
+    "TIMEOUT": "cancelled due to timeout",
+    "PARTIALLY_FILLED": "partially filled - keeping order open",
+    "ALL_CANCELLED": "cancelled (all unfilled and partially filled open orders cancelled)",
+    "CANCELLED_ON_EXCHANGE": "cancelled on exchange",
+}

freqtrade/data/btanalysis.py

@@ -111,7 +111,7 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
                             t.calc_profit(), t.calc_profit_ratio(),
                             t.open_rate, t.close_rate, t.amount,
                             (round((t.close_date.timestamp() - t.open_date.timestamp()) / 60, 2)
-                                if t.close_date else None),
+                             if t.close_date else None),
                             t.sell_reason,
                             t.fee_open, t.fee_close,
                             t.open_rate_requested,
@@ -129,39 +129,56 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
     return trades
 
 
-def load_trades(source: str, db_url: str, exportfilename: str) -> pd.DataFrame:
+def load_trades(source: str, db_url: str, exportfilename: Path,
+                no_trades: bool = False) -> pd.DataFrame:
     """
     Based on configuration option "trade_source":
     * loads data from DB (using `db_url`)
     * loads data from backtestfile (using `exportfilename`)
+    :param source: "DB" or "file" - specify source to load from
+    :param db_url: sqlalchemy formatted url to a database
+    :param exportfilename: Json file generated by backtesting
+    :param no_trades: Skip using trades, only return backtesting data columns
+    :return: DataFrame containing trades
     """
+    if no_trades:
+        df = pd.DataFrame(columns=BT_DATA_COLUMNS)
+        return df
+
     if source == "DB":
         return load_trades_from_db(db_url)
     elif source == "file":
-        return load_backtest_data(Path(exportfilename))
+        return load_backtest_data(exportfilename)
 
 
-def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> pd.DataFrame:
+def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
+                             date_index=False) -> pd.DataFrame:
     """
     Compare trades and backtested pair DataFrames to get trades performed on backtested period
     :return: the DataFrame of a trades of period
     """
-    trades = trades.loc[(trades['open_time'] >= dataframe.iloc[0]['date']) &
+    if date_index:
-                        (trades['close_time'] <= dataframe.iloc[-1]['date'])]
+        trades_start = dataframe.index[0]
+        trades_stop = dataframe.index[-1]
+    else:
+        trades_start = dataframe.iloc[0]['date']
+        trades_stop = dataframe.iloc[-1]['date']
+    trades = trades.loc[(trades['open_time'] >= trades_start) &
+                        (trades['close_time'] <= trades_stop)]
     return trades
 
 
-def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame],
+def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
-                              column: str = "close") -> pd.DataFrame:
+                                 column: str = "close") -> pd.DataFrame:
     """
     Combine multiple dataframes "column"
-    :param tickers: Dict of Dataframes, dict key should be pair.
+    :param data: Dict of Dataframes, dict key should be pair.
     :param column: Column in the original dataframes to use
     :return: DataFrame with the column renamed to the dict key, and a column
         named mean, containing the mean of all pairs.
     """
-    df_comb = pd.concat([tickers[pair].set_index('date').rename(
+    df_comb = pd.concat([data[pair].set_index('date').rename(
-        {column: pair}, axis=1)[pair] for pair in tickers], axis=1)
+        {column: pair}, axis=1)[pair] for pair in data], axis=1)
 
     df_comb['mean'] = df_comb.mean(axis=1)
 
@@ -203,13 +220,15 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time'
     """
     if len(trades) == 0:
         raise ValueError("Trade dataframe empty.")
-    profit_results = trades.sort_values(date_col)
+    profit_results = trades.sort_values(date_col).reset_index(drop=True)
     max_drawdown_df = pd.DataFrame()
     max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
     max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
     max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
 
-    high_date = profit_results.loc[max_drawdown_df['high_value'].idxmax(), date_col]
+    idxmin = max_drawdown_df['drawdown'].idxmin()
-    low_date = profit_results.loc[max_drawdown_df['drawdown'].idxmin(), date_col]
+    if idxmin == 0:
-
+        raise ValueError("No losing trade, therefore no drawdown.")
+    high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
+    low_date = profit_results.loc[idxmin, date_col]
     return abs(min(max_drawdown_df['drawdown'])), high_date, low_date

freqtrade/data/converter.py

@@ -1,24 +1,27 @@
 """
 Functions to convert data from one format to another
 """
+import itertools
 import logging
 from datetime import datetime, timezone
-from typing import Any, Dict
+from operator import itemgetter
+from typing import Any, Dict, List
 
 import pandas as pd
 from pandas import DataFrame, to_datetime
 
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
+from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS,
+                                 DEFAULT_TRADES_COLUMNS)
 
 logger = logging.getLogger(__name__)
 
 
-def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
+def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
-                           fill_missing: bool = True,
+                       fill_missing: bool = True, drop_incomplete: bool = True) -> DataFrame:
-                           drop_incomplete: bool = True) -> DataFrame:
     """
-    Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
+    Converts a list with candle (OHLCV) data (in format returned by ccxt.fetch_ohlcv)
-    :param ticker: ticker list, as returned by exchange.async_get_candle_history
+    to a Dataframe
+    :param ohlcv: list with candle (OHLCV) data, as returned by exchange.async_get_candle_history
     :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
     :param pair: Pair this data is for (used to warn if fillup was necessary)
     :param fill_missing: fill up missing candles with 0 candles
@@ -26,21 +29,18 @@ def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
     :param drop_incomplete: Drop the last candle of the dataframe, assuming it's incomplete
     :return: DataFrame
     """
-    logger.debug("Parsing tickerlist to dataframe")
+    logger.debug(f"Converting candle (OHLCV) data to dataframe for pair {pair}.")
     cols = DEFAULT_DATAFRAME_COLUMNS
-    frame = DataFrame(ticker, columns=cols)
+    df = DataFrame(ohlcv, columns=cols)
 
-    frame['date'] = to_datetime(frame['date'],
+    df['date'] = to_datetime(df['date'], unit='ms', utc=True, infer_datetime_format=True)
-                                unit='ms',
-                                utc=True,
-                                infer_datetime_format=True)
 
-    # Some exchanges return int values for volume and even for ohlc.
+    # Some exchanges return int values for Volume and even for OHLC.
     # Convert them since TA-LIB indicators used in the strategy assume floats
     # and fail with exception...
-    frame = frame.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
+    df = df.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float',
-                                'volume': 'float'})
+                          'volume': 'float'})
-    return clean_ohlcv_dataframe(frame, timeframe, pair,
+    return clean_ohlcv_dataframe(df, timeframe, pair,
                                  fill_missing=fill_missing,
                                  drop_incomplete=drop_incomplete)
 
@@ -49,11 +49,11 @@ def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
                           fill_missing: bool = True,
                           drop_incomplete: bool = True) -> DataFrame:
     """
-    Clense a ohlcv dataframe by
+    Clense a OHLCV dataframe by
       * Grouping it by date (removes duplicate tics)
       * dropping last candles if requested
       * Filling up missing data (if requested)
-    :param data: DataFrame containing ohlcv data.
+    :param data: DataFrame containing candle (OHLCV) data.
     :param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
     :param pair: Pair this data is for (used to warn if fillup was necessary)
     :param fill_missing: fill up missing candles with 0 candles
@@ -88,16 +88,16 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
     """
     from freqtrade.exchange import timeframe_to_minutes
 
-    ohlc_dict = {
+    ohlcv_dict = {
         'open': 'first',
         'high': 'max',
         'low': 'min',
         'close': 'last',
         'volume': 'sum'
     }
-    ticker_minutes = timeframe_to_minutes(timeframe)
+    timeframe_minutes = timeframe_to_minutes(timeframe)
     # Resample to create "NAN" values
-    df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)
+    df = dataframe.resample(f'{timeframe_minutes}min', on='date').agg(ohlcv_dict)
 
     # Forwardfill close for missing columns
     df['close'] = df['close'].fillna(method='ffill')
@@ -157,22 +157,43 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
     return frame
 
 
-def trades_to_ohlcv(trades: list, timeframe: str) -> DataFrame:
+def trades_remove_duplicates(trades: List[List]) -> List[List]:
     """
-    Converts trades list to ohlcv list
+    Removes duplicates from the trades list.
+    Uses itertools.groupby to avoid converting to pandas.
+    Tests show it as being pretty efficient on lists of 4M Lists.
+    :param trades: List of Lists with constants.DEFAULT_TRADES_COLUMNS as columns
+    :return: same format as above, but with duplicates removed
+    """
+    return [i for i, _ in itertools.groupby(sorted(trades, key=itemgetter(0)))]
+
+
+def trades_dict_to_list(trades: List[Dict]) -> List[List]:
+    """
+    Convert fetch_trades result into a List (to be more memory efficient).
+    :param trades: List of trades, as returned by ccxt.fetch_trades.
+    :return: List of Lists, with constants.DEFAULT_TRADES_COLUMNS as columns
+    """
+    return [[t[col] for col in DEFAULT_TRADES_COLUMNS] for t in trades]
+
+
+def trades_to_ohlcv(trades: List, timeframe: str) -> DataFrame:
+    """
+    Converts trades list to OHLCV list
     TODO: This should get a dedicated test
     :param trades: List of trades, as returned by ccxt.fetch_trades.
-    :param timeframe: Ticker timeframe to resample data to
+    :param timeframe: Timeframe to resample data to
-    :return: ohlcv Dataframe.
+    :return: OHLCV Dataframe.
     """
     from freqtrade.exchange import timeframe_to_minutes
-    ticker_minutes = timeframe_to_minutes(timeframe)
+    timeframe_minutes = timeframe_to_minutes(timeframe)
-    df = pd.DataFrame(trades)
+    df = pd.DataFrame(trades, columns=DEFAULT_TRADES_COLUMNS)
-    df['datetime'] = pd.to_datetime(df['datetime'])
+    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms',
-    df = df.set_index('datetime')
+                                     utc=True,)
+    df = df.set_index('timestamp')
 
-    df_new = df['price'].resample(f'{ticker_minutes}min').ohlc()
+    df_new = df['price'].resample(f'{timeframe_minutes}min').ohlc()
-    df_new['volume'] = df['amount'].resample(f'{ticker_minutes}min').sum()
+    df_new['volume'] = df['amount'].resample(f'{timeframe_minutes}min').sum()
     df_new['date'] = df_new.index
     # Drop 0 volume rows
     df_new = df_new.dropna()
@@ -206,7 +227,7 @@ def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to:
 
 def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to: str, erase: bool):
     """
-    Convert ohlcv from one format to another format.
+    Convert OHLCV from one format to another
     :param config: Config dictionary
     :param convert_from: Source format
     :param convert_to: Target format
@@ -216,7 +237,7 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
     src = get_datahandler(config['datadir'], convert_from)
     trg = get_datahandler(config['datadir'], convert_to)
     timeframes = config.get('timeframes', [config.get('ticker_interval')])
-    logger.info(f"Converting OHLCV for timeframe {timeframes}")
+    logger.info(f"Converting candle (OHLCV) for timeframe {timeframes}")
 
     if 'pairs' not in config:
         config['pairs'] = []
@@ -224,7 +245,7 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
         for timeframe in timeframes:
             config['pairs'].extend(src.ohlcv_get_pairs(config['datadir'],
                                                        timeframe))
-    logger.info(f"Converting OHLCV for {config['pairs']}")
+    logger.info(f"Converting candle (OHLCV) data for {config['pairs']}")
 
     for timeframe in timeframes:
         for pair in config['pairs']:

freqtrade/data/dataprovider.py

@@ -1,30 +1,34 @@
 """
 Dataprovider
 Responsible to provide data to the bot
-including Klines, tickers, historic data
+including ticker and orderbook data, live and historical candle (OHLCV) data
 Common Interface for bot and strategy to access data.
 """
 import logging
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional
 
 from pandas import DataFrame
 
 from freqtrade.data.history import load_pair_history
+from freqtrade.exceptions import DependencyException, OperationalException
 from freqtrade.exchange import Exchange
 from freqtrade.state import RunMode
+from freqtrade.typing import ListPairsWithTimeframes
+
 
 logger = logging.getLogger(__name__)
 
 
 class DataProvider:
 
-    def __init__(self, config: dict, exchange: Exchange) -> None:
+    def __init__(self, config: dict, exchange: Exchange, pairlists=None) -> None:
         self._config = config
         self._exchange = exchange
+        self._pairlists = pairlists
 
     def refresh(self,
-                pairlist: List[Tuple[str, str]],
+                pairlist: ListPairsWithTimeframes,
-                helping_pairs: List[Tuple[str, str]] = None) -> None:
+                helping_pairs: ListPairsWithTimeframes = None) -> None:
         """
         Refresh data, called with each cycle
         """
@@ -34,7 +38,7 @@ class DataProvider:
             self._exchange.refresh_latest_ohlcv(pairlist)
 
     @property
-    def available_pairs(self) -> List[Tuple[str, str]]:
+    def available_pairs(self) -> ListPairsWithTimeframes:
         """
         Return a list of tuples containing (pair, timeframe) for which data is currently cached.
         Should be whitelist + open trades.
@@ -43,10 +47,10 @@ class DataProvider:
 
     def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
         """
-        Get ohlcv data for the given pair as DataFrame
+        Get candle (OHLCV) data for the given pair as DataFrame
         Please use the `available_pairs` method to verify which pairs are currently cached.
         :param pair: pair to get the data for
-        :param timeframe: Ticker timeframe to get data for
+        :param timeframe: Timeframe to get data for
         :param copy: copy dataframe before returning if True.
                      Use False only for read-only operations (where the dataframe is not modified)
         """
@@ -58,7 +62,7 @@ class DataProvider:
 
     def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
         """
-        Get stored historic ohlcv data
+        Get stored historical candle (OHLCV) data
         :param pair: pair to get the data for
         :param timeframe: timeframe to get data for
         """
@@ -69,17 +73,17 @@ class DataProvider:
 
     def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:
         """
-        Return pair ohlcv data, either live or cached historical -- depending
+        Return pair candle (OHLCV) data, either live or cached historical -- depending
         on the runmode.
         :param pair: pair to get the data for
         :param timeframe: timeframe to get data for
         :return: Dataframe for this pair
         """
         if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
-            # Get live ohlcv data.
+            # Get live OHLCV data.
             data = self.ohlcv(pair=pair, timeframe=timeframe)
         else:
-            # Get historic ohlcv data (cached on disk).
+            # Get historical OHLCV data (cached on disk).
             data = self.historic_ohlcv(pair=pair, timeframe=timeframe)
         if len(data) == 0:
             logger.warning(f"No data found for ({pair}, {timeframe}).")
@@ -95,10 +99,14 @@ class DataProvider:
 
     def ticker(self, pair: str):
         """
-        Return last ticker data
+        Return last ticker data from exchange
+        :param pair: Pair to get the data for
+        :return: Ticker dict from exchange or empty dict if ticker is not available for the pair
         """
-        # TODO: Implement me
+        try:
-        pass
+            return self._exchange.fetch_ticker(pair)
+        except DependencyException:
+            return {}
 
     def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
         """
@@ -116,3 +124,17 @@ class DataProvider:
         can be "live", "dry-run", "backtest", "edgecli", "hyperopt" or "other".
         """
         return RunMode(self._config.get('runmode', RunMode.OTHER))
+
+    def current_whitelist(self) -> List[str]:
+        """
+        fetch latest available whitelist.
+
+        Useful when you have a large whitelist and need to call each pair as an informative pair.
+        As available pairs does not show whitelist until after informative pairs have been cached.
+        :return: list of pairs in whitelist
+        """
+
+        if self._pairlists:
+            return self._pairlists.whitelist
+        else:
+            raise OperationalException("Dataprovider was not initialized with a pairlist provider.")

freqtrade/data/history/history_utils.py

@@ -9,10 +9,13 @@ from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
-from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
+from freqtrade.data.converter import (ohlcv_to_dataframe,
+                                      trades_remove_duplicates,
+                                      trades_to_ohlcv)
 from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import Exchange
+from freqtrade.misc import format_ms_time
 
 logger = logging.getLogger(__name__)
 
@@ -28,10 +31,10 @@ def load_pair_history(pair: str,
                       data_handler: IDataHandler = None,
                       ) -> DataFrame:
     """
-    Load cached ticker history for the given pair.
+    Load cached ohlcv history for the given pair.
 
     :param pair: Pair to load data for
-    :param timeframe: Ticker timeframe (e.g. "5m")
+    :param timeframe: Timeframe (e.g. "5m")
     :param datadir: Path to the data storage location.
     :param data_format: Format of the data. Ignored if data_handler is set.
     :param timerange: Limit data to be loaded to this timerange
@@ -63,10 +66,10 @@ def load_data(datadir: Path,
               data_format: str = 'json',
               ) -> Dict[str, DataFrame]:
     """
-    Load ticker history data for a list of pairs.
+    Load ohlcv history data for a list of pairs.
 
     :param datadir: Path to the data storage location.
-    :param timeframe: Ticker Timeframe (e.g. "5m")
+    :param timeframe: Timeframe (e.g. "5m")
     :param pairs: List of pairs to load
     :param timerange: Limit data to be loaded to this timerange
     :param fill_up_missing: Fill missing values with "No action"-candles
@@ -104,10 +107,10 @@ def refresh_data(datadir: Path,
                  timerange: Optional[TimeRange] = None,
                  ) -> None:
     """
-    Refresh ticker history data for a list of pairs.
+    Refresh ohlcv history data for a list of pairs.
 
     :param datadir: Path to the data storage location.
-    :param timeframe: Ticker Timeframe (e.g. "5m")
+    :param timeframe: Timeframe (e.g. "5m")
     :param pairs: List of pairs to load
     :param exchange: Exchange object
     :param timerange: Limit data to be loaded to this timerange
@@ -165,7 +168,7 @@ def _download_pair_history(datadir: Path,
     Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
 
     :param pair: pair to download
-    :param timeframe: Ticker Timeframe (e.g 5m)
+    :param timeframe: Timeframe (e.g "5m")
     :param timerange: range of time to download
     :return: bool with success state
     """
@@ -194,8 +197,8 @@ def _download_pair_history(datadir: Path,
                                                    days=-30).float_timestamp) * 1000
                                                )
         # TODO: Maybe move parsing to exchange class (?)
-        new_dataframe = parse_ticker_dataframe(new_data, timeframe, pair,
+        new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,
-                                               fill_missing=False, drop_incomplete=True)
+                                           fill_missing=False, drop_incomplete=True)
         if data.empty:
             data = new_dataframe
         else:
@@ -257,27 +260,40 @@ def _download_trades_history(exchange: Exchange,
     """
     try:
 
-        since = timerange.startts * 1000 if timerange and timerange.starttype == 'date' else None
+        since = timerange.startts * 1000 if \
+            (timerange and timerange.starttype == 'date') else int(arrow.utcnow().shift(
+                days=-30).float_timestamp) * 1000
 
         trades = data_handler.trades_load(pair)
 
-        from_id = trades[-1]['id'] if trades else None
+        # TradesList columns are defined in constants.DEFAULT_TRADES_COLUMNS
+        # DEFAULT_TRADES_COLUMNS: 0 -> timestamp
+        # DEFAULT_TRADES_COLUMNS: 1 -> id
 
-        logger.debug("Current Start: %s", trades[0]['datetime'] if trades else 'None')
+        from_id = trades[-1][1] if trades else None
-        logger.debug("Current End: %s", trades[-1]['datetime'] if trades else 'None')
+        if trades and since < trades[-1][0]:
+            # Reset since to the last available point
+            # - 5 seconds (to ensure we're getting all trades)
+            since = trades[-1][0] - (5 * 1000)
+            logger.info(f"Using last trade date -5s - Downloading trades for {pair} "
+                        f"since: {format_ms_time(since)}.")
+
+        logger.debug(f"Current Start: {format_ms_time(trades[0][0]) if trades else 'None'}")
+        logger.debug(f"Current End: {format_ms_time(trades[-1][0]) if trades else 'None'}")
+        logger.info(f"Current Amount of trades: {len(trades)}")
 
         # Default since_ms to 30 days if nothing is given
         new_trades = exchange.get_historic_trades(pair=pair,
-                                                  since=since if since else
+                                                  since=since,
-                                                  int(arrow.utcnow().shift(
-                                                      days=-30).float_timestamp) * 1000,
                                                   from_id=from_id,
                                                   )
         trades.extend(new_trades[1])
+        # Remove duplicates to make sure we're not storing data we don't need
+        trades = trades_remove_duplicates(trades)
         data_handler.trades_store(pair, data=trades)
 
-        logger.debug("New Start: %s", trades[0]['datetime'])
+        logger.debug(f"New Start: {format_ms_time(trades[0][0])}")
-        logger.debug("New End: %s", trades[-1]['datetime'])
+        logger.debug(f"New End: {format_ms_time(trades[-1][0])}")
         logger.info(f"New Amount of trades: {len(trades)}")
         return True
 
@@ -362,7 +378,7 @@ def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
     :param pair: pair used for log output.
     :param min_date: start-date of the data
     :param max_date: end-date of the data
-    :param timeframe_min: ticker Timeframe in minutes
+    :param timeframe_min: Timeframe in minutes
     """
     # total difference in minutes / timeframe-minutes
     expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_min)

freqtrade/data/history/idatahandler.py

@@ -8,16 +8,20 @@ from abc import ABC, abstractclassmethod, abstractmethod
 from copy import deepcopy
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Dict, List, Optional, Type
+from typing import List, Optional, Type
 
 from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange
-from freqtrade.data.converter import clean_ohlcv_dataframe, trim_dataframe
+from freqtrade.data.converter import (clean_ohlcv_dataframe,
+                                      trades_remove_duplicates, trim_dataframe)
 from freqtrade.exchange import timeframe_to_seconds
 
 logger = logging.getLogger(__name__)
 
+# Type for trades list
+TradeList = List[List]
+
 
 class IDataHandler(ABC):
 
@@ -55,7 +59,7 @@ class IDataHandler(ABC):
         Implements the loading and conversion to a Pandas dataframe.
         Timerange trimming and dataframe validation happens outside of this method.
         :param pair: Pair to load data
-        :param timeframe: Ticker timeframe (e.g. "5m")
+        :param timeframe: Timeframe (e.g. "5m")
         :param timerange: Limit data to be loaded to this timerange.
                         Optionally implemented by subclasses to avoid loading
                         all data where possible.
@@ -67,7 +71,7 @@ class IDataHandler(ABC):
         """
         Remove data for this pair
         :param pair: Delete data for this pair.
-        :param timeframe: Ticker timeframe (e.g. "5m")
+        :param timeframe: Timeframe (e.g. "5m")
         :return: True when deleted, false if file did not exist.
         """
 
@@ -89,23 +93,25 @@ class IDataHandler(ABC):
         """
 
     @abstractmethod
-    def trades_store(self, pair: str, data: List[Dict]) -> None:
+    def trades_store(self, pair: str, data: TradeList) -> None:
         """
         Store trades data (list of Dicts) to file
         :param pair: Pair - used for filename
-        :param data: List of Dicts containing trade data
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
         """
 
     @abstractmethod
-    def trades_append(self, pair: str, data: List[Dict]):
+    def trades_append(self, pair: str, data: TradeList):
         """
         Append data to existing files
         :param pair: Pair - used for filename
-        :param data: List of Dicts containing trade data
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
         """
 
     @abstractmethod
-    def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> List[Dict]:
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
         """
         Load a pair from file, either .json.gz or .json
         :param pair: Load trades for this pair
@@ -121,6 +127,16 @@ class IDataHandler(ABC):
         :return: True when deleted, false if file did not exist.
         """
 
+    def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+        """
+        Load a pair from file, either .json.gz or .json
+        Removes duplicates in the process.
+        :param pair: Load trades for this pair
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+        return trades_remove_duplicates(self._trades_load(pair, timerange=timerange))
+
     def ohlcv_load(self, pair, timeframe: str,
                    timerange: Optional[TimeRange] = None,
                    fill_missing: bool = True,
@@ -129,10 +145,10 @@ class IDataHandler(ABC):
                    warn_no_data: bool = True
                    ) -> DataFrame:
         """
-        Load cached ticker history for the given pair.
+        Load cached candle (OHLCV) data for the given pair.
 
