diff --git a/docs/edge.md b/docs/edge.md index b208cb318..a4acffc44 100644 --- a/docs/edge.md +++ b/docs/edge.md @@ -3,159 +3,198 @@ This page explains how to use Edge Positioning module in your bot in order to enter into a trade only if the trade has a reasonable win rate and risk reward ratio, and consequently adjust your position size and stoploss. !!! Warning - Edge positioning is not compatible with dynamic whitelist. it overrides dynamic whitelist. + Edge positioning is not compatible with dynamic whitelist. If enabled, it overrides the dynamic whitelist option. !!! Note - Edge won't consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else will be ignored in its calculation. + Edge does not consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else are ignored in its calculation. ## Introduction -Trading is all about probability. No one can claim that he has a strategy working all the time. You have to assume that sometimes you lose.

-But it doesn't mean there is no rule, it only means rules should work "most of the time". Let's play a game: we toss a coin, heads: I give you 10$, tails: You give me 10$. Is it an interesting game ? no, it is quite boring, isn't it?

-But let's say the probability that we have heads is 80%, and the