         :param pair: Pair to load data for
-        :param timeframe: Ticker timeframe (e.g. "5m")
+        :param timeframe: Timeframe (e.g. "5m")
         :param timerange: Limit data to be loaded to this timerange
         :param fill_missing: Fill missing values with "No action"-candles
         :param drop_incomplete: Drop last candle assuming it may be incomplete.
@@ -147,12 +163,7 @@ class IDataHandler(ABC):
 
         pairdf = self._ohlcv_load(pair, timeframe,
                                   timerange=timerange_startup)
-        if pairdf.empty:
+        if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
-            if warn_no_data:
-                logger.warning(
-                    f'No history data for pair: "{pair}", timeframe: {timeframe}. '
-                    'Use `freqtrade download-data` to download the data'
-                )
             return pairdf
         else:
             enddate = pairdf.iloc[-1]['date']
@@ -160,13 +171,30 @@ class IDataHandler(ABC):
             if timerange_startup:
                 self._validate_pairdata(pair, pairdf, timerange_startup)
                 pairdf = trim_dataframe(pairdf, timerange_startup)
+                if self._check_empty_df(pairdf, pair, timeframe, warn_no_data):
+                    return pairdf
 
             # incomplete candles should only be dropped if we didn't trim the end beforehand.
-            return clean_ohlcv_dataframe(pairdf, timeframe,
+            pairdf = clean_ohlcv_dataframe(pairdf, timeframe,
-                                         pair=pair,
+                                           pair=pair,
-                                         fill_missing=fill_missing,
+                                           fill_missing=fill_missing,
-                                         drop_incomplete=(drop_incomplete and
+                                           drop_incomplete=(drop_incomplete and
-                                                          enddate == pairdf.iloc[-1]['date']))
+                                                            enddate == pairdf.iloc[-1]['date']))
+            self._check_empty_df(pairdf, pair, timeframe, warn_no_data)
+            return pairdf
+
+    def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str, warn_no_data: bool):
+        """
+        Warn on empty dataframe
+        """
+        if pairdf.empty:
+            if warn_no_data:
+                logger.warning(
+                    f'No history data for pair: "{pair}", timeframe: {timeframe}. '
+                    'Use `freqtrade download-data` to download the data'
+                )
+            return True
+        return False
 
     def _validate_pairdata(self, pair, pairdata: DataFrame, timerange: TimeRange):
         """

freqtrade/data/history/jsondatahandler.py

@@ -1,6 +1,7 @@
+import logging
 import re
 from pathlib import Path
-from typing import Dict, List, Optional
+from typing import List, Optional
 
 import numpy as np
 from pandas import DataFrame, read_json, to_datetime
@@ -8,8 +9,11 @@ from pandas import DataFrame, read_json, to_datetime
 from freqtrade import misc
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
+from freqtrade.data.converter import trades_dict_to_list
 
-from .idatahandler import IDataHandler
+from .idatahandler import IDataHandler, TradeList
+
+logger = logging.getLogger(__name__)
 
 
 class JsonDataHandler(IDataHandler):
@@ -60,7 +64,7 @@ class JsonDataHandler(IDataHandler):
         Implements the loading and conversion to a Pandas dataframe.
         Timerange trimming and dataframe validation happens outside of this method.
         :param pair: Pair to load data
-        :param timeframe: Ticker timeframe (e.g. "5m")
+        :param timeframe: Timeframe (e.g. "5m")
         :param timerange: Limit data to be loaded to this timerange.
                         Optionally implemented by subclasses to avoid loading
                         all data where possible.
@@ -83,7 +87,7 @@ class JsonDataHandler(IDataHandler):
         """
         Remove data for this pair
         :param pair: Delete data for this pair.
-        :param timeframe: Ticker timeframe (e.g. "5m")
+        :param timeframe: Timeframe (e.g. "5m")
         :return: True when deleted, false if file did not exist.
         """
         filename = self._pair_data_filename(self._datadir, pair, timeframe)
@@ -113,24 +117,26 @@ class JsonDataHandler(IDataHandler):
         # Check if regex found something and only return these results to avoid exceptions.
         return [match[0].replace('_', '/') for match in _tmp if match]
 
-    def trades_store(self, pair: str, data: List[Dict]) -> None:
+    def trades_store(self, pair: str, data: TradeList) -> None:
         """
         Store trades data (list of Dicts) to file
         :param pair: Pair - used for filename
-        :param data: List of Dicts containing trade data
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
         """
         filename = self._pair_trades_filename(self._datadir, pair)
         misc.file_dump_json(filename, data, is_zip=self._use_zip)
 
-    def trades_append(self, pair: str, data: List[Dict]):
+    def trades_append(self, pair: str, data: TradeList):
         """
         Append data to existing files
         :param pair: Pair - used for filename
-        :param data: List of Dicts containing trade data
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
         """
         raise NotImplementedError()
 
-    def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> List[Dict]:
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
         """
         Load a pair from file, either .json.gz or .json
         # TODO: respect timerange ...
@@ -140,9 +146,15 @@ class JsonDataHandler(IDataHandler):
         """
         filename = self._pair_trades_filename(self._datadir, pair)
         tradesdata = misc.file_load_json(filename)
+
         if not tradesdata:
             return []
 
+        if isinstance(tradesdata[0], dict):
+            # Convert trades dict to list
+            logger.info("Old trades format detected - converting")
+            tradesdata = trades_dict_to_list(tradesdata)
+            pass
         return tradesdata
 
     def trades_purge(self, pair: str) -> bool:

freqtrade/edge/edge_positioning.py

@@ -8,10 +8,10 @@ import numpy as np
 import utils_find_1st as utf1st
 from pandas import DataFrame
 
-from freqtrade import constants
 from freqtrade.configuration import TimeRange
-from freqtrade.data import history
+from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
 from freqtrade.exceptions import OperationalException
+from freqtrade.data.history import get_timerange, load_data, refresh_data
 from freqtrade.strategy.interface import SellType
 
 logger = logging.getLogger(__name__)
@@ -54,7 +54,7 @@ class Edge:
         if self.config['max_open_trades'] != float('inf'):
             logger.critical('max_open_trades should be -1 in config !')
 
-        if self.config['stake_amount'] != constants.UNLIMITED_STAKE_AMOUNT:
+        if self.config['stake_amount'] != UNLIMITED_STAKE_AMOUNT:
             raise OperationalException('Edge works only with unlimited stake amount')
 
         # Deprecated capital_available_percentage. Will use tradable_balance_ratio in the future.
@@ -96,7 +96,7 @@ class Edge:
         logger.info('Using local backtesting data (using whitelist in given config) ...')
 
         if self._refresh_pairs:
-            history.refresh_data(
+            refresh_data(
                 datadir=self.config['datadir'],
                 pairs=pairs,
                 exchange=self.exchange,
@@ -104,7 +104,7 @@ class Edge:
                 timerange=self._timerange,
             )
 
-        data = history.load_data(
+        data = load_data(
             datadir=self.config['datadir'],
             pairs=pairs,
             timeframe=self.strategy.ticker_interval,
@@ -119,10 +119,10 @@ class Edge:
             logger.critical("No data found. Edge is stopped ...")
             return False
 
-        preprocessed = self.strategy.tickerdata_to_dataframe(data)
+        preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
 
         # Print timeframe
-        min_date, max_date = history.get_timerange(preprocessed)
+        min_date, max_date = get_timerange(preprocessed)
         logger.info(
             'Measuring data from %s up to %s (%s days) ...',
             min_date.isoformat(),
@@ -137,10 +137,10 @@ class Edge:
             pair_data = pair_data.sort_values(by=['date'])
             pair_data = pair_data.reset_index(drop=True)
 
-            ticker_data = self.strategy.advise_sell(
+            df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
 
-            trades += self._find_trades_for_stoploss_range(ticker_data, pair, self._stoploss_range)
+            trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
 
         # If no trade found then exit
         if len(trades) == 0:
@@ -238,20 +238,9 @@ class Edge:
         :param result Dataframe
         :return: result Dataframe
         """
-
+        # We set stake amount to an arbitrary amount, as it doesn't change the calculation.
-        # stake and fees
+        # All returned values are relative, they are defined as ratios.
-        # stake = 0.015
-        # 0.05% is 0.0005
-        # fee = 0.001
-
-        # we set stake amount to an arbitrary amount.
-        # as it doesn't change the calculation.
-        # all returned values are relative.
-        # they are defined as ratios.
         stake = 0.015
-        fee = self.fee
-        open_fee = fee / 2
-        close_fee = fee / 2
 
         result['trade_duration'] = result['close_time'] - result['open_time']
 
@@ -262,12 +251,12 @@ class Edge:
 
         # Buy Price
         result['buy_vol'] = stake / result['open_rate']  # How many target are we buying
-        result['buy_fee'] = stake * open_fee
+        result['buy_fee'] = stake * self.fee
         result['buy_spend'] = stake + result['buy_fee']  # How much we're spending
 
         # Sell price
         result['sell_sum'] = result['buy_vol'] * result['close_rate']
-        result['sell_fee'] = result['sell_sum'] * close_fee
+        result['sell_fee'] = result['sell_sum'] * self.fee
         result['sell_take'] = result['sell_sum'] - result['sell_fee']
 
         # profit_ratio
@@ -317,7 +306,7 @@ class Edge:
         }
 
         # Group by (pair and stoploss) by applying above aggregator
-        df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
+        df = results.groupby(['pair', 'stoploss'])[['profit_abs', 'trade_duration']].agg(
             groupby_aggregator).reset_index(col_level=1)
 
         # Dropping level 0 as we don't need it
@@ -359,11 +348,11 @@ class Edge:
         # Returning a list of pairs in order of "expectancy"
         return final
 
-    def _find_trades_for_stoploss_range(self, ticker_data, pair, stoploss_range):
+    def _find_trades_for_stoploss_range(self, df, pair, stoploss_range):
-        buy_column = ticker_data['buy'].values
+        buy_column = df['buy'].values
-        sell_column = ticker_data['sell'].values
+        sell_column = df['sell'].values
-        date_column = ticker_data['date'].values
+        date_column = df['date'].values
-        ohlc_columns = ticker_data[['open', 'high', 'low', 'close']].values
+        ohlc_columns = df[['open', 'high', 'low', 'close']].values
 
         result: list = []
         for stoploss in stoploss_range:

freqtrade/exceptions.py

@@ -35,3 +35,10 @@ class TemporaryError(FreqtradeException):
     This could happen when an exchange is congested, unavailable, or the user
     has networking problems. Usually resolves itself after a time.
     """
+
+
+class StrategyError(FreqtradeException):
+    """
+    Errors with custom user-code deteced.
+    Usually caused by errors in the strategy.
+    """

freqtrade/exchange/binance.py

@@ -72,7 +72,7 @@ class Binance(Exchange):
             rate = self.price_to_precision(pair, rate)
 
             order = self._api.create_order(symbol=pair, type=ordertype, side='sell',
-                                           amount=amount, price=stop_price, params=params)
+                                           amount=amount, price=rate, params=params)
             logger.info('stoploss limit order added for %s. '
                         'stop price: %s. limit: %s', pair, stop_price, rate)
             return order

freqtrade/exchange/common.py

@@ -1,6 +1,6 @@
 import logging
 
-from freqtrade.exceptions import DependencyException, TemporaryError
+from freqtrade.exceptions import TemporaryError
 
 logger = logging.getLogger(__name__)
 
@@ -93,7 +93,7 @@ def retrier_async(f):
         count = kwargs.pop('count', API_RETRY_COUNT)
         try:
             return await f(*args, **kwargs)
-        except (TemporaryError, DependencyException) as ex:
+        except TemporaryError as ex:
             logger.warning('%s() returned exception: "%s"', f.__name__, ex)
             if count > 0:
                 count -= 1
@@ -111,7 +111,7 @@ def retrier(f):
         count = kwargs.pop('count', API_RETRY_COUNT)
         try:
             return f(*args, **kwargs)
-        except (TemporaryError, DependencyException) as ex:
+        except TemporaryError as ex:
             logger.warning('%s() returned exception: "%s"', f.__name__, ex)
             if count > 0:
                 count -= 1

freqtrade/exchange/exchange.py

@@ -18,12 +18,12 @@ from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE,
                                             TRUNCATE, decimal_to_precision)
 from pandas import DataFrame
 
-from freqtrade.data.converter import parse_ticker_dataframe
+from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
 from freqtrade.exceptions import (DependencyException, InvalidOrderException,
                                   OperationalException, TemporaryError)
 from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
-from freqtrade.misc import deep_merge_dicts
+from freqtrade.misc import deep_merge_dicts, safe_value_fallback
-
+from freqtrade.typing import ListPairsWithTimeframes
 
 CcxtModuleType = Any
 
@@ -351,7 +351,7 @@ class Exchange:
 
     def validate_timeframes(self, timeframe: Optional[str]) -> None:
         """
-        Checks if ticker interval from config is a supported timeframe on the exchange
+        Check if timeframe from config is a supported timeframe on the exchange
         """
         if not hasattr(self._api, "timeframes") or self._api.timeframes is None:
             # If timeframes attribute is missing (or is None), the exchange probably
@@ -364,11 +364,10 @@ class Exchange:
 
         if timeframe and (timeframe not in self.timeframes):
             raise OperationalException(
-                f"Invalid ticker interval '{timeframe}'. This exchange supports: {self.timeframes}")
+                f"Invalid timeframe '{timeframe}'. This exchange supports: {self.timeframes}")
 
         if timeframe and timeframe_to_minutes(timeframe) < 1:
-            raise OperationalException(
+            raise OperationalException("Timeframes < 1m are currently not supported by Freqtrade.")
-                f"Timeframes < 1m are currently not supported by Freqtrade.")
 
     def validate_ordertypes(self, order_types: Dict) -> None:
         """
@@ -452,6 +451,17 @@ class Exchange:
                 price = ceil(big_price) / pow(10, symbol_prec)
         return price
 
+    def price_get_one_pip(self, pair: str, price: float) -> float:
+        """
+        Get's the "1 pip" value for this pair.
+        Used in PriceFilter to calculate the 1pip movements.
+        """
+        precision = self.markets[pair]['precision']['price']
+        if self.precisionMode == TICK_SIZE:
+            return precision
+        else:
+            return 1 / pow(10, precision)
+
     def dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
                       rate: float, params: Dict = {}) -> Dict[str, Any]:
         order_id = f'dry_run_{side}_{randint(0, 10**6)}'
@@ -461,26 +471,31 @@ class Exchange:
             'pair': pair,
             'price': rate,
             'amount': _amount,
-            "cost": _amount * rate,
+            'cost': _amount * rate,
             'type': ordertype,
             'side': side,
             'remaining': _amount,
             'datetime': arrow.utcnow().isoformat(),
             'status': "closed" if ordertype == "market" else "open",
             'fee': None,
-            "info": {}
+            'info': {}
         }
-        self._store_dry_order(dry_order)
+        self._store_dry_order(dry_order, pair)
         # Copy order and close it - so the returned order is open unless it's a market order
         return dry_order
 
-    def _store_dry_order(self, dry_order: Dict) -> None:
+    def _store_dry_order(self, dry_order: Dict, pair: str) -> None:
         closed_order = dry_order.copy()
-        if closed_order["type"] in ["market", "limit"]:
+        if closed_order['type'] in ["market", "limit"]:
             closed_order.update({
-                "status": "closed",
+                'status': 'closed',
-                "filled": closed_order["amount"],
+                'filled': closed_order['amount'],
-                "remaining": 0
+                'remaining': 0,
+                'fee': {
+                    'currency': self.get_pair_quote_currency(pair),
+                    'cost': dry_order['cost'] * self.get_fee(pair),
+                    'rate': self.get_fee(pair)
+                }
             })
         if closed_order["type"] in ["stop_loss_limit"]:
             closed_order["info"].update({"stopPrice": closed_order["price"]})
@@ -599,7 +614,7 @@ class Exchange:
             return self._api.fetch_tickers()
         except ccxt.NotSupported as e:
             raise OperationalException(
-                f'Exchange {self._api.name} does not support fetching tickers in batch.'
+                f'Exchange {self._api.name} does not support fetching tickers in batch. '
                 f'Message: {e}') from e
         except (ccxt.NetworkError, ccxt.ExchangeError) as e:
             raise TemporaryError(
@@ -623,13 +638,13 @@ class Exchange:
     def get_historic_ohlcv(self, pair: str, timeframe: str,
                            since_ms: int) -> List:
         """
-        Gets candle history using asyncio and returns the list of candles.
+        Get candle history using asyncio and returns the list of candles.
-        Handles all async doing.
+        Handles all async work for this.
-        Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
+        Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
         :param pair: Pair to download
-        :param timeframe: Ticker Timeframe to get
+        :param timeframe: Timeframe to get data for
         :param since_ms: Timestamp in milliseconds to get history from
-        :returns List of tickers
+        :returns List with candle (OHLCV) data
         """
         return asyncio.get_event_loop().run_until_complete(
             self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
@@ -649,26 +664,27 @@ class Exchange:
             pair, timeframe, since) for since in
             range(since_ms, arrow.utcnow().timestamp * 1000, one_call)]
 
-        tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
+        results = await asyncio.gather(*input_coroutines, return_exceptions=True)
 
-        # Combine tickers
+        # Combine gathered results
         data: List = []
-        for p, timeframe, ticker in tickers:
+        for p, timeframe, res in results:
             if p == pair:
-                data.extend(ticker)
+                data.extend(res)
         # Sort data again after extending the result - above calls return in "async order"
         data = sorted(data, key=lambda x: x[0])
-        logger.info("downloaded %s with length %s.", pair, len(data))
+        logger.info("Downloaded data for %s with length %s.", pair, len(data))
         return data
 
-    def refresh_latest_ohlcv(self, pair_list: List[Tuple[str, str]]) -> List[Tuple[str, List]]:
+    def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes) -> List[Tuple[str, List]]:
         """
-        Refresh in-memory ohlcv asynchronously and set `_klines` with the result
+        Refresh in-memory OHLCV asynchronously and set `_klines` with the result
         Loops asynchronously over pair_list and downloads all pairs async (semi-parallel).
+        Only used in the dataprovider.refresh() method.
         :param pair_list: List of 2 element tuples containing pair, interval to refresh
-        :return: Returns a List of ticker-dataframes.
+        :return: TODO: return value is only used in the tests, get rid of it
         """
-        logger.debug("Refreshing ohlcv data for %d pairs", len(pair_list))
+        logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
 
         input_coroutines = []
 
@@ -679,15 +695,15 @@ class Exchange:
                 input_coroutines.append(self._async_get_candle_history(pair, timeframe))
             else:
                 logger.debug(
-                    "Using cached ohlcv data for pair %s, timeframe %s ...",
+                    "Using cached candle (OHLCV) data for pair %s, timeframe %s ...",
                     pair, timeframe
                 )
 
-        tickers = asyncio.get_event_loop().run_until_complete(
+        results = asyncio.get_event_loop().run_until_complete(
             asyncio.gather(*input_coroutines, return_exceptions=True))
 
         # handle caching
-        for res in tickers:
+        for res in results:
             if isinstance(res, Exception):
                 logger.warning("Async code raised an exception: %s", res.__class__.__name__)
                 continue
@@ -698,13 +714,14 @@ class Exchange:
             if ticks:
                 self._pairs_last_refresh_time[(pair, timeframe)] = ticks[-1][0] // 1000
             # keeping parsed dataframe in cache
-            self._klines[(pair, timeframe)] = parse_ticker_dataframe(
+            self._klines[(pair, timeframe)] = ohlcv_to_dataframe(
                 ticks, timeframe, pair=pair, fill_missing=True,
                 drop_incomplete=self._ohlcv_partial_candle)
-        return tickers
+
+        return results
 
     def _now_is_time_to_refresh(self, pair: str, timeframe: str) -> bool:
-        # Calculating ticker interval in seconds
+        # Timeframe in seconds
         interval_in_sec = timeframe_to_seconds(timeframe)
 
         return not ((self._pairs_last_refresh_time.get((pair, timeframe), 0)
@@ -714,11 +731,11 @@ class Exchange:
     async def _async_get_candle_history(self, pair: str, timeframe: str,
                                         since_ms: Optional[int] = None) -> Tuple[str, str, List]:
         """
-        Asynchronously gets candle histories using fetch_ohlcv
+        Asynchronously get candle history data using fetch_ohlcv
         returns tuple: (pair, timeframe, ohlcv_list)
         """
         try:
-            # fetch ohlcv asynchronously
+            # Fetch OHLCV asynchronously
             s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
             logger.debug(
                 "Fetching pair %s, interval %s, since %s %s...",
@@ -728,9 +745,9 @@ class Exchange:
             data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
                                                      since=since_ms)
 
-            # Because some exchange sort Tickers ASC and other DESC.
+            # Some exchanges sort OHLCV in ASC order and others in DESC.
-            # Ex: Bittrex returns a list of tickers ASC (oldest first, newest last)
+            # Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
-            # when GDAX returns a list of tickers DESC (newest first, oldest last)
+            # while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
             # Only sort if necessary to save computing time
             try:
                 if data and data[0][0] > data[-1][0]:
@@ -743,19 +760,20 @@ class Exchange:
 
         except ccxt.NotSupported as e:
             raise OperationalException(
-                f'Exchange {self._api.name} does not support fetching historical candlestick data.'
+                f'Exchange {self._api.name} does not support fetching historical '
-                f'Message: {e}') from e
+                f'candle (OHLCV) data. Message: {e}') from e
         except (ccxt.NetworkError, ccxt.ExchangeError) as e:
-            raise TemporaryError(f'Could not load ticker history for pair {pair} due to '
+            raise TemporaryError(f'Could not fetch historical candle (OHLCV) data '
-                                 f'{e.__class__.__name__}. Message: {e}') from e
+                                 f'for pair {pair} due to {e.__class__.__name__}. '
+                                 f'Message: {e}') from e
         except ccxt.BaseError as e:
-            raise OperationalException(f'Could not fetch ticker data for pair {pair}. '
+            raise OperationalException(f'Could not fetch historical candle (OHLCV) data '
-                                       f'Msg: {e}') from e
+                                       f'for pair {pair}. Message: {e}') from e
 
     @retrier_async
     async def _async_fetch_trades(self, pair: str,
                                   since: Optional[int] = None,
-                                  params: Optional[dict] = None) -> List[Dict]:
+                                  params: Optional[dict] = None) -> List[List]:
         """
         Asyncronously gets trade history using fetch_trades.
         Handles exchange errors, does one call to the exchange.
@@ -775,7 +793,7 @@ class Exchange:
                     '(' + arrow.get(since // 1000).isoformat() + ') ' if since is not None else ''
                 )
                 trades = await self._api_async.fetch_trades(pair, since=since, limit=1000)
-            return trades
+            return trades_dict_to_list(trades)
         except ccxt.NotSupported as e:
             raise OperationalException(
                 f'Exchange {self._api.name} does not support fetching historical trade data.'
@@ -789,7 +807,7 @@ class Exchange:
     async def _async_get_trade_history_id(self, pair: str,
                                           until: int,
                                           since: Optional[int] = None,
-                                          from_id: Optional[str] = None) -> Tuple[str, List[Dict]]:
+                                          from_id: Optional[str] = None) -> Tuple[str, List[List]]:
         """
         Asyncronously gets trade history using fetch_trades
         use this when exchange uses id-based iteration (check `self._trades_pagination`)
@@ -800,7 +818,7 @@ class Exchange:
         returns tuple: (pair, trades-list)
         """
 
-        trades: List[Dict] = []
+        trades: List[List] = []
 
         if not from_id:
             # Fetch first elements using timebased method to get an ID to paginate on
@@ -809,7 +827,9 @@ class Exchange:
             # e.g. Binance returns the "last 1000" candles within a 1h time interval
             # - so we will miss the first trades.
             t = await self._async_fetch_trades(pair, since=since)
-            from_id = t[-1]['id']
+            # DEFAULT_TRADES_COLUMNS: 0 -> timestamp
+            # DEFAULT_TRADES_COLUMNS: 1 -> id
+            from_id = t[-1][1]
             trades.extend(t[:-1])
         while True:
             t = await self._async_fetch_trades(pair,
@@ -817,21 +837,21 @@ class Exchange:
             if len(t):
                 # Skip last id since its the key for the next call
                 trades.extend(t[:-1])
-                if from_id == t[-1]['id'] or t[-1]['timestamp'] > until:
+                if from_id == t[-1][1] or t[-1][0] > until:
                     logger.debug(f"Stopping because from_id did not change. "
-                                 f"Reached {t[-1]['timestamp']} > {until}")
+                                 f"Reached {t[-1][0]} > {until}")
                     # Reached the end of the defined-download period - add last trade as well.
                     trades.extend(t[-1:])
                     break
 
-                from_id = t[-1]['id']
+                from_id = t[-1][1]
             else:
                 break
 
         return (pair, trades)
 
     async def _async_get_trade_history_time(self, pair: str, until: int,
-                                            since: Optional[int] = None) -> Tuple[str, List]:
+                                            since: Optional[int] = None) -> Tuple[str, List[List]]:
         """
         Asyncronously gets trade history using fetch_trades,
         when the exchange uses time-based iteration (check `self._trades_pagination`)
@@ -841,16 +861,18 @@ class Exchange:
         returns tuple: (pair, trades-list)
         """
 
-        trades: List[Dict] = []
+        trades: List[List] = []
+        # DEFAULT_TRADES_COLUMNS: 0 -> timestamp
+        # DEFAULT_TRADES_COLUMNS: 1 -> id
         while True:
             t = await self._async_fetch_trades(pair, since=since)
             if len(t):
-                since = t[-1]['timestamp']
+                since = t[-1][1]
                 trades.extend(t)
                 # Reached the end of the defined-download period
-                if until and t[-1]['timestamp'] > until:
+                if until and t[-1][0] > until:
                     logger.debug(
-                        f"Stopping because until was reached. {t[-1]['timestamp']} > {until}")
+                        f"Stopping because until was reached. {t[-1][0]} > {until}")
                     break
             else:
                 break
@@ -860,7 +882,7 @@ class Exchange:
     async def _async_get_trade_history(self, pair: str,
                                        since: Optional[int] = None,
                                        until: Optional[int] = None,
-                                       from_id: Optional[str] = None) -> Tuple[str, List[Dict]]:
+                                       from_id: Optional[str] = None) -> Tuple[str, List[List]]:
         """
         Async wrapper handling downloading trades using either time or id based methods.
         """
@@ -883,14 +905,14 @@ class Exchange:
                             until: Optional[int] = None,
                             from_id: Optional[str] = None) -> Tuple[str, List]:
         """
-        Gets candle history using asyncio and returns the list of candles.
+        Get trade history data using asyncio.
-        Handles all async doing.
+        Handles all async work and returns the list of candles.
-        Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
+        Async over one pair, assuming we get `self._ohlcv_candle_limit` candles per call.
         :param pair: Pair to download
         :param since: Timestamp in milliseconds to get history from
         :param until: Timestamp in milliseconds. Defaults to current timestamp if not defined.
         :param from_id: Download data starting with ID (if id is known)
-        :returns List of tickers
+        :returns List of trade data
         """
         if not self.exchange_has("fetchTrades"):
             raise OperationalException("This exchange does not suport downloading Trades.")
@@ -899,10 +921,18 @@ class Exchange:
             self._async_get_trade_history(pair=pair, since=since,
                                           until=until, from_id=from_id))
 
+    def check_order_canceled_empty(self, order: Dict) -> bool:
+        """
+        Verify if an order has been cancelled without being partially filled
+        :param order: Order dict as returned from get_order()
+        :return: True if order has been cancelled without being filled, False otherwise.
+        """
+        return order.get('status') in ('closed', 'canceled') and order.get('filled') == 0.0
+
     @retrier
-    def cancel_order(self, order_id: str, pair: str) -> None:
+    def cancel_order(self, order_id: str, pair: str) -> Dict:
         if self._config['dry_run']:
-            return
+            return {}
 
         try:
             return self._api.cancel_order(order_id, pair)
@@ -915,6 +945,37 @@ class Exchange:
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
+    def is_cancel_order_result_suitable(self, corder) -> bool:
+        if not isinstance(corder, dict):
+            return False
+
+        required = ('fee', 'status', 'amount')
+        return all(k in corder for k in required)
+
+    def cancel_order_with_result(self, order_id: str, pair: str, amount: float) -> Dict:
+        """
+        Cancel order returning a result.
+        Creates a fake result if cancel order returns a non-usable result
+        and get_order does not work (certain exchanges don't return cancelled orders)
+        :param order_id: Orderid to cancel
+        :param pair: Pair corresponding to order_id
+        :param amount: Amount to use for fake response
+        :return: Result from either cancel_order if usable, or fetch_order
+        """
+        try:
+            corder = self.cancel_order(order_id, pair)
+            if self.is_cancel_order_result_suitable(corder):
+                return corder
+        except InvalidOrderException:
+            logger.warning(f"Could not cancel order {order_id}.")
+        try:
+            order = self.get_order(order_id, pair)
+        except InvalidOrderException:
+            logger.warning(f"Could not fetch cancelled order {order_id}.")
+            order = {'fee': {}, 'status': 'canceled', 'amount': amount, 'info': {}}
+
+        return order
+
     @retrier
     def get_order(self, order_id: str, pair: str) -> Dict:
         if self._config['dry_run']:
@@ -988,9 +1049,9 @@ class Exchange:
 
             return matched_trades
 
-        except ccxt.NetworkError as e:
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
             raise TemporaryError(
-                f'Could not get trades due to networking error. Message: {e}') from e
+                f'Could not get trades due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
@@ -1010,6 +1071,61 @@ class Exchange:
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
+    @staticmethod
+    def order_has_fee(order: Dict) -> bool:
+        """
+        Verifies if the passed in order dict has the needed keys to extract fees,
+        and that these keys (currency, cost) are not empty.
+        :param order: Order or trade (one trade) dict
+        :return: True if the fee substructure contains currency and cost, false otherwise
+        """
+        if not isinstance(order, dict):
+            return False
+        return ('fee' in order and order['fee'] is not None
+                and (order['fee'].keys() >= {'currency', 'cost'})
+                and order['fee']['currency'] is not None
+                and order['fee']['cost'] is not None
+                )
+
+    def calculate_fee_rate(self, order: Dict) -> Optional[float]:
+        """
+        Calculate fee rate if it's not given by the exchange.
+        :param order: Order or trade (one trade) dict
+        """
+        if order['fee'].get('rate') is not None:
+            return order['fee'].get('rate')
+        fee_curr = order['fee']['currency']
+        # Calculate fee based on order details
+        if fee_curr in self.get_pair_base_currency(order['symbol']):
+            # Base currency - divide by amount
+            return round(
+                order['fee']['cost'] / safe_value_fallback(order, order, 'filled', 'amount'), 8)
+        elif fee_curr in self.get_pair_quote_currency(order['symbol']):
+            # Quote currency - divide by cost
+            return round(order['fee']['cost'] / order['cost'], 8)
+        else:
+            # If Fee currency is a different currency
+            try:
+                comb = self.get_valid_pair_combination(fee_curr, self._config['stake_currency'])
+                tick = self.fetch_ticker(comb)
+
+                fee_to_quote_rate = safe_value_fallback(tick, tick, 'last', 'ask')
+                return round((order['fee']['cost'] * fee_to_quote_rate) / order['cost'], 8)
+            except DependencyException:
+                return None
+
+    def extract_cost_curr_rate(self, order: Dict) -> Tuple[float, str, Optional[float]]:
+        """
+        Extract tuple of cost, currency, rate.
+        Requires order_has_fee to run first!
+        :param order: Order or trade (one trade) dict
+        :return: Tuple with cost, currency, rate of the given fee dict
+        """
+        return (order['fee']['cost'],
+                order['fee']['currency'],
+                self.calculate_fee_rate(order))
+        # calculate rate ? (order['fee']['cost'] / (order['amount'] * order['price']))
+
 
 def is_exchange_bad(exchange_name: str) -> bool:
     return exchange_name in BAD_EXCHANGES

freqtrade/exchange/kraken.py

@@ -7,7 +7,7 @@ import ccxt
 from freqtrade.exceptions import (DependencyException, InvalidOrderException,
                                   OperationalException, TemporaryError)
 from freqtrade.exchange import Exchange
-from freqtrade.exchange.exchange import retrier
+from freqtrade.exchange.common import retrier
 
 logger = logging.getLogger(__name__)
 

freqtrade/freqtradebot.py

@@ -7,7 +7,7 @@ import traceback
 from datetime import datetime
 from math import isclose
 from threading import Lock
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional
 
 import arrow
 from cachetools import TTLCache
@@ -20,12 +20,14 @@ from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
 from freqtrade.exceptions import DependencyException, InvalidOrderException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date
+from freqtrade.misc import safe_value_fallback
 from freqtrade.pairlist.pairlistmanager import PairListManager
 from freqtrade.persistence import Trade
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.rpc import RPCManager, RPCMessageType
 from freqtrade.state import State
 from freqtrade.strategy.interface import IStrategy, SellType
+from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
 from freqtrade.wallets import Wallets
 
 logger = logging.getLogger(__name__)
@@ -52,8 +54,11 @@ class FreqtradeBot:
         # Init objects
         self.config = config
 
-        self._sell_rate_cache = TTLCache(maxsize=100, ttl=5)
+        # Cache values for 1800 to avoid frequent polling of the exchange for prices
-        self._buy_rate_cache = TTLCache(maxsize=100, ttl=5)
+        # Caching only applies to RPC methods, so prices for open trades are still
+        # refreshed once every iteration.
+        self._sell_rate_cache = TTLCache(maxsize=100, ttl=1800)
+        self._buy_rate_cache = TTLCache(maxsize=100, ttl=1800)
 
         self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
 
@@ -66,20 +71,20 @@ class FreqtradeBot:
 
         self.wallets = Wallets(self.config, self.exchange)
 
-        self.dataprovider = DataProvider(self.config, self.exchange)
+        self.pairlists = PairListManager(self.exchange, self.config)
+
+        self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists)
 
         # Attach Dataprovider to Strategy baseclass
         IStrategy.dp = self.dataprovider
         # Attach Wallets to Strategy baseclass
         IStrategy.wallets = self.wallets
 
-        self.pairlists = PairListManager(self.exchange, self.config)
-
         # Initializing Edge only if enabled
         self.edge = Edge(self.config, self.exchange, self.strategy) if \
             self.config.get('edge', {}).get('enabled', False) else None
 
-        self.active_pair_whitelist = self._refresh_whitelist()
+        self.active_pair_whitelist = self._refresh_active_whitelist()
 
         # Set initial bot state from config
         initial_state = self.config.get('initial_state')
@@ -111,6 +116,9 @@ class FreqtradeBot:
         """
         logger.info('Cleaning up modules ...')
 
+        if self.config['cancel_open_orders_on_exit']:
+            self.cancel_all_open_orders()
+
         self.rpc.cleanup()
         persistence.cleanup()
 
@@ -137,12 +145,16 @@ class FreqtradeBot:
         # Query trades from persistence layer
         trades = Trade.get_open_trades()
 
-        self.active_pair_whitelist = self._refresh_whitelist(trades)
+        self.active_pair_whitelist = self._refresh_active_whitelist(trades)
 
         # Refreshing candles
-        self.dataprovider.refresh(self._create_pair_whitelist(self.active_pair_whitelist),
+        self.dataprovider.refresh(self.pairlists.create_pair_list(self.active_pair_whitelist),
                                   self.strategy.informative_pairs())
 
+        with self._sell_lock:
+            # Check and handle any timed out open orders
+            self.check_handle_timedout()
+
         # Protect from collisions with forcesell.
         # Without this, freqtrade my try to recreate stoploss_on_exchange orders
         # while selling is in process, since telegram messages arrive in an different thread.
@@ -154,13 +166,19 @@ class FreqtradeBot:
         if self.get_free_open_trades():
             self.enter_positions()
 
-        # Check and handle any timed out open orders
-        self.check_handle_timedout()
         Trade.session.flush()
 
-    def _refresh_whitelist(self, trades: List[Trade] = []) -> List[str]:
+    def process_stopped(self) -> None:
         """
-        Refresh whitelist from pairlist or edge and extend it with trades.
+        Close all orders that were left open
+        """
+        if self.config['cancel_open_orders_on_exit']:
+            self.cancel_all_open_orders()
+
+    def _refresh_active_whitelist(self, trades: List[Trade] = []) -> List[str]:
+        """
+        Refresh active whitelist from pairlist or edge and extend it with
+        pairs that have open trades.
         """
         # Refresh whitelist
         self.pairlists.refresh_pairlist()
@@ -172,17 +190,11 @@ class FreqtradeBot:
             _whitelist = self.edge.adjust(_whitelist)
 
         if trades:
-            # Extend active-pair whitelist with pairs from open trades
+            # Extend active-pair whitelist with pairs of open trades
-            # It ensures that tickers are downloaded for open trades
+            # It ensures that candle (OHLCV) data are downloaded for open trades as well
             _whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
         return _whitelist
 
-    def _create_pair_whitelist(self, pairs: List[str]) -> List[Tuple[str, str]]:
-        """
-        Create pair-whitelist tuple with (pair, ticker_interval)
-        """
-        return [(pair, self.config['ticker_interval']) for pair in pairs]
-
     def get_free_open_trades(self):
         """
         Return the number of free open trades slots or 0 if
@@ -395,15 +407,17 @@ class FreqtradeBot:
             logger.info(f"Pair {pair} is currently locked.")
             return False
 
+        # get_free_open_trades is checked before create_trade is called
+        # but it is still used here to prevent opening too many trades within one iteration
+        if not self.get_free_open_trades():
+            logger.debug(f"Can't open a new trade for {pair}: max number of trades is reached.")
+            return False
+
         # running get_signal on historical data fetched
         dataframe = self.dataprovider.ohlcv(pair, self.strategy.ticker_interval)
         (buy, sell) = self.strategy.get_signal(pair, self.strategy.ticker_interval, dataframe)
 
         if buy and not sell:
-            if not self.get_free_open_trades():
-                logger.debug("Can't open a new trade: max number of trades is reached.")
-                return False
-
             stake_amount = self.get_trade_stake_amount(pair)
             if not stake_amount:
                 logger.debug(f"Stake amount is 0, ignoring possible trade for {pair}.")
@@ -598,14 +612,13 @@ class FreqtradeBot:
         trades_closed = 0
         for trade in trades:
             try:
-                self.update_trade_state(trade)
 
                 if (self.strategy.order_types.get('stoploss_on_exchange') and
                         self.handle_stoploss_on_exchange(trade)):
                     trades_closed += 1
                     continue
                 # Check if we can sell our current pair
-                if trade.open_order_id is None and self.handle_trade(trade):
+                if trade.open_order_id is None and trade.is_open and self.handle_trade(trade):
                     trades_closed += 1
 
             except DependencyException as exception:
@@ -628,7 +641,7 @@ class FreqtradeBot:
 
     def get_sell_rate(self, pair: str, refresh: bool) -> float:
         """
-        Get sell rate - either using get-ticker bid or first bid based on orderbook
+        Get sell rate - either using ticker bid or first bid based on orderbook
         The orderbook portion is only used for rpc messaging, which would otherwise fail
         for BitMex (has no bid/ask in fetch_ticker)
         or remain static in any other case since it's not updating.
@@ -747,7 +760,7 @@ class FreqtradeBot:
         # We check if stoploss order is fulfilled
         if stoploss_order and stoploss_order['status'] == 'closed':
             trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
-            trade.update(stoploss_order)
+            self.update_trade_state(trade, stoploss_order, sl_order=True)
             # Lock pair for one candle to prevent immediate rebuys
             self.strategy.lock_pair(trade.pair,
                                     timeframe_to_next_date(self.config['ticker_interval']))
@@ -858,105 +871,134 @@ class FreqtradeBot:
                     continue
                 order = self.exchange.get_order(trade.open_order_id, trade.pair)
             except (RequestException, DependencyException, InvalidOrderException):
-                logger.info(
+                logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
-                    'Cannot query order for %s due to %s',
-                    trade,
-                    traceback.format_exc())
                 continue
 
-            # Check if trade is still actually open
+            fully_cancelled = self.update_trade_state(trade, order)
-            if float(order.get('remaining', 0.0)) == 0.0:
-                self.wallets.update()
-                continue
 
-            if ((order['side'] == 'buy' and order['status'] == 'canceled')
+            if (order['side'] == 'buy' and (order['status'] == 'open' or fully_cancelled) and (
-                    or (self._check_timed_out('buy', order))):
+                    fully_cancelled
-                self.handle_timedout_limit_buy(trade, order)
+                    or self._check_timed_out('buy', order)
-                self.wallets.update()
+                    or strategy_safe_wrapper(self.strategy.check_buy_timeout,
-                order_type = self.strategy.order_types['buy']
+                                             default_retval=False)(pair=trade.pair,
-                self._notify_buy_cancel(trade, order_type)
+                                                                   trade=trade,
+                                                                   order=order))):
+                self.handle_cancel_buy(trade, order, constants.CANCEL_REASON['TIMEOUT'])
 
-            elif ((order['side'] == 'sell' and order['status'] == 'canceled')
+            elif (order['side'] == 'sell' and (order['status'] == 'open' or fully_cancelled) and (
-                  or (self._check_timed_out('sell', order))):
+                  fully_cancelled
-                self.handle_timedout_limit_sell(trade, order)
+                  or self._check_timed_out('sell', order)
-                self.wallets.update()
+                  or strategy_safe_wrapper(self.strategy.check_sell_timeout,
-                order_type = self.strategy.order_types['sell']
+                                           default_retval=False)(pair=trade.pair,
-                self._notify_sell_cancel(trade, order_type)
+                                                                 trade=trade,
+                                                                 order=order))):
+                self.handle_cancel_sell(trade, order, constants.CANCEL_REASON['TIMEOUT'])
 
-    def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
+    def cancel_all_open_orders(self) -> None:
         """
-        Buy timeout - cancel order
+        Cancel all orders that are currently open
+        :return: None
+        """
+
+        for trade in Trade.get_open_order_trades():
+            try:
+                order = self.exchange.get_order(trade.open_order_id, trade.pair)
+            except (DependencyException, InvalidOrderException):
+                logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
+                continue
+
+            if order['side'] == 'buy':
+                self.handle_cancel_buy(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
+
+            elif order['side'] == 'sell':
+                self.handle_cancel_sell(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
+
+    def handle_cancel_buy(self, trade: Trade, order: Dict, reason: str) -> bool:
+        """
+        Buy cancel - cancel order
         :return: True if order was fully cancelled
         """
-        if order['status'] != 'canceled':
+        was_trade_fully_canceled = False
-            reason = "cancelled due to timeout"
+
-            corder = self.exchange.cancel_order(trade.open_order_id, trade.pair)
+        # Cancelled orders may have the status of 'canceled' or 'closed'
-            logger.info('Buy order %s for %s.', reason, trade)
+        if order['status'] not in ('canceled', 'closed'):
+            reason = constants.CANCEL_REASON['TIMEOUT']
+            corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
+                                                            trade.amount)
         else:
             # Order was cancelled already, so we can reuse the existing dict
             corder = order
-            reason = "cancelled on exchange"
+            reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
-            logger.info('Buy order %s for %s.', reason, trade)
 
-        if corder.get('remaining', order['remaining']) == order['amount']:
+        logger.info('Buy order %s for %s.', reason, trade)
+
+        # Using filled to determine the filled amount
+        filled_amount = safe_value_fallback(corder, order, 'filled', 'filled')
+
+        if isclose(filled_amount, 0.0, abs_tol=constants.MATH_CLOSE_PREC):
+            logger.info('Buy order fully cancelled. Removing %s from database.', trade)
             # if trade is not partially completed, just delete the trade
             Trade.session.delete(trade)
             Trade.session.flush()
-            return True
+            was_trade_fully_canceled = True
+        else:
+            # if trade is partially complete, edit the stake details for the trade
+            # and close the order
+            # cancel_order may not contain the full order dict, so we need to fallback
+            # to the order dict aquired before cancelling.
+            # we need to fall back to the values from order if corder does not contain these keys.
+            trade.amount = filled_amount
+            trade.stake_amount = trade.amount * trade.open_rate
+            self.update_trade_state(trade, corder, trade.amount)
 
-        # if trade is partially complete, edit the stake details for the trade
+            trade.open_order_id = None
-        # and close the order
+            logger.info('Partial buy order timeout for %s.', trade)
-        # cancel_order may not contain the full order dict, so we need to fallback
+            self.rpc.send_msg({
-        # to the order dict aquired before cancelling.
+                'type': RPCMessageType.STATUS_NOTIFICATION,
-        # we need to fall back to the values from order if corder does not contain these keys.
+                'status': f'Remaining buy order for {trade.pair} cancelled due to timeout'
-        trade.amount = order['amount'] - corder.get('remaining', order['remaining'])
+            })
-        trade.stake_amount = trade.amount * trade.open_rate
-        # verify if fees were taken from amount to avoid problems during selling
-        try:
-            new_amount = self.get_real_amount(trade, corder if 'fee' in corder else order,
-                                              trade.amount)
-            if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
-                trade.amount = new_amount
-                # Fee was applied, so set to 0
-                trade.fee_open = 0
-                trade.recalc_open_trade_price()
-        except DependencyException as e:
-            logger.warning("Could not update trade amount: %s", e)
 
-        trade.open_order_id = None
+        self.wallets.update()
-        logger.info('Partial buy order timeout for %s.', trade)
+        self._notify_buy_cancel(trade, order_type=self.strategy.order_types['buy'])
-        self.rpc.send_msg({
+        return was_trade_fully_canceled
-            'type': RPCMessageType.STATUS_NOTIFICATION,
-            'status': f'Remaining buy order for {trade.pair} cancelled due to timeout'
-        })
-        return False
 
-    def handle_timedout_limit_sell(self, trade: Trade, order: Dict) -> bool:
+    def handle_cancel_sell(self, trade: Trade, order: Dict, reason: str) -> str:
         """
-        Sell timeout - cancel order and update trade
+        Sell cancel - cancel order and update trade
-        :return: True if order was fully cancelled
+        :return: Reason for cancel
         """
-        # if trade is not partially completed, just cancel the trade
+        # if trade is not partially completed, just cancel the order
-        if order['remaining'] == order['amount']:
+        if order['remaining'] == order['amount'] or order.get('filled') == 0.0:
-            if order["status"] != "canceled":
+            if not self.exchange.check_order_canceled_empty(order):
-                reason = "cancelled due to timeout"
+                try:
-                # if trade is not partially completed, just delete the trade
+                    # if trade is not partially completed, just delete the order
-                self.exchange.cancel_order(trade.open_order_id, trade.pair)
+                    self.exchange.cancel_order(trade.open_order_id, trade.pair)
+                except InvalidOrderException:
+                    logger.exception(f"Could not cancel sell order {trade.open_order_id}")
+                    return 'error cancelling order'
                 logger.info('Sell order %s for %s.', reason, trade)
             else:
-                reason = "cancelled on exchange"
+                reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
                 logger.info('Sell order %s for %s.', reason, trade)
 
             trade.close_rate = None
+            trade.close_rate_requested = None
             trade.close_profit = None
+            trade.close_profit_abs = None
             trade.close_date = None
             trade.is_open = True
             trade.open_order_id = None
+        else:
+            # TODO: figure out how to handle partially complete sell orders
+            reason = constants.CANCEL_REASON['PARTIALLY_FILLED']
 
-            return True
+        self.wallets.update()
-
+        self._notify_sell_cancel(
-        # TODO: figure out how to handle partially complete sell orders
+            trade,
-        return False
+            order_type=self.strategy.order_types['sell'],
+            reason=reason
+        )
+        return reason
 
     def _safe_sell_amount(self, pair: str, amount: float) -> float:
         """
@@ -977,7 +1019,7 @@ class FreqtradeBot:
         if wallet_amount >= amount:
             return amount
         elif wallet_amount > amount * 0.98:
-            logger.info(f"{pair} - Falling back to wallet-amount.")
+            logger.info(f"{pair} - Falling back to wallet-amount {wallet_amount} -> {amount}.")
             return wallet_amount
         else:
             raise DependencyException(
@@ -1027,7 +1069,7 @@ class FreqtradeBot:
         trade.sell_reason = sell_reason.value
         # In case of market sell orders the order can be closed immediately
         if order.get('status', 'unknown') == 'closed':
-            trade.update(order)
+            self.update_trade_state(trade, order)
         Trade.session.flush()
 
         # Lock pair for one candle to prevent immediate rebuys
@@ -1043,7 +1085,7 @@ class FreqtradeBot:
         """
         profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
         profit_trade = trade.calc_profit(rate=profit_rate)
-        # Use cached ticker here - it was updated seconds ago.
+        # Use cached rates here - it was updated seconds ago.
         current_rate = self.get_sell_rate(trade.pair, False)
         profit_ratio = trade.calc_profit_ratio(profit_rate)
         gain = "profit" if profit_ratio > 0 else "loss"
@@ -1075,10 +1117,15 @@ class FreqtradeBot:
         # Send the message
         self.rpc.send_msg(msg)
 
-    def _notify_sell_cancel(self, trade: Trade, order_type: str) -> None:
+    def _notify_sell_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
         """
         Sends rpc notification when a sell cancel occured.
         """
+        if trade.sell_order_status == reason:
+            return
+        else:
+            trade.sell_order_status = reason
+
         profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
         profit_trade = trade.calc_profit(rate=profit_rate)
         current_rate = self.get_sell_rate(trade.pair, False)
@@ -1102,6 +1149,7 @@ class FreqtradeBot:
             'close_date': trade.close_date,
             'stake_currency': self.config['stake_currency'],
             'fiat_currency': self.config.get('fiat_display_currency', None),
+            'reason': reason,
         }
 
         if 'fiat_display_currency' in self.config:
@@ -1116,84 +1164,134 @@ class FreqtradeBot:
 # Common update trade state methods
 #
 
-    def update_trade_state(self, trade: Trade, action_order: dict = None) -> None:
+    def update_trade_state(self, trade: Trade, action_order: dict = None,
+                           order_amount: float = None, sl_order: bool = False) -> bool:
         """
         Checks trades with open orders and updates the amount if necessary
+        Handles closing both buy and sell orders.
+        :return: True if order has been cancelled without being filled partially, False otherwise
         """
         # Get order details for actual price per unit
         if trade.open_order_id:
-            # Update trade with order values
+            order_id = trade.open_order_id
-            logger.info('Found open order for %s', trade)
+        elif trade.stoploss_order_id and sl_order:
-            try:
+            order_id = trade.stoploss_order_id
-                order = action_order or self.exchange.get_order(trade.open_order_id, trade.pair)
+        else:
-            except InvalidOrderException as exception:
+            return False
-                logger.warning('Unable to fetch order %s: %s', trade.open_order_id, exception)
+        # Update trade with order values
-                return
+        logger.info('Found open order for %s', trade)
-            # Try update amount (binance-fix)
+        try:
-            try:
+            order = action_order or self.exchange.get_order(order_id, trade.pair)
-                new_amount = self.get_real_amount(trade, order)
+        except InvalidOrderException as exception:
-                if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
+            logger.warning('Unable to fetch order %s: %s', order_id, exception)
-                    order['amount'] = new_amount
+            return False
-                    # Fee was applied, so set to 0
+        # Try update amount (binance-fix)
-                    trade.fee_open = 0
+        try:
-                    trade.recalc_open_trade_price()
+            new_amount = self.get_real_amount(trade, order, order_amount)
+            if not isclose(order['amount'], new_amount, abs_tol=constants.MATH_CLOSE_PREC):
+                order['amount'] = new_amount
+                order.pop('filled', None)
+                trade.recalc_open_trade_price()
+        except DependencyException as exception:
+            logger.warning("Could not update trade amount: %s", exception)
 
-            except DependencyException as exception:
+        if self.exchange.check_order_canceled_empty(order):
-                logger.warning("Could not update trade amount: %s", exception)
+            # Trade has been cancelled on exchange
+            # Handling of this will happen in check_handle_timeout.
+            return True
+        trade.update(order)
 
-            trade.update(order)
+        # Updating wallets when order is closed
+        if not trade.is_open:
+            self.wallets.update()
+        return False
 
-            # Updating wallets when order is closed
+    def apply_fee_conditional(self, trade: Trade, trade_base_currency: str,
-            if not trade.is_open:
+                              amount: float, fee_abs: float) -> float:
-                self.wallets.update()
+        """
+        Applies the fee to amount (either from Order or from Trades).
+        Can eat into dust if more than the required asset is available.
+        """
+        self.wallets.update()
+        if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount:
+            # Eat into dust if we own more than base currency
+            logger.info(f"Fee amount for {trade} was in base currency - "
+                        f"Eating Fee {fee_abs} into dust.")
+        elif fee_abs != 0:
+            real_amount = self.exchange.amount_to_precision(trade.pair, amount - fee_abs)
+            logger.info(f"Applying fee on amount for {trade} "
+                        f"(from {amount} to {real_amount}).")
+            return real_amount
+        return amount
 
     def get_real_amount(self, trade: Trade, order: Dict, order_amount: float = None) -> float:
         """
-        Get real amount for the trade
+        Detect and update trade fee.
+        Calls trade.update_fee() uppon correct detection.
+        Returns modified amount if the fee was taken from the destination currency.
         Necessary for exchanges which charge fees in base currency (e.g. binance)
+        :return: identical (or new) amount for the trade
         """
+        # Init variables
         if order_amount is None:
             order_amount = order['amount']
         # Only run for closed orders
-        if trade.fee_open == 0 or order['status'] == 'open':
+        if trade.fee_updated(order.get('side', '')) or order['status'] == 'open':
             return order_amount
 
         trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
         # use fee from order-dict if possible
-        if ('fee' in order and order['fee'] is not None and
+        if self.exchange.order_has_fee(order):
-                (order['fee'].keys() >= {'currency', 'cost'})):
+            fee_cost, fee_currency, fee_rate = self.exchange.extract_cost_curr_rate(order)
-            if (order['fee']['currency'] is not None and
+            logger.info(f"Fee for Trade {trade} [{order.get('side')}]: "
-                    order['fee']['cost'] is not None and
+                        f"{fee_cost:.8g} {fee_currency} - rate: {fee_rate}")
-                    trade_base_currency == order['fee']['currency']):
-                new_amount = order_amount - order['fee']['cost']
-                logger.info("Applying fee on amount for %s (from %s to %s) from Order",
-                            trade, order['amount'], new_amount)
-                return new_amount
 
-        # Fallback to Trades
+            trade.update_fee(fee_cost, fee_currency, fee_rate, order.get('side', ''))
+            if trade_base_currency == fee_currency:
+                # Apply fee to amount
+                return self.apply_fee_conditional(trade, trade_base_currency,
+                                                  amount=order_amount, fee_abs=fee_cost)
+            return order_amount
+        return self.fee_detection_from_trades(trade, order, order_amount)
+
+    def fee_detection_from_trades(self, trade: Trade, order: Dict, order_amount: float) -> float:
+        """
+        fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee.
+        """
         trades = self.exchange.get_trades_for_order(trade.open_order_id, trade.pair,
                                                     trade.open_date)
 
         if len(trades) == 0:
             logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)
             return order_amount
+        fee_currency = None
         amount = 0
-        fee_abs = 0
+        fee_abs = 0.0
+        fee_cost = 0.0
+        trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
+        fee_rate_array: List[float] = []
         for exectrade in trades:
             amount += exectrade['amount']
-            if ("fee" in exectrade and exectrade['fee'] is not None and
+            if self.exchange.order_has_fee(exectrade):
-                    (exectrade['fee'].keys() >= {'currency', 'cost'})):
+                fee_cost_, fee_currency, fee_rate_ = self.exchange.extract_cost_curr_rate(exectrade)
+                fee_cost += fee_cost_
+                if fee_rate_ is not None:
+                    fee_rate_array.append(fee_rate_)
                 # only applies if fee is in quote currency!
-                if (exectrade['fee']['currency'] is not None and
+                if trade_base_currency == fee_currency:
-                        exectrade['fee']['cost'] is not None and
+                    fee_abs += fee_cost_
-                        trade_base_currency == exectrade['fee']['currency']):
+        # Ensure at least one trade was found:
-                    fee_abs += exectrade['fee']['cost']
+        if fee_currency:
+            # fee_rate should use mean
+            fee_rate = sum(fee_rate_array) / float(len(fee_rate_array)) if fee_rate_array else None
+            trade.update_fee(fee_cost, fee_currency, fee_rate, order.get('side', ''))
 
         if not isclose(amount, order_amount, abs_tol=constants.MATH_CLOSE_PREC):
             logger.warning(f"Amount {amount} does not match amount {trade.amount}")
             raise DependencyException("Half bought? Amounts don't match")
-        real_amount = amount - fee_abs
+
         if fee_abs != 0:
-            logger.info(f"Applying fee on amount for {trade} "
+            return self.apply_fee_conditional(trade, trade_base_currency,
-                        f"(from {order_amount} to {real_amount}) from Trades")
+                                              amount=amount, fee_abs=fee_abs)
-        return real_amount
+        else:
+            return amount

freqtrade/loggers.py

@@ -18,13 +18,13 @@ def _set_loggers(verbosity: int = 0) -> None:
     """
 
     logging.getLogger('requests').setLevel(
-            logging.INFO if verbosity <= 1 else logging.DEBUG
+        logging.INFO if verbosity <= 1 else logging.DEBUG
     )
     logging.getLogger("urllib3").setLevel(
-            logging.INFO if verbosity <= 1 else logging.DEBUG
+        logging.INFO if verbosity <= 1 else logging.DEBUG
     )
     logging.getLogger('ccxt.base.exchange').setLevel(
-            logging.INFO if verbosity <= 2 else logging.DEBUG
+        logging.INFO if verbosity <= 2 else logging.DEBUG
     )
     logging.getLogger('telegram').setLevel(logging.INFO)
 

freqtrade/misc.py

@@ -81,13 +81,13 @@ def file_load_json(file):
         gzipfile = file
     # Try gzip file first, otherwise regular json file.
     if gzipfile.is_file():
-        logger.debug('Loading ticker data from file %s', gzipfile)
+        logger.debug(f"Loading historical data from file {gzipfile}")
-        with gzip.open(gzipfile) as tickerdata:
+        with gzip.open(gzipfile) as datafile:
-            pairdata = json_load(tickerdata)
+            pairdata = json_load(datafile)
     elif file.is_file():
-        logger.debug('Loading ticker data from file %s', file)
+        logger.debug(f"Loading historical data from file {file}")
-        with open(file) as tickerdata:
+        with open(file) as datafile:
-            pairdata = json_load(tickerdata)
+            pairdata = json_load(datafile)
     else:
         return None
     return pairdata
@@ -134,6 +134,21 @@ def round_dict(d, n):
     return {k: (round(v, n) if isinstance(v, float) else v) for k, v in d.items()}
 
 
+def safe_value_fallback(dict1: dict, dict2: dict, key1: str, key2: str, default_value=None):
+    """
+    Search a value in dict1, return this if it's not None.
+    Fall back to dict2 - return key2 from dict2 if it's not None.
+    Else falls back to None.
+
+    """
+    if key1 in dict1 and dict1[key1] is not None:
+        return dict1[key1]
+    else:
+        if key2 in dict2 and dict2[key2] is not None:
+            return dict2[key2]
+    return default_value
+
+
 def plural(num: float, singular: str, plural: str = None) -> str:
     return singular if (num == 1 or num == -1) else plural or singular + 's'
 
@@ -148,3 +163,15 @@ def render_template(templatefile: str, arguments: dict = {}) -> str:
     )
     template = env.get_template(templatefile)
     return template.render(**arguments)
+
+
+def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
+                                  arguments: dict = {}) -> str:
+    """
+    Use templatefile if possible, otherwise fall back to templatefallbackfile
+    """
+    from jinja2.exceptions import TemplateNotFound
+    try:
+        return render_template(templatefile, arguments)
+    except TemplateNotFound:
+        return render_template(templatefallbackfile, arguments)

freqtrade/optimize/backtesting.py

@@ -6,8 +6,7 @@ This module contains the backtesting logic
 import logging
 from copy import deepcopy
 from datetime import datetime, timedelta
-from pathlib import Path
+from typing import Any, Dict, List, NamedTuple, Optional, Tuple
-from typing import Any, Dict, List, NamedTuple, Optional
 
 import arrow
 from pandas import DataFrame
@@ -19,10 +18,9 @@ from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
-from freqtrade.misc import file_dump_json
+from freqtrade.optimize.optimize_reports import (show_backtest_results,
-from freqtrade.optimize.optimize_reports import (
+                                                 store_backtest_result)
-    generate_text_table, generate_text_table_sell_reason,
+from freqtrade.pairlist.pairlistmanager import PairListManager
-    generate_text_table_strategy)
 from freqtrade.persistence import Trade
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.state import RunMode
@@ -66,10 +64,19 @@ class Backtesting:
         self.strategylist: List[IStrategy] = []
         self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
 
+        self.pairlists = PairListManager(self.exchange, self.config)
+        if 'VolumePairList' in self.pairlists.name_list:
+            raise OperationalException("VolumePairList not allowed for backtesting.")
+
+        self.pairlists.refresh_pairlist()
+
+        if len(self.pairlists.whitelist) == 0:
+            raise OperationalException("No pair in whitelist.")
+
         if config.get('fee'):
             self.fee = config['fee']
         else:
-            self.fee = self.exchange.get_fee(symbol=self.config['exchange']['pair_whitelist'][0])
+            self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
 
         if self.config.get('runmode') != RunMode.HYPEROPT:
             self.dataprovider = DataProvider(self.config, self.exchange)
@@ -88,8 +95,8 @@ class Backtesting:
             validate_config_consistency(self.config)
 
         if "ticker_interval" not in self.config:
-            raise OperationalException("Ticker-interval needs to be set in either configuration "
+            raise OperationalException("Timeframe (ticker interval) needs to be set in either "
-                                       "or as cli argument `--ticker-interval 5m`")
+                                       "configuration or as cli argument `--ticker-interval 5m`")
         self.timeframe = str(self.config.get('ticker_interval'))
         self.timeframe_min = timeframe_to_minutes(self.timeframe)
 
@@ -108,13 +115,13 @@ class Backtesting:
         # And the regular "stoploss" function would not apply to that case
         self.strategy.order_types['stoploss_on_exchange'] = False
 
-    def load_bt_data(self):
+    def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
         timerange = TimeRange.parse_timerange(None if self.config.get(
             'timerange') is None else str(self.config.get('timerange')))
 
         data = history.load_data(
             datadir=self.config['datadir'],
-            pairs=self.config['exchange']['pair_whitelist'],
+            pairs=self.pairlists.whitelist,
             timeframe=self.timeframe,
             timerange=timerange,
             startup_candles=self.required_startup,
@@ -134,49 +141,33 @@ class Backtesting:
 
         return data, timerange
 
-    def _store_backtest_result(self, recordfilename: Path, results: DataFrame,
+    def _get_ohlcv_as_lists(self, processed: Dict) -> Dict[str, DataFrame]:
-                               strategyname: Optional[str] = None) -> None:
-
-        records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
-                    t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
-                    t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
-                   for index, t in results.iterrows()]
-
-        if records:
-            if strategyname:
-                # Inject strategyname to filename
-                recordfilename = Path.joinpath(
-                    recordfilename.parent,
-                    f'{recordfilename.stem}-{strategyname}').with_suffix(recordfilename.suffix)
-            logger.info(f'Dumping backtest results to {recordfilename}')
-            file_dump_json(recordfilename, records)
-
-    def _get_ticker_list(self, processed: Dict) -> Dict[str, DataFrame]:
         """
-        Helper function to convert a processed tickerlist into a list for performance reasons.
+        Helper function to convert a processed dataframes into lists for performance reasons.
 
         Used by backtest() - so keep this optimized for performance.
         """
         headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
-        ticker: Dict = {}
+        data: Dict = {}
-        # Create ticker dict
+        # Create dict with data
         for pair, pair_data in processed.items():
             pair_data.loc[:, 'buy'] = 0  # cleanup from previous run
             pair_data.loc[:, 'sell'] = 0  # cleanup from previous run
 
-            ticker_data = self.strategy.advise_sell(
+            df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
 
-            # to avoid using data from future, we buy/sell with signal from previous candle
+            # To avoid using data from future, we use buy/sell signals shifted
-            ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
+            # from the previous candle
-            ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
+            df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
+            df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
 
-            ticker_data.drop(ticker_data.head(1).index, inplace=True)
+            df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
 
             # Convert from Pandas to list for performance reasons
             # (Looping Pandas is slow.)
-            ticker[pair] = [x for x in ticker_data.itertuples()]
+            data[pair] = [x for x in df_analyzed.itertuples()]
-        return ticker
+        return data
 
     def _get_close_rate(self, sell_row, trade: Trade, sell: SellCheckTuple,
                         trade_dur: int) -> float:
@@ -220,7 +211,7 @@ class Backtesting:
 
     def _get_sell_trade_entry(
             self, pair: str, buy_row: DataFrame,
-            partial_ticker: List, trade_count_lock: Dict,
+            partial_ohlcv: List, trade_count_lock: Dict,
             stake_amount: float, max_open_trades: int) -> Optional[BacktestResult]:
 
         trade = Trade(
@@ -235,7 +226,7 @@ class Backtesting:
         )
         logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
         # calculate win/lose forwards from buy point
-        for sell_row in partial_ticker:
+        for sell_row in partial_ohlcv:
             if max_open_trades > 0:
                 # Increase trade_count_lock for every iteration
                 trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
@@ -259,9 +250,9 @@ class Backtesting:
                                       close_rate=closerate,
                                       sell_reason=sell.sell_type
                                       )
-        if partial_ticker:
+        if partial_ohlcv:
             # no sell condition found - trade stil open at end of backtest period
-            sell_row = partial_ticker[-1]
+            sell_row = partial_ohlcv[-1]
             bt_res = BacktestResult(pair=pair,
                                     profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
                                     profit_abs=trade.calc_profit(rate=sell_row.open),
@@ -308,8 +299,9 @@ class Backtesting:
         trades = []
         trade_count_lock: Dict = {}
 
-        # Dict of ticker-lists for performance (looping lists is a lot faster than dataframes)
+        # Use dict of lists with data for performance
-        ticker: Dict = self._get_ticker_list(processed)
+        # (looping lists is a lot faster than pandas DataFrames)
+        data: Dict = self._get_ohlcv_as_lists(processed)
 
         lock_pair_until: Dict = {}
         # Indexes per pair, so some pairs are allowed to have a missing start.
@@ -319,12 +311,12 @@ class Backtesting:
         # Loop timerange and get candle for each pair at that point in time
         while tmp < end_date:
 
-            for i, pair in enumerate(ticker):
+            for i, pair in enumerate(data):
                 if pair not in indexes:
                     indexes[pair] = 0
 
                 try:
-                    row = ticker[pair][indexes[pair]]
+                    row = data[pair][indexes[pair]]
                 except IndexError:
                     # missing Data for one pair at the end.
                     # Warnings for this are shown during data loading
@@ -352,7 +344,7 @@ class Backtesting:
 
                 # since indexes has been incremented before, we need to go one step back to
                 # also check the buying candle for sell conditions.
-                trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]-1:],
+                trade_entry = self._get_sell_trade_entry(pair, row, data[pair][indexes[pair]-1:],
                                                          trade_count_lock, stake_amount,
                                                          max_open_trades)
 
@@ -395,7 +387,7 @@ class Backtesting:
             self._set_strategy(strat)
 
             # need to reprocess data every time to populate signals
-            preprocessed = self.strategy.tickerdata_to_dataframe(data)
+            preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
 
             # Trim startup period from analyzed dataframe
             for pair, df in preprocessed.items():
@@ -416,44 +408,7 @@ class Backtesting:
                 position_stacking=position_stacking,
             )
 
-        for strategy, results in all_results.items():
+        if self.config.get('export', False):
-
+            store_backtest_result(self.config['exportfilename'], all_results)
-            if self.config.get('export', False):
+        # Show backtest results
-                self._store_backtest_result(Path(self.config['exportfilename']), results,
+        show_backtest_results(self.config, data, all_results)
-                                            strategy if len(self.strategylist) > 1 else None)
-
-            print(f"Result for strategy {strategy}")
-            table = generate_text_table(data, stake_currency=self.config['stake_currency'],
-                                        max_open_trades=self.config['max_open_trades'],
-                                        results=results)
-            if isinstance(table, str):
-                print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
-            print(table)
-
-            table = generate_text_table_sell_reason(data,
-                                                    stake_currency=self.config['stake_currency'],
-                                                    max_open_trades=self.config['max_open_trades'],
-                                                    results=results)
-            if isinstance(table, str):
-                print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
-            print(table)
-
-            table = generate_text_table(data,
-                                        stake_currency=self.config['stake_currency'],
-                                        max_open_trades=self.config['max_open_trades'],
-                                        results=results.loc[results.open_at_end], skip_nan=True)
-            if isinstance(table, str):
-                print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
-            print(table)
-            if isinstance(table, str):
-                print('=' * len(table.splitlines()[0]))
-            print()
-        if len(all_results) > 1:
-            # Print Strategy summary table
-            table = generate_text_table_strategy(self.config['stake_currency'],
-                                                 self.config['max_open_trades'],
-                                                 all_results=all_results)
-            print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
-            print(table)
-            print('=' * len(table.splitlines()[0]))
-            print('\nFor more details, please look at the detail tables above')

freqtrade/optimize/hyperopt.py

@@ -7,7 +7,6 @@ This module contains the hyperopt logic
 import locale
 import logging
 import random
-import sys
 import warnings
 from math import ceil
 from collections import OrderedDict
@@ -18,10 +17,10 @@ from typing import Any, Dict, List, Optional
 
 import rapidjson
 from colorama import Fore, Style
-from colorama import init as colorama_init
 from joblib import (Parallel, cpu_count, delayed, dump, load,
                     wrap_non_picklable_objects)
 from pandas import DataFrame, json_normalize, isna
+import progressbar
 import tabulate
 from os import path
 import io
@@ -43,15 +42,16 @@ with warnings.catch_warnings():
     from skopt import Optimizer
     from skopt.space import Dimension
 
-
+progressbar.streams.wrap_stderr()
+progressbar.streams.wrap_stdout()
 logger = logging.getLogger(__name__)
 
 
 INITIAL_POINTS = 30
 
-# Keep no more than 2*SKOPT_MODELS_MAX_NUM models
+# Keep no more than SKOPT_MODEL_QUEUE_SIZE models
-# in the skopt models list
+# in the skopt model queue, to optimize memory consumption
-SKOPT_MODELS_MAX_NUM = 10
+SKOPT_MODEL_QUEUE_SIZE = 10
 
 MAX_LOSS = 100000  # just a big enough number to be bad result in loss optimization
 
@@ -75,10 +75,10 @@ class Hyperopt:
         self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
         self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
 
-        self.trials_file = (self.config['user_data_dir'] /
+        self.results_file = (self.config['user_data_dir'] /
-                            'hyperopt_results' / 'hyperopt_results.pickle')
+                             'hyperopt_results' / 'hyperopt_results.pickle')
-        self.tickerdata_pickle = (self.config['user_data_dir'] /
+        self.data_pickle_file = (self.config['user_data_dir'] /
-                                  'hyperopt_results' / 'hyperopt_tickerdata.pkl')
+                                 'hyperopt_results' / 'hyperopt_tickerdata.pkl')
         self.total_epochs = config.get('epochs', 0)
 
         self.current_best_loss = 100
@@ -88,10 +88,10 @@ class Hyperopt:
         else:
             logger.info("Continuing on previous hyperopt results.")
 
-        self.num_trials_saved = 0
+        self.num_epochs_saved = 0
 
         # Previous evaluations
-        self.trials: List = []
+        self.epochs: List = []
 
         # Populate functions here (hasattr is slow so should not be run during "regular" operations)
         if hasattr(self.custom_hyperopt, 'populate_indicators'):
@@ -132,7 +132,7 @@ class Hyperopt:
         """
         Remove hyperopt pickle files to restart hyperopt.
         """
-        for f in [self.tickerdata_pickle, self.trials_file]:
+        for f in [self.data_pickle_file, self.results_file]:
             p = Path(f)
             if p.is_file():
                 logger.info(f"Removing `{p}`.")
@@ -151,27 +151,26 @@ class Hyperopt:
         # and the values are taken from the list of parameters.
         return {d.name: v for d, v in zip(dimensions, raw_params)}
 
-    def save_trials(self, final: bool = False) -> None:
+    def _save_results(self) -> None:
         """
-        Save hyperopt trials to file
+        Save hyperopt results to file
         """
-        num_trials = len(self.trials)
+        num_epochs = len(self.epochs)
-        if num_trials > self.num_trials_saved:
+        if num_epochs > self.num_epochs_saved:
-            logger.debug(f"Saving {num_trials} {plural(num_trials, 'epoch')}.")
+            logger.debug(f"Saving {num_epochs} {plural(num_epochs, 'epoch')}.")
-            dump(self.trials, self.trials_file)
+            dump(self.epochs, self.results_file)
-            self.num_trials_saved = num_trials
+            self.num_epochs_saved = num_epochs
-        if final:
+            logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
-            logger.info(f"{num_trials} {plural(num_trials, 'epoch')} "
+                         f"saved to '{self.results_file}'.")
-                        f"saved to '{self.trials_file}'.")
 
     @staticmethod
-    def _read_trials(trials_file: Path) -> List:
+    def _read_results(results_file: Path) -> List:
         """
-        Read hyperopt trials file
+        Read hyperopt results from file
         """
-        logger.info("Reading Trials from '%s'", trials_file)
+        logger.info("Reading epochs from '%s'", results_file)
-        trials = load(trials_file)
+        data = load(results_file)
-        return trials
+        return data
 
     def _get_params_details(self, params: Dict) -> Dict:
         """
@@ -266,36 +265,17 @@ class Hyperopt:
         Log results if it is better than any previous evaluation
         """
         is_best = results['is_best']
-        if not self.print_all:
-            # Print '\n' after each 100th epoch to separate dots from the log messages.
-            # Otherwise output is messy on a terminal.
-            print('.', end='' if results['current_epoch'] % 100 != 0 else None)  # type: ignore
-            sys.stdout.flush()
 
         if self.print_all or is_best:
-            if not self.print_all:
+            print(
-                # Separate the results explanation string from dots
+                self.get_result_table(
-                print("\n")
+                    self.config, results, self.total_epochs,
-            self.print_result_table(self.config, results, self.total_epochs,
+                    self.print_all, self.print_colorized,
-                                    self.print_all, self.print_colorized,
+                    self.hyperopt_table_header
-                                    self.hyperopt_table_header)
+                )
+            )
             self.hyperopt_table_header = 2
 
-    @staticmethod
-    def print_results_explanation(results, total_epochs, highlight_best: bool,
-                                  print_colorized: bool) -> None:
-        """
-        Log results explanation string
-        """
-        explanation_str = Hyperopt._format_explanation_string(results, total_epochs)
-        # Colorize output
-        if print_colorized:
-            if results['total_profit'] > 0:
-                explanation_str = Fore.GREEN + explanation_str
-            if highlight_best and results['is_best']:
-                explanation_str = Style.BRIGHT + explanation_str
-        print(explanation_str)
-
     @staticmethod
     def _format_explanation_string(results, total_epochs) -> str:
         return (("*" if results['is_initial_point'] else " ") +
@@ -304,13 +284,13 @@ class Hyperopt:
                 f"Objective: {results['loss']:.5f}")
 
     @staticmethod
-    def print_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
+    def get_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
-                           print_colorized: bool, remove_header: int) -> None:
+                         print_colorized: bool, remove_header: int) -> str:
         """
         Log result table
         """
         if not results:
-            return
+            return ''
 
         tabulate.PRESERVE_WHITESPACE = True
 
@@ -323,8 +303,9 @@ class Hyperopt:
         trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit',
                           'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
         trials['is_profit'] = False
-        trials.loc[trials['is_initial_point'], 'Best'] = '*'
+        trials.loc[trials['is_initial_point'], 'Best'] = '*     '
         trials.loc[trials['is_best'], 'Best'] = 'Best'
+        trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Trades'] = trials['Trades'].astype(str)
 
@@ -381,7 +362,7 @@ class Hyperopt:
                 trials.to_dict(orient='list'), tablefmt='psql',
                 headers='keys', stralign="right"
             )
-        print(table)
+        return table
 
     @staticmethod
     def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
@@ -394,27 +375,35 @@ class Hyperopt:
 
         # Verification for overwrite
         if path.isfile(csv_file):
-            logger.error("CSV-File already exists!")
+            logger.error(f"CSV file already exists: {csv_file}")
             return
 
         try:
             io.open(csv_file, 'w+').close()
         except IOError:
-            logger.error("Filed to create CSV-File!")
+            logger.error(f"Failed to create CSV file: {csv_file}")
             return
 
         trials = json_normalize(results, max_level=1)
         trials['Best'] = ''
         trials['Stake currency'] = config['stake_currency']
-        trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
+
-                         'results_metrics.avg_profit', 'results_metrics.total_profit',
+        base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
-                         'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
+                        'results_metrics.avg_profit', 'results_metrics.total_profit',
-                         'loss', 'is_initial_point', 'is_best']]
+                        'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
-        trials.columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
+                        'loss', 'is_initial_point', 'is_best']
-                          'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
+        param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
+        trials = trials[base_metrics + param_metrics]
+
+        base_columns = ['Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Stake currency',
+                        'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
+        param_columns = list(results[0]['params_dict'].keys())
+        trials.columns = base_columns + param_columns
+
         trials['is_profit'] = False
         trials.loc[trials['is_initial_point'], 'Best'] = '*'
         trials.loc[trials['is_best'], 'Best'] = 'Best'
+        trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Epoch'] = trials['Epoch'].astype(str)
         trials['Trades'] = trials['Trades'].astype(str)
@@ -437,7 +426,7 @@ class Hyperopt:
 
         trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
         trials.to_csv(csv_file, index=False, header=True, mode='w', encoding='UTF-8')
-        print("CSV-File created!")
+        logger.info(f"CSV file created: {csv_file}")
 
     def has_space(self, space: str) -> bool:
         """
@@ -512,7 +501,7 @@ class Hyperopt:
             self.backtesting.strategy.trailing_only_offset_is_reached = \
                 d['trailing_only_offset_is_reached']
 
-        processed = load(self.tickerdata_pickle)
+        processed = load(self.data_pickle_file)
 
         min_date, max_date = get_timerange(processed)
 
@@ -581,43 +570,28 @@ class Hyperopt:
             n_initial_points=INITIAL_POINTS,
             acq_optimizer_kwargs={'n_jobs': cpu_count},
             random_state=self.random_state,
+            model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
         )
 
-    def fix_optimizer_models_list(self) -> None:
-        """
-        WORKAROUND: Since skopt is not actively supported, this resolves problems with skopt
-        memory usage, see also: https://github.com/scikit-optimize/scikit-optimize/pull/746
-
-        This may cease working when skopt updates if implementation of this intrinsic
-        part changes.
-        """
-        n = len(self.opt.models) - SKOPT_MODELS_MAX_NUM
-        # Keep no more than 2*SKOPT_MODELS_MAX_NUM models in the skopt models list,
-        # remove the old ones. These are actually of no use, the current model
-        # from the estimator is the only one used in the skopt optimizer.
-        # Freqtrade code also does not inspect details of the models.
-        if n >= SKOPT_MODELS_MAX_NUM:
-            logger.debug(f"Fixing skopt models list, removing {n} old items...")
-            del self.opt.models[0:n]
-
     def run_optimizer_parallel(self, parallel, asked, i) -> List:
         return parallel(delayed(
                         wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
 
     @staticmethod
-    def load_previous_results(trials_file: Path) -> List:
+    def load_previous_results(results_file: Path) -> List:
         """
         Load data for epochs from the file if we have one
         """
-        trials: List = []
+        epochs: List = []
-        if trials_file.is_file() and trials_file.stat().st_size > 0:
+        if results_file.is_file() and results_file.stat().st_size > 0:
-            trials = Hyperopt._read_trials(trials_file)
+            epochs = Hyperopt._read_results(results_file)
-            if trials[0].get('is_best') is None:
+            # Detection of some old format, without 'is_best' field saved
+            if epochs[0].get('is_best') is None:
                 raise OperationalException(
                     "The file with Hyperopt results is incompatible with this version "
                     "of Freqtrade and cannot be loaded.")
-            logger.info(f"Loaded {len(trials)} previous evaluations from disk.")
+            logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
-        return trials
+        return epochs
 
     def _set_random_state(self, random_state: Optional[int]) -> int:
         return random_state or random.randint(1, 2**16 - 1)
@@ -628,7 +602,7 @@ class Hyperopt:
         self.hyperopt_table_header = -1
         data, timerange = self.backtesting.load_bt_data()
 
-        preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
+        preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
 
         # Trim startup period from analyzed dataframe
         for pair, df in preprocessed.items():
@@ -639,12 +613,13 @@ class Hyperopt:
             'Hyperopting with data from %s up to %s (%s days)..',
             min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
         )
-        dump(preprocessed, self.tickerdata_pickle)
+        dump(preprocessed, self.data_pickle_file)
 
         # We don't need exchange instance anymore while running hyperopt
         self.backtesting.exchange = None  # type: ignore
+        self.backtesting.pairlists = None  # type: ignore
 
-        self.trials = self.load_previous_results(self.trials_file)
+        self.epochs = self.load_previous_results(self.results_file)
 
         cpus = cpu_count()
         logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
@@ -653,57 +628,85 @@ class Hyperopt:
 
         self.dimensions: List[Dimension] = self.hyperopt_space()
         self.opt = self.get_optimizer(self.dimensions, config_jobs)
-
-        if self.print_colorized:
-            colorama_init(autoreset=True)
-
         try:
             with Parallel(n_jobs=config_jobs) as parallel:
                 jobs = parallel._effective_n_jobs()
                 logger.info(f'Effective number of parallel workers used: {jobs}')
-                EVALS = ceil(self.total_epochs / jobs)
-                for i in range(EVALS):
-                    # Correct the number of epochs to be processed for the last
-                    # iteration (should not exceed self.total_epochs in total)
-                    n_rest = (i + 1) * jobs - self.total_epochs
-                    current_jobs = jobs - n_rest if n_rest > 0 else jobs
 
-                    asked = self.opt.ask(n_points=current_jobs)
+                # Define progressbar
-                    f_val = self.run_optimizer_parallel(parallel, asked, i)
+                if self.print_colorized:
-                    self.opt.tell(asked, [v['loss'] for v in f_val])
+                    widgets = [
-                    self.fix_optimizer_models_list()
+                        ' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
+                        ' (', progressbar.Percentage(), ')] ',
+                        progressbar.Bar(marker=progressbar.AnimatedMarker(
+                            fill='\N{FULL BLOCK}',
+                            fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
+                            marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
+                        )),
+                        ' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
+                    ]
+                else:
+                    widgets = [
+                        ' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
+                        ' (', progressbar.Percentage(), ')] ',
+                        progressbar.Bar(marker=progressbar.AnimatedMarker(
+                            fill='\N{FULL BLOCK}',
+                        )),
+                        ' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
+                    ]
+                with progressbar.ProgressBar(
+                         max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
+                         widgets=widgets
+                     ) as pbar:
+                    EVALS = ceil(self.total_epochs / jobs)
+                    for i in range(EVALS):
+                        # Correct the number of epochs to be processed for the last
+                        # iteration (should not exceed self.total_epochs in total)
+                        n_rest = (i + 1) * jobs - self.total_epochs
+                        current_jobs = jobs - n_rest if n_rest > 0 else jobs
 
-                    for j, val in enumerate(f_val):
+                        asked = self.opt.ask(n_points=current_jobs)
-                        # Use human-friendly indexes here (starting from 1)
+                        f_val = self.run_optimizer_parallel(parallel, asked, i)
-                        current = i * jobs + j + 1
+                        self.opt.tell(asked, [v['loss'] for v in f_val])
-                        val['current_epoch'] = current
-                        val['is_initial_point'] = current <= INITIAL_POINTS
-                        logger.debug(f"Optimizer epoch evaluated: {val}")
 
-                        is_best = self.is_best_loss(val, self.current_best_loss)
+                        # Calculate progressbar outputs
-                        # This value is assigned here and not in the optimization method
+                        for j, val in enumerate(f_val):
-                        # to keep proper order in the list of results. That's because
+                            # Use human-friendly indexes here (starting from 1)
-                        # evaluations can take different time. Here they are aligned in the
+                            current = i * jobs + j + 1
-                        # order they will be shown to the user.
+                            val['current_epoch'] = current
-                        val['is_best'] = is_best
+                            val['is_initial_point'] = current <= INITIAL_POINTS
 
-                        self.print_results(val)
+                            logger.debug(f"Optimizer epoch evaluated: {val}")
+
+                            is_best = self.is_best_loss(val, self.current_best_loss)
+                            # This value is assigned here and not in the optimization method
+                            # to keep proper order in the list of results. That's because
+                            # evaluations can take different time. Here they are aligned in the
+                            # order they will be shown to the user.
+                            val['is_best'] = is_best
+                            self.print_results(val)
+
+                            if is_best:
+                                self.current_best_loss = val['loss']
+                            self.epochs.append(val)
+
+                            # Save results after each best epoch and every 100 epochs
+                            if is_best or current % 100 == 0:
+                                self._save_results()
+
+                            pbar.update(current)
 
-                        if is_best:
-                            self.current_best_loss = val['loss']
-                        self.trials.append(val)
-                        # Save results after each best epoch and every 100 epochs
-                        if is_best or current % 100 == 0:
-                            self.save_trials()
         except KeyboardInterrupt:
             print('User interrupted..')
 
-        self.save_trials(final=True)
+        self._save_results()
+        logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
+                    f"saved to '{self.results_file}'.")
 
-        if self.trials:
+        if self.epochs:
-            sorted_trials = sorted(self.trials, key=itemgetter('loss'))
+            sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
-            results = sorted_trials[0]
+            best_epoch = sorted_epochs[0]
-            self.print_epoch_details(results, self.total_epochs, self.print_json)
+            self.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
         else:
             # This is printed when Ctrl+C is pressed quickly, before first epochs have
             # a chance to be evaluated.

freqtrade/optimize/optimize_reports.py

@@ -1,9 +1,38 @@
+import logging
 from datetime import timedelta
+from pathlib import Path
 from typing import Dict
 
 from pandas import DataFrame
 from tabulate import tabulate
 
+from freqtrade.misc import file_dump_json
+
+logger = logging.getLogger(__name__)
+
+
+def store_backtest_result(recordfilename: Path, all_results: Dict[str, DataFrame]) -> None:
+    """
+    Stores backtest results to file (one file per strategy)
+    :param recordfilename: Destination filename
+    :param all_results: Dict of Dataframes, one results dataframe per strategy
+    """
+    for strategy, results in all_results.items():
+        records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
+                    t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
+                    t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
+                   for index, t in results.iterrows()]
+
+        if records:
+            filename = recordfilename
+            if len(all_results) > 1:
+                # Inject strategy to filename
+                filename = Path.joinpath(
+                    recordfilename.parent,
+                    f'{recordfilename.stem}-{strategy}').with_suffix(recordfilename.suffix)
+            logger.info(f'Dumping backtest results to {filename}')
+            file_dump_json(filename, records)
+
 
 def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
                         results: DataFrame, skip_nan: bool = False) -> str:
@@ -69,12 +98,12 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
                     floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")  # type: ignore
 
 
-def generate_text_table_sell_reason(
+def generate_text_table_sell_reason(stake_currency: str, max_open_trades: int,
-    data: Dict[str, Dict], stake_currency: str, max_open_trades: int, results: DataFrame
+                                    results: DataFrame) -> str:
-) -> str:
     """
     Generate small table outlining Backtest results
-    :param data: Dict of <pair: dataframe> containing data that was used during backtesting.
+    :param stake_currency: Stakecurrency used
+    :param max_open_trades: Max_open_trades parameter
     :param results: Dataframe containing the backtest results
     :return: pretty printed table with tabulate as string
     """
@@ -173,3 +202,43 @@ def generate_edge_table(results: dict) -> str:
     # Ignore type as floatfmt does allow tuples but mypy does not know that
     return tabulate(tabular_data, headers=headers,
                     floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")  # type: ignore
+
+
+def show_backtest_results(config: Dict, btdata: Dict[str, DataFrame],
+                          all_results: Dict[str, DataFrame]):
+    for strategy, results in all_results.items():
+
+        print(f"Result for strategy {strategy}")
+        table = generate_text_table(btdata, stake_currency=config['stake_currency'],
+                                    max_open_trades=config['max_open_trades'],
+                                    results=results)
+        if isinstance(table, str):
+            print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
+        print(table)
+
+        table = generate_text_table_sell_reason(stake_currency=config['stake_currency'],
+                                                max_open_trades=config['max_open_trades'],
+                                                results=results)
+        if isinstance(table, str):
+            print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
+        print(table)
+
+        table = generate_text_table(btdata,
+                                    stake_currency=config['stake_currency'],
+                                    max_open_trades=config['max_open_trades'],
+                                    results=results.loc[results.open_at_end], skip_nan=True)
+        if isinstance(table, str):
+            print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
+        print(table)
+        if isinstance(table, str):
+            print('=' * len(table.splitlines()[0]))
+        print()
+    if len(all_results) > 1:
+        # Print Strategy summary table
+        table = generate_text_table_strategy(config['stake_currency'],
+                                             config['max_open_trades'],
+                                             all_results=all_results)
+        print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
+        print(table)
+        print('=' * len(table.splitlines()[0]))
+        print('\nFor more details, please look at the detail tables above')

freqtrade/pairlist/IPairList.py

@@ -1,16 +1,16 @@
 """
-Static List provider
+PairList Handler base class
-
+"""
-Provides lists as configured in config.json
-
- """
 import logging
 from abc import ABC, abstractmethod, abstractproperty
 from copy import deepcopy
 from typing import Any, Dict, List
 
+from cachetools import TTLCache, cached
+
 from freqtrade.exchange import market_is_active
 
+
 logger = logging.getLogger(__name__)
 
 
@@ -21,16 +21,19 @@ class IPairList(ABC):
                  pairlist_pos: int) -> None:
         """
         :param exchange: Exchange instance
-        :param pairlistmanager: Instanciating Pairlist manager
+        :param pairlistmanager: Instantiated Pairlist manager
         :param config: Global bot configuration
-        :param pairlistconfig: Configuration for this pairlist - can be empty.
+        :param pairlistconfig: Configuration for this Pairlist Handler - can be empty.
-        :param pairlist_pos: Position of the filter in the pairlist-filter-list
+        :param pairlist_pos: Position of the Pairlist Handler in the chain
         """
         self._exchange = exchange
         self._pairlistmanager = pairlistmanager
         self._config = config
         self._pairlistconfig = pairlistconfig
         self._pairlist_pos = pairlist_pos
+        self.refresh_period = self._pairlistconfig.get('refresh_period', 1800)
+        self._last_refresh = 0
+        self._log_cache = TTLCache(maxsize=1024, ttl=self.refresh_period)
 
     @property
     def name(self) -> str:
@@ -40,6 +43,24 @@ class IPairList(ABC):
         """
         return self.__class__.__name__
 
+    def log_on_refresh(self, logmethod, message: str) -> None:
+        """
+        Logs message - not more often than "refresh_period" to avoid log spamming
+        Logs the log-message as debug as well to simplify debugging.
+        :param logmethod: Function that'll be called. Most likely `logger.info`.
+        :param message: String containing the message to be sent to the function.
+        :return: None.
+        """
+
+        @cached(cache=self._log_cache)
+        def _log_on_refresh(message: str):
+            logmethod(message)
+
+        # Log as debug first
+        logger.debug(message)
+        # Call hidden function.
+        _log_on_refresh(message)
+
     @abstractproperty
     def needstickers(self) -> bool:
         """
@@ -72,10 +93,10 @@ class IPairList(ABC):
         """
         Verify and remove items from pairlist - returning a filtered pairlist.
         Logs a warning or info depending on `aswarning`.
-        Pairlists explicitly using this method shall use `aswarning=False`!
+        Pairlist Handlers explicitly using this method shall use `aswarning=False`!
         :param pairlist: Pairlist to validate
         :param blacklist: Blacklist to validate pairlist against
-        :param aswarning: Log message as Warning or info
+        :param aswarning: Log message as Warning or Info
         :return: pairlist - blacklisted pairs
         """
         for pair in deepcopy(pairlist):

freqtrade/pairlist/PrecisionFilter.py

@@ -1,14 +1,26 @@
+"""
+Precision pair list filter
+"""
 import logging
 from copy import deepcopy
-from typing import Dict, List
+from typing import Any, Dict, List
 
 from freqtrade.pairlist.IPairList import IPairList
 
+
 logger = logging.getLogger(__name__)
 
 
 class PrecisionFilter(IPairList):
 
+    def __init__(self, exchange, pairlistmanager,
+                 config: Dict[str, Any], pairlistconfig: Dict[str, Any],
+                 pairlist_pos: int) -> None:
+        super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
+
+        # Precalculate sanitized stoploss value to avoid recalculation for every pair
+        self._stoploss = 1 - abs(self._config['stoploss'])
+
     @property
     def needstickers(self) -> bool:
         """
@@ -31,33 +43,32 @@ class PrecisionFilter(IPairList):
         :param ticker: ticker dict as returned from ccxt.load_markets()
         :param stoploss: stoploss value as set in the configuration
                         (already cleaned to be 1 - stoploss)
-        :return: True if the pair can stay, false if it should be removed
+        :return: True if the pair can stay, False if it should be removed
         """
         stop_price = ticker['ask'] * stoploss
+
         # Adjust stop-prices to precision
         sp = self._exchange.price_to_precision(ticker["symbol"], stop_price)
+
         stop_gap_price = self._exchange.price_to_precision(ticker["symbol"], stop_price * 0.99)
         logger.debug(f"{ticker['symbol']} - {sp} : {stop_gap_price}")
+
         if sp <= stop_gap_price:
-            logger.info(f"Removed {ticker['symbol']} from whitelist, "
+            self.log_on_refresh(logger.info,
-                        f"because stop price {sp} would be <= stop limit {stop_gap_price}")
+                                f"Removed {ticker['symbol']} from whitelist, "
+                                f"because stop price {sp} would be <= stop limit {stop_gap_price}")
             return False
+
         return True
 
     def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
         Filters and sorts pairlists and assigns and returns them again.
         """
-        stoploss = self._config.get('stoploss')
-        if stoploss is not None:
-            # Precalculate sanitized stoploss value to avoid recalculation for every pair
-            stoploss = 1 - abs(stoploss)
         # Copy list since we're modifying this list
         for p in deepcopy(pairlist):
-            ticker = tickers.get(p)
             # Filter out assets which would not allow setting a stoploss
-            if not ticker or (stoploss and not self._validate_precision_filter(ticker, stoploss)):
+            if not self._validate_precision_filter(tickers[p], self._stoploss):
                 pairlist.remove(p)
-                continue
 
         return pairlist

freqtrade/pairlist/PriceFilter.py

@@ -1,9 +1,13 @@
+"""
+Price pair list filter
+"""
 import logging
 from copy import deepcopy
 from typing import Any, Dict, List
 
 from freqtrade.pairlist.IPairList import IPairList
 
+
 logger = logging.getLogger(__name__)
 
 
@@ -35,21 +39,22 @@ class PriceFilter(IPairList):
         """
         Check if if one price-step (pip) is > than a certain barrier.
         :param ticker: ticker dict as returned from ccxt.load_markets()
-        :param precision: Precision
         :return: True if the pair can stay, false if it should be removed
         """
-        precision = self._exchange.markets[ticker['symbol']]['precision']['price']
+        if ticker['last'] is None:
-
+            self.log_on_refresh(logger.info,
-        compare = ticker['last'] + 1 / pow(10, precision)
+                                f"Removed {ticker['symbol']} from whitelist, because "
-        changeperc = (compare - ticker['last']) / ticker['last']
+                                "ticker['last'] is empty (Usually no trade in the last 24h).")
+            return False
+        compare = self._exchange.price_get_one_pip(ticker['symbol'], ticker['last'])
+        changeperc = compare / ticker['last']
         if changeperc > self._low_price_ratio:
-            logger.info(f"Removed {ticker['symbol']} from whitelist, "
+            self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
-                        f"because 1 unit is {changeperc * 100:.3f}%")
+                                             f"because 1 unit is {changeperc * 100:.3f}%")
             return False
         return True
 
     def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
-
         """
         Filters and sorts pairlist and returns the whitelist again.
         Called on each bot iteration - please use internal caching if necessary
@@ -57,14 +62,11 @@ class PriceFilter(IPairList):
         :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new whitelist
         """
-        # Copy list since we're modifying this list
+        if self._low_price_ratio:
-        for p in deepcopy(pairlist):
+            # Copy list since we're modifying this list
-            ticker = tickers.get(p)
+            for p in deepcopy(pairlist):
-            if not ticker:
+                # Filter out assets which would not allow setting a stoploss
-                pairlist.remove(p)
+                if not self._validate_ticker_lowprice(tickers[p]):
-
+                    pairlist.remove(p)
-            # Filter out assets which would not allow setting a stoploss
-            if self._low_price_ratio and not self._validate_ticker_lowprice(ticker):
-                pairlist.remove(p)
 
         return pairlist

freqtrade/pairlist/ShuffleFilter.py

@@ -0,0 +1,50 @@
+"""
+Shuffle pair list filter
+"""
+import logging
+import random
+from typing import Dict, List
+
+from freqtrade.pairlist.IPairList import IPairList
+
+
+logger = logging.getLogger(__name__)
+
+
+class ShuffleFilter(IPairList):
+
+    def __init__(self, exchange, pairlistmanager, config, pairlistconfig: dict,
+                 pairlist_pos: int) -> None:
+        super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
+
+        self._seed = pairlistconfig.get('seed')
+        self._random = random.Random(self._seed)
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requries tickers, an empty List is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return (f"{self.name} - Shuffling pairs" +
+                (f", seed = {self._seed}." if self._seed is not None else "."))
+
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
+        :return: new whitelist
+        """
+        # Shuffle is done inplace
+        self._random.shuffle(pairlist)
+
+        return pairlist

freqtrade/pairlist/SpreadFilter.py

@@ -1,9 +1,13 @@
+"""
+Spread pair list filter
+"""
 import logging
 from copy import deepcopy
 from typing import Dict, List
 
 from freqtrade.pairlist.IPairList import IPairList
 
+
 logger = logging.getLogger(__name__)
 
 
@@ -31,8 +35,24 @@ class SpreadFilter(IPairList):
         return (f"{self.name} - Filtering pairs with ask/bid diff above "
                 f"{self._max_spread_ratio * 100}%.")
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+    def _validate_spread(self, ticker: dict) -> bool:
+        """
+        Validate spread for the ticker
+        :param ticker: ticker dict as returned from ccxt.load_markets()
+        :return: True if the pair can stay, False if it should be removed
+        """
+        if 'bid' in ticker and 'ask' in ticker:
+            spread = 1 - ticker['bid'] / ticker['ask']
+            if spread > self._max_spread_ratio:
+                self.log_on_refresh(logger.info, f"Removed {ticker['symbol']} from whitelist, "
+                                                 f"because spread {spread * 100:.3f}% >"
+                                                 f"{self._max_spread_ratio * 100}%")
+                return False
+            else:
+                return True
+        return False
 
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
         Filters and sorts pairlist and returns the whitelist again.
         Called on each bot iteration - please use internal caching if necessary
@@ -41,19 +61,10 @@ class SpreadFilter(IPairList):
         :return: new whitelist
         """
         # Copy list since we're modifying this list
-
-        spread = None
         for p in deepcopy(pairlist):
-            ticker = tickers.get(p)
+            ticker = tickers[p]
-            assert ticker is not None
+            # Filter out assets
-            if 'bid' in ticker and 'ask' in ticker:
+            if not self._validate_spread(ticker):
-                spread = 1 - ticker['bid'] / ticker['ask']
-                if not ticker or spread > self._max_spread_ratio:
-                    logger.info(f"Removed {ticker['symbol']} from whitelist, "
-                                f"because spread {spread * 100:.3f}% >"
-                                f"{self._max_spread_ratio * 100}%")
-                    pairlist.remove(p)
-            else:
                 pairlist.remove(p)
 
         return pairlist

freqtrade/pairlist/StaticPairList.py

@@ -1,14 +1,14 @@
 """
-Static List provider
+Static Pair List provider
 
-Provides lists as configured in config.json
+Provides pair white list as it configured in config
-
+"""
- """
 import logging
 from typing import Dict, List
 
 from freqtrade.pairlist.IPairList import IPairList
 
+
 logger = logging.getLogger(__name__)
 
 

freqtrade/pairlist/VolumePairList.py

@@ -1,9 +1,8 @@
 """
 Volume PairList provider
 
-Provides lists as configured in config.json
+Provides dynamic pair list based on trade volumes
-
+"""
- """
 import logging
 from datetime import datetime
 from typing import Any, Dict, List
@@ -11,8 +10,10 @@ from typing import Any, Dict, List
 from freqtrade.exceptions import OperationalException
 from freqtrade.pairlist.IPairList import IPairList
 
+
 logger = logging.getLogger(__name__)
 
+
 SORT_VALUES = ['askVolume', 'bidVolume', 'quoteVolume']
 
 
@@ -24,8 +25,10 @@ class VolumePairList(IPairList):
 
         if 'number_assets' not in self._pairlistconfig:
             raise OperationalException(
-                f'`number_assets` not specified. Please check your configuration '
+                '`number_assets` not specified. Please check your configuration '
                 'for "pairlist.config.number_assets"')
+
+        self._stake_currency = config['stake_currency']
         self._number_pairs = self._pairlistconfig['number_assets']
         self._sort_key = self._pairlistconfig.get('sort_key', 'quoteVolume')
         self._min_value = self._pairlistconfig.get('min_value', 0)
@@ -36,10 +39,15 @@ class VolumePairList(IPairList):
                 'Exchange does not support dynamic whitelist.'
                 'Please edit your config and restart the bot'
             )
+
         if not self._validate_keys(self._sort_key):
             raise OperationalException(
                 f'key {self._sort_key} not in {SORT_VALUES}')
-        self._last_refresh = 0
+
+        if self._sort_key != 'quoteVolume':
+            logger.warning(
+                "DEPRECATED: using any key other than quoteVolume for VolumePairList is deprecated."
+                )
 
     @property
     def needstickers(self) -> bool:
@@ -68,47 +76,47 @@ class VolumePairList(IPairList):
         :return: new whitelist
         """
         # Generate dynamic whitelist
-        if self._last_refresh + self.refresh_period < datetime.now().timestamp():
+        # Must always run if this pairlist is not the first in the list.
-            self._last_refresh = int(datetime.now().timestamp())
+        if (self._pairlist_pos != 0 or
-            return self._gen_pair_whitelist(pairlist,
+                (self._last_refresh + self.refresh_period < datetime.now().timestamp())):
-                                            tickers,
-                                            self._config['stake_currency'],
-                                            self._sort_key,
-                                            self._min_value
-                                            )
-        else:
-            return pairlist
 
-    def _gen_pair_whitelist(self, pairlist: List[str], tickers: Dict,
+            self._last_refresh = int(datetime.now().timestamp())
-                            base_currency: str, key: str, min_val: int) -> List[str]:
+            pairs = self._gen_pair_whitelist(pairlist, tickers)
+        else:
+            pairs = pairlist
+
+        self.log_on_refresh(logger.info, f"Searching {self._number_pairs} pairs: {pairs}")
+
+        return pairs
+
+    def _gen_pair_whitelist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
         Updates the whitelist with with a dynamically generated list
-        :param base_currency: base currency as str
+        :param pairlist: pairlist to filter or sort
-        :param key: sort key (defaults to 'quoteVolume')
         :param tickers: Tickers (from exchange.get_tickers()).
         :return: List of pairs
         """
-
         if self._pairlist_pos == 0:
             # If VolumePairList is the first in the list, use fresh pairlist
             # Check if pair quote currency equals to the stake currency.
-            filtered_tickers = [v for k, v in tickers.items()
+            filtered_tickers = [
-                                if (self._exchange.get_pair_quote_currency(k) == base_currency
+                    v for k, v in tickers.items()
-                                    and v[key] is not None)]
+                    if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
+                        and v[self._sort_key] is not None)]
         else:
-            # If other pairlist is in front, use the incomming pairlist.
+            # If other pairlist is in front, use the incoming pairlist.
             filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
 
-        if min_val > 0:
+        if self._min_value > 0:
-            filtered_tickers = list(filter(lambda t: t[key] > min_val, filtered_tickers))
+            filtered_tickers = [
+                    v for v in filtered_tickers if v[self._sort_key] > self._min_value]
 
-        sorted_tickers = sorted(filtered_tickers, reverse=True, key=lambda t: t[key])
+        sorted_tickers = sorted(filtered_tickers, reverse=True, key=lambda t: t[self._sort_key])
 
         # Validate whitelist to only have active market pairs
         pairs = self._whitelist_for_active_markets([s['symbol'] for s in sorted_tickers])
         pairs = self._verify_blacklist(pairs, aswarning=False)
-        # Limit to X number of pairs
+        # Limit pairlist to the requested number of pairs
         pairs = pairs[:self._number_pairs]
-        logger.info(f"Searching {self._number_pairs} pairs: {pairs}")
 
         return pairs

freqtrade/pairlist/pairlistmanager.py

@@ -1,10 +1,8 @@
 """
-Static List provider
+PairList manager class
-
+"""
-Provides lists as configured in config.json
-
- """
 import logging
+from copy import deepcopy
 from typing import Dict, List
 
 from cachetools import TTLCache, cached
@@ -12,6 +10,8 @@ from cachetools import TTLCache, cached
 from freqtrade.exceptions import OperationalException
 from freqtrade.pairlist.IPairList import IPairList
 from freqtrade.resolvers import PairListResolver
+from freqtrade.typing import ListPairsWithTimeframes
+
 
 logger = logging.getLogger(__name__)
 
@@ -23,24 +23,25 @@ class PairListManager():
         self._config = config
         self._whitelist = self._config['exchange'].get('pair_whitelist')
         self._blacklist = self._config['exchange'].get('pair_blacklist', [])
-        self._pairlists: List[IPairList] = []
+        self._pairlist_handlers: List[IPairList] = []
         self._tickers_needed = False
-        for pl in self._config.get('pairlists', None):
+        for pairlist_handler_config in self._config.get('pairlists', None):
-            if 'method' not in pl:
+            if 'method' not in pairlist_handler_config:
-                logger.warning(f"No method in {pl}")
+                logger.warning(f"No method found in {pairlist_handler_config}, ignoring.")
                 continue
-            pairl = PairListResolver.load_pairlist(pl.get('method'),
+            pairlist_handler = PairListResolver.load_pairlist(
-                                                   exchange=exchange,
+                    pairlist_handler_config['method'],
-                                                   pairlistmanager=self,
+                    exchange=exchange,
-                                                   config=config,
+                    pairlistmanager=self,
-                                                   pairlistconfig=pl,
+                    config=config,
-                                                   pairlist_pos=len(self._pairlists)
+                    pairlistconfig=pairlist_handler_config,
-                                                   )
+                    pairlist_pos=len(self._pairlist_handlers)
-            self._tickers_needed = pairl.needstickers or self._tickers_needed
+                    )
-            self._pairlists.append(pairl)
+            self._tickers_needed |= pairlist_handler.needstickers
+            self._pairlist_handlers.append(pairlist_handler)
 
-        if not self._pairlists:
+        if not self._pairlist_handlers:
-            raise OperationalException("No Pairlist defined!")
+            raise OperationalException("No Pairlist Handlers defined")
 
     @property
     def whitelist(self) -> List[str]:
@@ -60,15 +61,15 @@ class PairListManager():
     @property
     def name_list(self) -> List[str]:
         """
-        Get list of loaded pairlists names
+        Get list of loaded Pairlist Handler names
         """
-        return [p.name for p in self._pairlists]
+        return [p.name for p in self._pairlist_handlers]
 
     def short_desc(self) -> List[Dict]:
         """
-        List of short_desc for each pairlist
+        List of short_desc for each Pairlist Handler
         """
-        return [{p.name: p.short_desc()} for p in self._pairlists]
+        return [{p.name: p.short_desc()} for p in self._pairlist_handlers]
 
     @cached(TTLCache(maxsize=1, ttl=1800))
     def _get_cached_tickers(self):
@@ -76,21 +77,41 @@ class PairListManager():
 
     def refresh_pairlist(self) -> None:
         """
-        Run pairlist through all configured pairlists.
+        Run pairlist through all configured Pairlist Handlers.
         """
-
+        # Tickers should be cached to avoid calling the exchange on each call.
-        pairlist = self._whitelist.copy()
-
-        # tickers should be cached to avoid calling the exchange on each call.
         tickers: Dict = {}
         if self._tickers_needed:
             tickers = self._get_cached_tickers()
 
-        # Process all pairlists in chain
+        # Adjust whitelist if filters are using tickers
-        for pl in self._pairlists:
+        pairlist = self._prepare_whitelist(self._whitelist.copy(), tickers)
-            pairlist = pl.filter_pairlist(pairlist, tickers)
 
-        # Validation against blacklist happens after the pairlists to ensure blacklist is respected.
+        # Process all Pairlist Handlers in the chain
+        for pairlist_handler in self._pairlist_handlers:
+            pairlist = pairlist_handler.filter_pairlist(pairlist, tickers)
+
+        # Validation against blacklist happens after the chain of Pairlist Handlers
+        # to ensure blacklist is respected.
         pairlist = IPairList.verify_blacklist(pairlist, self.blacklist, True)
 
         self._whitelist = pairlist
+
+    def _prepare_whitelist(self, pairlist: List[str], tickers) -> List[str]:
+        """
+        Prepare sanitized pairlist for Pairlist Handlers that use tickers data - remove
+        pairs that do not have ticker available
+        """
+        if self._tickers_needed:
+            # Copy list since we're modifying this list
+            for p in deepcopy(pairlist):
+                if p not in tickers:
+                    pairlist.remove(p)
+
+        return pairlist
+
+    def create_pair_list(self, pairs: List[str], timeframe: str = None) -> ListPairsWithTimeframes:
+        """
+        Create list of pair tuples with (pair, ticker_interval)
+        """
+        return [(pair, timeframe or self._config['ticker_interval']) for pair in pairs]

freqtrade/persistence.py

@@ -86,11 +86,15 @@ def check_migrate(engine) -> None:
         logger.debug(f'trying {table_back_name}')
 
     # Check for latest column
-    if not has_column(cols, 'open_trade_price'):
+    if not has_column(cols, 'sell_order_status'):
         logger.info(f'Running database migration - backup available as {table_back_name}')
 
         fee_open = get_column_def(cols, 'fee_open', 'fee')
+        fee_open_cost = get_column_def(cols, 'fee_open_cost', 'null')
+        fee_open_currency = get_column_def(cols, 'fee_open_currency', 'null')
         fee_close = get_column_def(cols, 'fee_close', 'fee')
+        fee_close_cost = get_column_def(cols, 'fee_close_cost', 'null')
+        fee_close_currency = get_column_def(cols, 'fee_close_currency', 'null')
         open_rate_requested = get_column_def(cols, 'open_rate_requested', 'null')
         close_rate_requested = get_column_def(cols, 'close_rate_requested', 'null')
         stop_loss = get_column_def(cols, 'stop_loss', '0.0')
@@ -106,6 +110,10 @@ def check_migrate(engine) -> None:
         ticker_interval = get_column_def(cols, 'ticker_interval', 'null')
         open_trade_price = get_column_def(cols, 'open_trade_price',
                                           f'amount * open_rate * (1 + {fee_open})')
+        close_profit_abs = get_column_def(
+            cols, 'close_profit_abs',
+            f"(amount * close_rate * (1 - {fee_close})) - {open_trade_price}")
+        sell_order_status = get_column_def(cols, 'sell_order_status', 'null')
 
         # Schema migration necessary
         engine.execute(f"alter table trades rename to {table_back_name}")
@@ -117,13 +125,15 @@ def check_migrate(engine) -> None:
 
         # Copy data back - following the correct schema
         engine.execute(f"""insert into trades
-                (id, exchange, pair, is_open, fee_open, fee_close, open_rate,
+                (id, exchange, pair, is_open,
+                fee_open, fee_open_cost, fee_open_currency,
+                fee_close, fee_close_cost, fee_open_currency, open_rate,
                 open_rate_requested, close_rate, close_rate_requested, close_profit,
                 stake_amount, amount, open_date, close_date, open_order_id,
                 stop_loss, stop_loss_pct, initial_stop_loss, initial_stop_loss_pct,
                 stoploss_order_id, stoploss_last_update,
-                max_rate, min_rate, sell_reason, strategy,
+                max_rate, min_rate, sell_reason, sell_order_status, strategy,
-                ticker_interval, open_trade_price
+                ticker_interval, open_trade_price, close_profit_abs
                 )
             select id, lower(exchange),
                 case
@@ -133,7 +143,9 @@ def check_migrate(engine) -> None:
                     else pair
                     end
                 pair,
-                is_open, {fee_open} fee_open, {fee_close} fee_close,
+                is_open, {fee_open} fee_open, {fee_open_cost} fee_open_cost,
+                {fee_open_currency} fee_open_currency, {fee_close} fee_close,
+                {fee_close_cost} fee_close_cost, {fee_close_currency} fee_close_currency,
                 open_rate, {open_rate_requested} open_rate_requested, close_rate,
                 {close_rate_requested} close_rate_requested, close_profit,
                 stake_amount, amount, open_date, close_date, open_order_id,
@@ -142,8 +154,9 @@ def check_migrate(engine) -> None:
                 {initial_stop_loss_pct} initial_stop_loss_pct,
                 {stoploss_order_id} stoploss_order_id, {stoploss_last_update} stoploss_last_update,
                 {max_rate} max_rate, {min_rate} min_rate, {sell_reason} sell_reason,
+                {sell_order_status} sell_order_status,
                 {strategy} strategy, {ticker_interval} ticker_interval,
-                {open_trade_price} open_trade_price
+                {open_trade_price} open_trade_price, {close_profit_abs} close_profit_abs
                 from {table_back_name}
              """)
 
@@ -182,14 +195,19 @@ class Trade(_DECL_BASE):
     pair = Column(String, nullable=False, index=True)
     is_open = Column(Boolean, nullable=False, default=True, index=True)
     fee_open = Column(Float, nullable=False, default=0.0)
+    fee_open_cost = Column(Float, nullable=True)
+    fee_open_currency = Column(String, nullable=True)
     fee_close = Column(Float, nullable=False, default=0.0)
+    fee_close_cost = Column(Float, nullable=True)
+    fee_close_currency = Column(String, nullable=True)
     open_rate = Column(Float)
     open_rate_requested = Column(Float)
-    # open_trade_price - calcuated via _calc_open_trade_price
+    # open_trade_price - calculated via _calc_open_trade_price
     open_trade_price = Column(Float)
     close_rate = Column(Float)
     close_rate_requested = Column(Float)
     close_profit = Column(Float)
+    close_profit_abs = Column(Float)
     stake_amount = Column(Float, nullable=False)
     amount = Column(Float)
     open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
@@ -212,6 +230,7 @@ class Trade(_DECL_BASE):
     # Lowest price reached
     min_rate = Column(Float, nullable=True)
     sell_reason = Column(String, nullable=True)
+    sell_order_status = Column(String, nullable=True)
     strategy = Column(String, nullable=True)
     ticker_interval = Column(Integer, nullable=True)
 
@@ -229,6 +248,13 @@ class Trade(_DECL_BASE):
         return {
             'trade_id': self.id,
             'pair': self.pair,
+            'is_open': self.is_open,
+            'fee_open': self.fee_open,
+            'fee_open_cost': self.fee_open_cost,
+            'fee_open_currency': self.fee_open_currency,
+            'fee_close': self.fee_close,
+            'fee_close_cost': self.fee_close_cost,
+            'fee_close_currency': self.fee_close_currency,
             'open_date_hum': arrow.get(self.open_date).humanize(),
             'open_date': self.open_date.strftime("%Y-%m-%d %H:%M:%S"),
             'close_date_hum': (arrow.get(self.close_date).humanize()
@@ -236,14 +262,25 @@ class Trade(_DECL_BASE):
             'close_date': (self.close_date.strftime("%Y-%m-%d %H:%M:%S")
                            if self.close_date else None),
             'open_rate': self.open_rate,
+            'open_rate_requested': self.open_rate_requested,
+            'open_trade_price': self.open_trade_price,
             'close_rate': self.close_rate,
+            'close_rate_requested': self.close_rate_requested,
             'amount': round(self.amount, 8),
             'stake_amount': round(self.stake_amount, 8),
+            'close_profit': self.close_profit,
+            'sell_reason': self.sell_reason,
+            'sell_order_status': self.sell_order_status,
             'stop_loss': self.stop_loss,
             'stop_loss_pct': (self.stop_loss_pct * 100) if self.stop_loss_pct else None,
             'initial_stop_loss': self.initial_stop_loss,
             'initial_stop_loss_pct': (self.initial_stop_loss_pct * 100
                                       if self.initial_stop_loss_pct else None),
+            'min_rate': self.min_rate,
+            'max_rate': self.max_rate,
+            'strategy': self.strategy,
+            'ticker_interval': self.ticker_interval,
+            'open_order_id': self.open_order_id,
         }
 
     def adjust_min_max_rates(self, current_price: float) -> None:
@@ -311,7 +348,7 @@ class Trade(_DECL_BASE):
         if order_type in ('market', 'limit') and order['side'] == 'buy':
             # Update open rate and actual amount
             self.open_rate = Decimal(order['price'])
-            self.amount = Decimal(order['amount'])
+            self.amount = Decimal(order.get('filled', order['amount']))
             self.recalc_open_trade_price()
             logger.info('%s_BUY has been fulfilled for %s.', order_type.upper(), self)
             self.open_order_id = None
@@ -334,14 +371,45 @@ class Trade(_DECL_BASE):
         """
         self.close_rate = Decimal(rate)
         self.close_profit = self.calc_profit_ratio()
+        self.close_profit_abs = self.calc_profit()
         self.close_date = datetime.utcnow()
         self.is_open = False
+        self.sell_order_status = 'closed'
         self.open_order_id = None
         logger.info(
             'Marking %s as closed as the trade is fulfilled and found no open orders for it.',
             self
         )
 
+    def update_fee(self, fee_cost: float, fee_currency: Optional[str], fee_rate: Optional[float],
+                   side: str) -> None:
+        """
+        Update Fee parameters. Only acts once per side
+        """
+        if side == 'buy' and self.fee_open_currency is None:
+            self.fee_open_cost = fee_cost
+            self.fee_open_currency = fee_currency
+            if fee_rate is not None:
+                self.fee_open = fee_rate
+                # Assume close-fee will fall into the same fee category and take an educated guess
+                self.fee_close = fee_rate
+        elif side == 'sell' and self.fee_close_currency is None:
+            self.fee_close_cost = fee_cost
+            self.fee_close_currency = fee_currency
+            if fee_rate is not None:
+                self.fee_close = fee_rate
+
+    def fee_updated(self, side: str) -> bool:
+        """
+        Verify if this side (buy / sell) has already been updated
+        """
+        if side == 'buy':
+            return self.fee_open_currency is not None
+        elif side == 'sell':
+            return self.fee_close_currency is not None
+        else:
+            return False
+
     def _calc_open_trade_price(self) -> float:
         """
         Calculate the open_rate including open_fee.

freqtrade/plot/plotting.py

@@ -6,10 +6,11 @@ import pandas as pd
 
 from freqtrade.configuration import TimeRange
 from freqtrade.data.btanalysis import (calculate_max_drawdown,
-                                       combine_tickers_with_mean,
+                                       combine_dataframes_with_mean,
                                        create_cum_profit,
                                        extract_trades_of_period, load_trades)
 from freqtrade.data.converter import trim_dataframe
+from freqtrade.exchange import timeframe_to_prev_date
 from freqtrade.data.history import load_data
 from freqtrade.misc import pair_to_filename
 from freqtrade.resolvers import StrategyResolver
@@ -29,7 +30,7 @@ except ImportError:
 def init_plotscript(config):
     """
     Initialize objects needed for plotting
-    :return: Dict with tickers, trades and pairs
+    :return: Dict with candle (OHLCV) data, trades and pairs
     """
 
     if "pairs" in config:
@@ -40,7 +41,7 @@ def init_plotscript(config):
     # Set timerange to use
     timerange = TimeRange.parse_timerange(config.get("timerange"))
 
-    tickers = load_data(
+    data = load_data(
         datadir=config.get("datadir"),
         pairs=pairs,
         timeframe=config.get('ticker_interval', '5m'),
@@ -48,12 +49,22 @@ def init_plotscript(config):
         data_format=config.get('dataformat_ohlcv', 'json'),
     )
 
-    trades = load_trades(config['trade_source'],
+    no_trades = False
-                         db_url=config.get('db_url'),
+    if config.get('no_trades', False):
-                         exportfilename=config.get('exportfilename'),
+        no_trades = True
-                         )
+    elif not config['exportfilename'].is_file() and config['trade_source'] == 'file':
+        logger.warning("Backtest file is missing skipping trades.")
+        no_trades = True
+
+    trades = load_trades(
+        config['trade_source'],
+        db_url=config.get('db_url'),
+        exportfilename=config.get('exportfilename'),
+        no_trades=no_trades
+    )
     trades = trim_dataframe(trades, timerange, 'open_time')
-    return {"tickers": tickers,
+
+    return {"ohlcv": data,
             "trades": trades,
             "pairs": pairs,
             }
@@ -112,7 +123,8 @@ def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_sub
     return fig
 
 
-def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame) -> make_subplots:
+def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
+                     timeframe: str) -> make_subplots:
     """
     Add scatter points indicating max drawdown
     """
@@ -122,12 +134,12 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame) -> m
         drawdown = go.Scatter(
             x=[highdate, lowdate],
             y=[
-                df_comb.loc[highdate, 'cum_profit'],
+                df_comb.loc[timeframe_to_prev_date(timeframe, highdate), 'cum_profit'],
-                df_comb.loc[lowdate, 'cum_profit'],
+                df_comb.loc[timeframe_to_prev_date(timeframe, lowdate), 'cum_profit'],
             ],
             mode='markers',
-            name=f"Max drawdown {max_drawdown:.2f}%",
+            name=f"Max drawdown {max_drawdown * 100:.2f}%",
-            text=f"Max drawdown {max_drawdown:.2f}%",
+            text=f"Max drawdown {max_drawdown * 100:.2f}%",
             marker=dict(
                 symbol='square-open',
                 size=9,
@@ -368,10 +380,13 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
     return fig
 
 
-def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
+def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
                           trades: pd.DataFrame, timeframe: str) -> go.Figure:
     # Combine close-values for all pairs, rename columns to "pair"
-    df_comb = combine_tickers_with_mean(tickers, "close")
+    df_comb = combine_dataframes_with_mean(data, "close")
+
+    # Trim trades to available OHLCV data
+    trades = extract_trades_of_period(df_comb, trades, date_index=True)
 
     # Add combined cumulative profit
     df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
@@ -395,7 +410,7 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
 
     fig.add_trace(avgclose, 1, 1)
     fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
-    fig = add_max_drawdown(fig, 2, trades, df_comb)
+    fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe)
 
     for pair in pairs:
         profit_col = f'cum_profit_{pair}'
@@ -439,7 +454,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
     """
     From configuration provided
     - Initializes plot-script
-    - Get tickers data
+    - Get candle (OHLCV) data
     - Generate Dafaframes populated with indicators and signals based on configured strategy
     - Load trades excecuted during the selected period
     - Generate Plotly plot objects
@@ -451,19 +466,17 @@ def load_and_plot_trades(config: Dict[str, Any]):
     plot_elements = init_plotscript(config)
     trades = plot_elements['trades']
     pair_counter = 0
-    for pair, data in plot_elements["tickers"].items():
+    for pair, data in plot_elements["ohlcv"].items():
         pair_counter += 1
         logger.info("analyse pair %s", pair)
-        tickers = {}
-        tickers[pair] = data
 
-        dataframe = strategy.analyze_ticker(tickers[pair], {'pair': pair})
+        df_analyzed = strategy.analyze_ticker(data, {'pair': pair})
         trades_pair = trades.loc[trades['pair'] == pair]
-        trades_pair = extract_trades_of_period(dataframe, trades_pair)
+        trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
 
         fig = generate_candlestick_graph(
             pair=pair,
-            data=dataframe,
+            data=df_analyzed,
             trades=trades_pair,
             indicators1=config.get("indicators1", []),
             indicators2=config.get("indicators2", []),
@@ -494,7 +507,7 @@ def plot_profit(config: Dict[str, Any]) -> None:
 
     # Create an average close price of all the pairs that were involved.
     # this could be useful to gauge the overall market trend
-    fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"],
+    fig = generate_profit_graph(plot_elements["pairs"], plot_elements["ohlcv"],
                                 trades, config.get('ticker_interval', '5m'))
     store_plot_file(fig, filename='freqtrade-profit-plot.html',
                     directory=config['user_data_dir'] / "plot", auto_open=True)

freqtrade/rpc/api_server.py

@@ -2,11 +2,17 @@ import logging
 import threading
 from datetime import date, datetime
 from ipaddress import IPv4Address
-from typing import Dict, Callable, Any
+from typing import Any, Callable, Dict
 
 from arrow import Arrow
 from flask import Flask, jsonify, request
 from flask.json import JSONEncoder
+from flask_cors import CORS
+from flask_jwt_extended import (JWTManager, create_access_token,
+                                create_refresh_token, get_jwt_identity,
+                                jwt_refresh_token_required,
+                                verify_jwt_in_request_optional)
+from werkzeug.security import safe_str_cmp
 from werkzeug.serving import make_server
 
 from freqtrade.__init__ import __version__
@@ -38,9 +44,9 @@ class ArrowJSONEncoder(JSONEncoder):
 def require_login(func: Callable[[Any, Any], Any]):
 
     def func_wrapper(obj, *args, **kwargs):
-
+        verify_jwt_in_request_optional()
         auth = request.authorization
-        if auth and obj.check_auth(auth.username, auth.password):
+        if get_jwt_identity() or auth and obj.check_auth(auth.username, auth.password):
             return func(obj, *args, **kwargs)
         else:
             return jsonify({"error": "Unauthorized"}), 401
@@ -70,8 +76,8 @@ class ApiServer(RPC):
     """
 
     def check_auth(self, username, password):
-        return (username == self._config['api_server'].get('username') and
+        return (safe_str_cmp(username, self._config['api_server'].get('username')) and
-                password == self._config['api_server'].get('password'))
+                safe_str_cmp(password, self._config['api_server'].get('password')))
 
     def __init__(self, freqtrade) -> None:
         """
@@ -83,6 +89,13 @@ class ApiServer(RPC):
 
         self._config = freqtrade.config
         self.app = Flask(__name__)
+        self._cors = CORS(self.app, resources={r"/api/*": {"origins": "*"}})
+
+        # Setup the Flask-JWT-Extended extension
+        self.app.config['JWT_SECRET_KEY'] = self._config['api_server'].get(
+            'jwt_secret_key', 'super-secret')
+
+        self.jwt = JWTManager(self.app)
         self.app.json_encoder = ArrowJSONEncoder
 
         # Register application handling
@@ -148,6 +161,10 @@ class ApiServer(RPC):
         self.app.register_error_handler(404, self.page_not_found)
 
         # Actions to control the bot
+        self.app.add_url_rule(f'{BASE_URI}/token/login', 'login',
+                              view_func=self._token_login, methods=['POST'])
+        self.app.add_url_rule(f'{BASE_URI}/token/refresh', 'token_refresh',
+                              view_func=self._token_refresh, methods=['POST'])
         self.app.add_url_rule(f'{BASE_URI}/start', 'start',
                               view_func=self._start, methods=['POST'])
         self.app.add_url_rule(f'{BASE_URI}/stop', 'stop', view_func=self._stop, methods=['POST'])
@@ -173,7 +190,8 @@ class ApiServer(RPC):
                               view_func=self._show_config, methods=['GET'])
         self.app.add_url_rule(f'{BASE_URI}/ping', 'ping',
                               view_func=self._ping, methods=['GET'])
-
+        self.app.add_url_rule(f'{BASE_URI}/trades', 'trades',
+                              view_func=self._trades, methods=['GET'])
         # Combined actions and infos
         self.app.add_url_rule(f'{BASE_URI}/blacklist', 'blacklist', view_func=self._blacklist,
                               methods=['GET', 'POST'])
@@ -198,6 +216,37 @@ class ApiServer(RPC):
             'code': 404
         }), 404
 
+    @require_login
+    @rpc_catch_errors
+    def _token_login(self):
+        """
+        Handler for /token/login
+        Returns a JWT token
+        """
+        auth = request.authorization
+        if auth and self.check_auth(auth.username, auth.password):
+            keystuff = {'u': auth.username}
+            ret = {
+                'access_token': create_access_token(identity=keystuff),
+                'refresh_token': create_refresh_token(identity=keystuff),
+            }
+            return self.rest_dump(ret)
+
+        return jsonify({"error": "Unauthorized"}), 401
+
+    @jwt_refresh_token_required
+    @rpc_catch_errors
+    def _token_refresh(self):
+        """
+        Handler for /token/refresh
+        Returns a JWT token based on a JWT refresh token
+        """
+        current_user = get_jwt_identity()
+        new_token = create_access_token(identity=current_user, fresh=False)
+
+        ret = {'access_token': new_token}
+        return self.rest_dump(ret)
+
     @require_login
     @rpc_catch_errors
     def _start(self):
@@ -358,6 +407,18 @@ class ApiServer(RPC):
                                     self._config.get('fiat_display_currency', ''))
         return self.rest_dump(results)
 
+    @require_login
+    @rpc_catch_errors
+    def _trades(self):
+        """
+        Handler for /trades.
+
+        Returns the X last trades in json format
+        """
+        limit = int(request.args.get('limit', 0))
+        results = self._rpc_trade_history(limit)
+        return self.rest_dump(results)
+
     @require_login
     @rpc_catch_errors
     def _whitelist(self):

freqtrade/rpc/rpc.py

@@ -94,6 +94,7 @@ class RPC:
             'dry_run': config['dry_run'],
             'stake_currency': config['stake_currency'],
             'stake_amount': config['stake_amount'],
+            'max_open_trades': config['max_open_trades'],
             'minimal_roi': config['minimal_roi'].copy(),
             'stoploss': config['stoploss'],
             'trailing_stop': config['trailing_stop'],
@@ -103,6 +104,8 @@ class RPC:
             'ticker_interval': config['ticker_interval'],
             'exchange': config['exchange']['name'],
             'strategy': config['strategy'],
+            'forcebuy_enabled': config.get('forcebuy_enable', False),
+            'state': str(self._freqtrade.state)
         }
         return val
 
@@ -183,7 +186,7 @@ class RPC:
 
     def _rpc_daily_profit(
             self, timescale: int,
-            stake_currency: str, fiat_display_currency: str) -> List[List[Any]]:
+            stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
         today = datetime.utcnow().date()
         profit_days: Dict[date, Dict] = {}
 
@@ -197,34 +200,46 @@ class RPC:
                 Trade.close_date >= profitday,
                 Trade.close_date < (profitday + timedelta(days=1))
             ]).order_by(Trade.close_date).all()
-            curdayprofit = sum(trade.calc_profit() for trade in trades)
+            curdayprofit = sum(trade.close_profit_abs for trade in trades)
             profit_days[profitday] = {
                 'amount': f'{curdayprofit:.8f}',
                 'trades': len(trades)
             }
 
-        return [
+        data = [
-            [
+            {
-                key,
+                'date': key,
-                '{value:.8f} {symbol}'.format(
+                'abs_profit': f'{float(value["amount"]):.8f}',
-                    value=float(value['amount']),
+                'fiat_value': '{value:.3f}'.format(
-                    symbol=stake_currency
-                ),
-                '{value:.3f} {symbol}'.format(
                     value=self._fiat_converter.convert_amount(
                         value['amount'],
                         stake_currency,
                         fiat_display_currency
                     ) if self._fiat_converter else 0,
-                    symbol=fiat_display_currency
                 ),
-                '{value} trade{s}'.format(
+                'trade_count': f'{value["trades"]}',
-                    value=value['trades'],
+            }
-                    s='' if value['trades'] < 2 else 's'
-                ),
-            ]
             for key, value in profit_days.items()
         ]
+        return {
+            'stake_currency': stake_currency,
+            'fiat_display_currency': fiat_display_currency,
+            'data': data
+        }
+
+    def _rpc_trade_history(self, limit: int) -> Dict:
+        """ Returns the X last trades """
+        if limit > 0:
+            trades = Trade.get_trades().order_by(Trade.id.desc()).limit(limit)
+        else:
+            trades = Trade.get_trades().order_by(Trade.id.desc()).all()
+
+        output = [trade.to_json() for trade in trades]
+
+        return {
+            "trades": output,
+            "trades_count": len(output)
+        }
 
     def _rpc_trade_statistics(
             self, stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
@@ -246,8 +261,8 @@ class RPC:
                 durations.append((trade.close_date - trade.open_date).total_seconds())
 
             if not trade.is_open:
-                profit_ratio = trade.calc_profit_ratio()
+                profit_ratio = trade.close_profit
-                profit_closed_coin.append(trade.calc_profit())
+                profit_closed_coin.append(trade.close_profit_abs)
                 profit_closed_ratio.append(profit_ratio)
             else:
                 # Get current rate
@@ -530,5 +545,5 @@ class RPC:
     def _rpc_edge(self) -> List[Dict[str, Any]]:
         """ Returns information related to Edge """
         if not self._freqtrade.edge:
-            raise RPCException(f'Edge is not enabled.')
+            raise RPCException('Edge is not enabled.')
         return self._freqtrade.edge.accepted_pairs()

freqtrade/rpc/telegram.py

@@ -172,7 +172,8 @@ class Telegram(RPC):
                             ' / {profit_fiat:.3f} {fiat_currency})`').format(**msg)
 
         elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
-            message = "*{exchange}:* Cancelling Open Sell Order for {pair}".format(**msg)
+            message = ("*{exchange}:* Cancelling Open Sell Order "
+                       "for {pair}. Reason: {reason}").format(**msg)
 
         elif msg['type'] == RPCMessageType.STATUS_NOTIFICATION:
             message = '*Status:* `{status}`'.format(**msg)
@@ -225,11 +226,15 @@ class Telegram(RPC):
                     # Adding stoploss and stoploss percentage only if it is not None
                     "*Stoploss:* `{stop_loss:.8f}` " +
                     ("`({stop_loss_pct:.2f}%)`" if r['stop_loss_pct'] else ""),
-
-                    "*Open Order:* `{open_order}`" if r['open_order'] else ""
                 ]
+                if r['open_order']:
+                    if r['sell_order_status']:
+                        lines.append("*Open Order:* `{open_order}` - `{sell_order_status}`")
+                    else:
+                        lines.append("*Open Order:* `{open_order}`")
+
                 # Filter empty lines using list-comprehension
-                messages.append("\n".join([l for l in lines if l]).format(**r))
+                messages.append("\n".join([line for line in lines if line]).format(**r))
 
             for msg in messages:
                 self._send_msg(msg)
@@ -275,14 +280,18 @@ class Telegram(RPC):
                 stake_cur,
                 fiat_disp_cur
             )
-            stats_tab = tabulate(stats,
+            stats_tab = tabulate(
-                                 headers=[
+                [[day['date'],
-                                     'Day',
+                  f"{day['abs_profit']} {stats['stake_currency']}",
-                                     f'Profit {stake_cur}',
+                  f"{day['fiat_value']} {stats['fiat_display_currency']}",
-                                     f'Profit {fiat_disp_cur}',
+                  f"{day['trade_count']} trades"] for day in stats['data']],
-                                     f'Trades'
+                headers=[
-                                 ],
+                    'Day',
-                                 tablefmt='simple')
+                    f'Profit {stake_cur}',
+                    f'Profit {fiat_disp_cur}',
+                    'Trades',
+                ],
+                tablefmt='simple')
             message = f'<b>Daily Profit over the last {timescale} days</b>:\n<pre>{stats_tab}</pre>'
             self._send_msg(message, parse_mode=ParseMode.HTML)
         except RPCException as e:
@@ -578,7 +587,7 @@ class Telegram(RPC):
                   "*/whitelist:* `Show current whitelist` \n" \
                   "*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs " \
                   "to the blacklist.` \n" \
-                  "*/edge:* `Shows validated pairs by Edge if it is enabeld` \n" \
+                  "*/edge:* `Shows validated pairs by Edge if it is enabled` \n" \
                   "*/help:* `This help message`\n" \
                   "*/version:* `Show version`"
 
@@ -620,10 +629,12 @@ class Telegram(RPC):
             f"*Mode:* `{'Dry-run' if val['dry_run'] else 'Live'}`\n"
             f"*Exchange:* `{val['exchange']}`\n"
             f"*Stake per trade:* `{val['stake_amount']} {val['stake_currency']}`\n"
+            f"*Max open Trades:* `{val['max_open_trades']}`\n"
             f"*Minimum ROI:* `{val['minimal_roi']}`\n"
             f"{sl_info}"
             f"*Ticker Interval:* `{val['ticker_interval']}`\n"
-            f"*Strategy:* `{val['strategy']}`"
+            f"*Strategy:* `{val['strategy']}`\n"
+            f"*Current state:* `{val['state']}`"
         )
 
     def _send_msg(self, msg: str, parse_mode: ParseMode = ParseMode.MARKDOWN) -> None:

freqtrade/rpc/webhook.py

@@ -47,9 +47,9 @@ class Webhook(RPC):
                 valuedict = self._config['webhook'].get('webhooksell', None)
             elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
                 valuedict = self._config['webhook'].get('webhooksellcancel', None)
-            elif msg['type'] in(RPCMessageType.STATUS_NOTIFICATION,
+            elif msg['type'] in (RPCMessageType.STATUS_NOTIFICATION,
-                                RPCMessageType.CUSTOM_NOTIFICATION,
+                                 RPCMessageType.CUSTOM_NOTIFICATION,
-                                RPCMessageType.WARNING_NOTIFICATION):
+                                 RPCMessageType.WARNING_NOTIFICATION):
                 valuedict = self._config['webhook'].get('webhookstatus', None)
             else:
                 raise NotImplementedError('Unknown message type: {}'.format(msg['type']))

freqtrade/state.py

@@ -14,6 +14,9 @@ class State(Enum):
     STOPPED = 2
     RELOAD_CONF = 3
 
+    def __str__(self):
+        return f"{self.name.lower()}"
+
 
 class RunMode(Enum):
     """

freqtrade/strategy/interface.py

@@ -3,18 +3,21 @@ IStrategy interface
 This module defines the interface to apply for strategies
 """
 import logging
+import warnings
 from abc import ABC, abstractmethod
 from datetime import datetime, timezone
 from enum import Enum
-from typing import Dict, List, NamedTuple, Optional, Tuple
+from typing import Dict, NamedTuple, Optional, Tuple
-import warnings
 
 import arrow
 from pandas import DataFrame
 
 from freqtrade.data.dataprovider import DataProvider
+from freqtrade.exceptions import StrategyError
 from freqtrade.exchange import timeframe_to_minutes
 from freqtrade.persistence import Trade
+from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
+from freqtrade.typing import ListPairsWithTimeframes
 from freqtrade.wallets import Wallets
 
 
@@ -59,7 +62,7 @@ class IStrategy(ABC):
     Attributes you can use:
         minimal_roi -> Dict: Minimal ROI designed for the strategy
         stoploss -> float: optimal stoploss designed for the strategy
-        ticker_interval -> str: value of the ticker interval to use for the strategy
+        ticker_interval -> str: value of the timeframe (ticker interval) to use with the strategy
     """
     # Strategy interface version
     # Default to version 2
@@ -125,7 +128,7 @@ class IStrategy(ABC):
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
         Populate indicators that will be used in the Buy and Sell strategy
-        :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
         """
@@ -148,7 +151,43 @@ class IStrategy(ABC):
         :return: DataFrame with sell column
         """
 
-    def informative_pairs(self) -> List[Tuple[str, str]]:
+    def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+        """
+        Check buy timeout function callback.
+        This method can be used to override the buy-timeout.
+        It is called whenever a limit buy order has been created,
+        and is not yet fully filled.
+        Configuration options in `unfilledtimeout` will be verified before this,
+        so ensure to set these timeouts high enough.
+
+        When not implemented by a strategy, this simply returns False.
+        :param pair: Pair the trade is for
+        :param trade: trade object.
+        :param order: Order dictionary as returned from CCXT.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the buy-order is cancelled.
+        """
+        return False
+
+    def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
+        """
+        Check sell timeout function callback.
+        This method can be used to override the sell-timeout.
+        It is called whenever a limit sell order has been created,
+        and is not yet fully filled.
+        Configuration options in `unfilledtimeout` will be verified before this,
+        so ensure to set these timeouts high enough.
+
+        When not implemented by a strategy, this simply returns False.
+        :param pair: Pair the trade is for
+        :param trade: trade object.
+        :param order: Order dictionary as returned from CCXT.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return bool: When True is returned, then the sell-order is cancelled.
+        """
+        return False
+
+    def informative_pairs(self) -> ListPairsWithTimeframes:
         """
         Define additional, informative pair/interval combinations to be cached from the exchange.
         These pair/interval combinations are non-tradeable, unless they are part
@@ -200,11 +239,11 @@ class IStrategy(ABC):
 
     def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
-        Parses the given ticker history and returns a populated DataFrame
+        Parses the given candle (OHLCV) data and returns a populated DataFrame
         add several TA indicators and buy signal to it
-        :param dataframe: Dataframe containing ticker data
+        :param dataframe: Dataframe containing data from exchange
         :param metadata: Metadata dictionary with additional data (e.g. 'pair')
-        :return: DataFrame with ticker data and indicator data
+        :return: DataFrame of candle (OHLCV) data with indicator data and signals added
         """
         logger.debug("TA Analysis Launched")
         dataframe = self.advise_indicators(dataframe, metadata)
@@ -214,12 +253,12 @@ class IStrategy(ABC):
 
     def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
-        Parses the given ticker history and returns a populated DataFrame
+        Parses the given candle (OHLCV) data and returns a populated DataFrame
         add several TA indicators and buy signal to it
         WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
-        :param dataframe: Dataframe containing ticker data
+        :param dataframe: Dataframe containing data from exchange
         :param metadata: Metadata dictionary with additional data (e.g. 'pair')
-        :return: DataFrame with ticker data and indicator data
+        :return: DataFrame of candle (OHLCV) data with indicator data and signals added
         """
         pair = str(metadata.get('pair'))
 
@@ -241,8 +280,25 @@ class IStrategy(ABC):
 
         return dataframe
 
-    def get_signal(self, pair: str, interval: str,
+    @staticmethod
-                   dataframe: DataFrame) -> Tuple[bool, bool]:
+    def preserve_df(dataframe: DataFrame) -> Tuple[int, float, datetime]:
+        """ keep some data for dataframes """
+        return len(dataframe), dataframe["close"].iloc[-1], dataframe["date"].iloc[-1]
+
+    @staticmethod
+    def assert_df(dataframe: DataFrame, df_len: int, df_close: float, df_date: datetime):
+        """ make sure data is unmodified """
+        message = ""
+        if df_len != len(dataframe):
+            message = "length"
+        elif df_close != dataframe["close"].iloc[-1]:
+            message = "last close price"
+        elif df_date != dataframe["date"].iloc[-1]:
+            message = "last date"
+        if message:
+            raise StrategyError(f"Dataframe returned from strategy has mismatching {message}.")
+
+    def get_signal(self, pair: str, interval: str, dataframe: DataFrame) -> Tuple[bool, bool]:
         """
         Calculates current signal based several technical analysis indicators
         :param pair: pair in format ANT/BTC
@@ -251,31 +307,27 @@ class IStrategy(ABC):
         :return: (Buy, Sell) A bool-tuple indicating buy/sell signal
         """
         if not isinstance(dataframe, DataFrame) or dataframe.empty:
-            logger.warning('Empty ticker history for pair %s', pair)
+            logger.warning('Empty candle (OHLCV) data for pair %s', pair)
             return False, False
 
+        latest_date = dataframe['date'].max()
         try:
-            dataframe = self._analyze_ticker_internal(dataframe, {'pair': pair})
+            df_len, df_close, df_date = self.preserve_df(dataframe)
-        except ValueError as error:
+            dataframe = strategy_safe_wrapper(
-            logger.warning(
+                self._analyze_ticker_internal, message=""
-                'Unable to analyze ticker for pair %s: %s',
+                )(dataframe, {'pair': pair})
-                pair,
+            self.assert_df(dataframe, df_len, df_close, df_date)
-                str(error)
+        except StrategyError as error:
-            )
+            logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
-            return False, False
+
-        except Exception as error:
-            logger.exception(
-                'Unexpected error when analyzing ticker for pair %s: %s',
-                pair,
-                str(error)
-            )
             return False, False
 
         if dataframe.empty:
             logger.warning('Empty dataframe for pair %s', pair)
             return False, False
 
-        latest = dataframe.iloc[-1]
+        latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
+
         signal_date = arrow.get(latest['date'])
         interval_minutes = timeframe_to_minutes(interval)
 
@@ -446,19 +498,22 @@ class IStrategy(ABC):
         else:
             return current_profit > roi
 
-    def tickerdata_to_dataframe(self, tickerdata: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
+    def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
         """
-        Creates a dataframe and populates indicators for given ticker data
+        Creates a dataframe and populates indicators for given candle (OHLCV) data
         Used by optimize operations only, not during dry / live runs.
+        Using .copy() to get a fresh copy of the dataframe for every strategy run.
+        Has positive effects on memory usage for whatever reason - also when
+        using only one strategy.
         """
-        return {pair: self.advise_indicators(pair_data, {'pair': pair})
+        return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair})
-                for pair, pair_data in tickerdata.items()}
+                for pair, pair_data in data.items()}
 
     def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
         Populate indicators that will be used in the Buy and Sell strategy
         This method should not be overridden.
-        :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
         """

freqtrade/strategy/strategy_wrapper.py

@@ -0,0 +1,35 @@
+import logging
+
+from freqtrade.exceptions import StrategyError
+
+logger = logging.getLogger(__name__)
+
+
+def strategy_safe_wrapper(f, message: str = "", default_retval=None):
+    """
+    Wrapper around user-provided methods and functions.
+    Caches all exceptions and returns either the default_retval (if it's not None) or raises
+    a StrategyError exception, which then needs to be handled by the calling method.
+    """
+    def wrapper(*args, **kwargs):
+        try:
+            return f(*args, **kwargs)
+        except ValueError as error:
+            logger.warning(
+                f"{message}"
+                f"Strategy caused the following exception: {error}"
+                f"{f}"
+            )
+            if default_retval is None:
+                raise StrategyError(str(error)) from error
+            return default_retval
+        except Exception as error:
+            logger.exception(
+                f"{message}"
+                f"Unexpected error {error} calling {f}"
+            )
+            if default_retval is None:
+                raise StrategyError(str(error)) from error
+            return default_retval
+
+    return wrapper

freqtrade/templates/base_config.json.j2

@@ -6,6 +6,7 @@
     "fiat_display_currency": "{{ fiat_display_currency }}",
     "ticker_interval": "{{ ticker_interval }}",
     "dry_run": {{ dry_run | lower }},
+    "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
         "buy": 10,
         "sell": 30

freqtrade/templates/base_hyperopt.py.j2

@@ -66,6 +66,9 @@ class {{ hyperopt }}(IHyperOpt):
                         dataframe['close'], dataframe['sar']
                     ))
 
+            # Check that the candle had volume
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),
@@ -111,6 +114,9 @@ class {{ hyperopt }}(IHyperOpt):
                         dataframe['sar'], dataframe['close']
                     ))
 
+            # Check that the candle had volume
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),

freqtrade/templates/base_strategy.py.j2

@@ -99,7 +99,7 @@ class {{ strategy }}(IStrategy):
         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
         """
@@ -137,3 +137,4 @@ class {{ strategy }}(IStrategy):
             ),
             'sell'] = 1
         return dataframe
+    {{ additional_methods | indent(4) }}

freqtrade/templates/sample_hyperopt.py

@@ -78,6 +78,9 @@ class SampleHyperOpt(IHyperOpt):
                         dataframe['close'], dataframe['sar']
                     ))
 
+            # Check that volume is not 0
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),
@@ -138,6 +141,9 @@ class SampleHyperOpt(IHyperOpt):
                         dataframe['sar'], dataframe['close']
                     ))
 
+            # Check that volume is not 0
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),

freqtrade/templates/sample_hyperopt_advanced.py

@@ -93,6 +93,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
                         dataframe['close'], dataframe['sar']
                     ))
 
+            # Check that volume is not 0
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),
@@ -153,6 +156,9 @@ class AdvancedSampleHyperOpt(IHyperOpt):
                         dataframe['sar'], dataframe['close']
                     ))
 
+            # Check that volume is not 0
+            conditions.append(dataframe['volume'] > 0)
+
             if conditions:
                 dataframe.loc[
                     reduce(lambda x, y: x & y, conditions),

freqtrade/templates/sample_strategy.py

@@ -116,7 +116,7 @@ class SampleStrategy(IStrategy):
         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
         """

freqtrade/templates/subtemplates/strategy_methods_advanced.j2

@@ -0,0 +1,40 @@
+
+def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
+    """
+    Check buy timeout function callback.
+    This method can be used to override the buy-timeout.
+    It is called whenever a limit buy order has been created,
+    and is not yet fully filled.
+    Configuration options in `unfilledtimeout` will be verified before this,
+    so ensure to set these timeouts high enough.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, this simply returns False.
+    :param pair: Pair the trade is for
+    :param trade: trade object.
+    :param order: Order dictionary as returned from CCXT.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return bool: When True is returned, then the buy-order is cancelled.
+    """
+    return False
+
+def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
+    """
+    Check sell timeout function callback.
+    This method can be used to override the sell-timeout.
+    It is called whenever a limit sell order has been created,
+    and is not yet fully filled.
+    Configuration options in `unfilledtimeout` will be verified before this,
+    so ensure to set these timeouts high enough.
+
+    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
+
+    When not implemented by a strategy, this simply returns False.
+    :param pair: Pair the trade is for
+    :param trade: trade object.
+    :param order: Order dictionary as returned from CCXT.
+    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+    :return bool: When True is returned, then the sell-order is cancelled.
+    """
+    return False

freqtrade/typing.py

@@ -0,0 +1,8 @@
+"""
+Common Freqtrade types
+"""
+
+from typing import List, Tuple
+
+# List of pairs with their timeframes
+ListPairsWithTimeframes = List[Tuple[str, str]]

freqtrade/worker.py

@@ -37,9 +37,7 @@ class Worker:
         self._heartbeat_msg: float = 0
 
         # Tell systemd that we completed initialization phase
-        if self._sd_notify:
+        self._notify("READY=1")
-            logger.debug("sd_notify: READY=1")
-            self._sd_notify.notify("READY=1")
 
     def _init(self, reconfig: bool) -> None:
         """
@@ -60,6 +58,15 @@ class Worker:
         self._sd_notify = sdnotify.SystemdNotifier() if \
             self._config.get('internals', {}).get('sd_notify', False) else None
 
+    def _notify(self, message: str) -> None:
+        """
+        Removes the need to verify in all occurances if sd_notify is enabled
+        :param message: Message to send to systemd if it's enabled.
+        """
+        if self._sd_notify:
+            logger.debug(f"sd_notify: {message}")
+            self._sd_notify.notify(message)
+
     def run(self) -> None:
         state = None
         while True:
@@ -89,17 +96,13 @@ class Worker:
 
         if state == State.STOPPED:
             # Ping systemd watchdog before sleeping in the stopped state
-            if self._sd_notify:
+            self._notify("WATCHDOG=1\nSTATUS=State: STOPPED.")
-                logger.debug("sd_notify: WATCHDOG=1\\nSTATUS=State: STOPPED.")
-                self._sd_notify.notify("WATCHDOG=1\nSTATUS=State: STOPPED.")
 
             self._throttle(func=self._process_stopped, throttle_secs=self._throttle_secs)
 
         elif state == State.RUNNING:
             # Ping systemd watchdog before throttling
-            if self._sd_notify:
+            self._notify("WATCHDOG=1\nSTATUS=State: RUNNING.")
-                logger.debug("sd_notify: WATCHDOG=1\\nSTATUS=State: RUNNING.")
-                self._sd_notify.notify("WATCHDOG=1\nSTATUS=State: RUNNING.")
 
             self._throttle(func=self._process_running, throttle_secs=self._throttle_secs)
 
@@ -131,8 +134,7 @@ class Worker:
         return result
 
     def _process_stopped(self) -> None:
-        # Maybe do here something in the future...
+        self.freqtrade.process_stopped()
-        pass
 
     def _process_running(self) -> None:
         try:
@@ -155,9 +157,7 @@ class Worker:
         replaces it with the new instance
         """
         # Tell systemd that we initiated reconfiguration
-        if self._sd_notify:
+        self._notify("RELOADING=1")
-            logger.debug("sd_notify: RELOADING=1")
-            self._sd_notify.notify("RELOADING=1")
 
         # Clean up current freqtrade modules
         self.freqtrade.cleanup()
@@ -168,15 +168,11 @@ class Worker:
         self.freqtrade.notify_status('config reloaded')
 
         # Tell systemd that we completed reconfiguration
-        if self._sd_notify:
+        self._notify("READY=1")
-            logger.debug("sd_notify: READY=1")
-            self._sd_notify.notify("READY=1")
 
     def exit(self) -> None:
         # Tell systemd that we are exiting now
-        if self._sd_notify:
+        self._notify("STOPPING=1")
-            logger.debug("sd_notify: STOPPING=1")
-            self._sd_notify.notify("STOPPING=1")
 
         if self.freqtrade:
             self.freqtrade.notify_status('process died')

mkdocs.yml

@@ -24,6 +24,7 @@ nav:
         - Plotting: plotting.md
     - SQL Cheatsheet: sql_cheatsheet.md
     - Advanced Post-installation Tasks: advanced-setup.md
+    - Advanced Strategy: strategy-advanced.md
     - Advanced Hyperopt: advanced-hyperopt.md
     - Sandbox Testing: sandbox-testing.md
     - Deprecated Features: deprecated.md

requirements-common.txt

@@ -1,18 +1,18 @@
 # requirements without requirements installable via conda
 # mainly used for Raspberry pi installs
-ccxt==1.23.81
+ccxt==1.27.91
-SQLAlchemy==1.3.13
+SQLAlchemy==1.3.17
-python-telegram-bot==12.4.2
+python-telegram-bot==12.7
-arrow==0.15.5
+arrow==0.15.6
-cachetools==4.0.0
+cachetools==4.1.0
 requests==2.23.0
-urllib3==1.25.8
+urllib3==1.25.9
 wrapt==1.12.1
 jsonschema==3.2.0
-TA-Lib==0.4.17
+TA-Lib==0.4.18
-tabulate==0.8.6
+tabulate==0.8.7
 pycoingecko==1.2.0
-jinja2==2.11.1
+jinja2==2.11.2
 
 # find first, C search in arrays
 py_find_1st==1.1.4
@@ -24,10 +24,12 @@ python-rapidjson==0.9.1
 sdnotify==0.3.2
 
 # Api server
-flask==1.1.1
+flask==1.1.2
+flask-jwt-extended==3.24.1
+flask-cors==3.0.8
 
 # Support for colorized terminal output
 colorama==0.4.3
 # Building config files interactively
-questionary==1.5.1
+questionary==1.5.2
-prompt-toolkit==3.0.4
+prompt-toolkit==3.0.5

requirements-dev.txt

@@ -3,15 +3,15 @@
 -r requirements-plot.txt
 -r requirements-hyperopt.txt
 
-coveralls==1.11.1
+coveralls==2.0.0
-flake8==3.7.9
+flake8==3.8.1
 flake8-type-annotations==0.1.0
-flake8-tidy-imports==4.0.0
+flake8-tidy-imports==4.1.0
-mypy==0.761
+mypy==0.770
-pytest==5.3.5
+pytest==5.4.2
-pytest-asyncio==0.10.0
+pytest-asyncio==0.12.0
 pytest-cov==2.8.1
-pytest-mock==2.0.0
+pytest-mock==3.1.0
 pytest-random-order==1.0.4
 
 # Convert jupyter notebooks to markdown documents

requirements-hyperopt.txt

@@ -6,4 +6,5 @@ scipy==1.4.1
 scikit-learn==0.22.2.post1
 scikit-optimize==0.7.4
 filelock==3.0.12
-joblib==0.14.1
+joblib==0.15.1
+progressbar2==3.51.3

requirements-plot.txt

@@ -1,5 +1,5 @@
 # Include all requirements to run the bot.
 -r requirements.txt
 
-plotly==4.5.3
+plotly==4.7.1
 

requirements.txt

@@ -1,5 +1,5 @@
 # Load common requirements
 -r requirements-common.txt
 
-numpy==1.18.1
+numpy==1.18.4
-pandas==1.0.1
+pandas==1.0.3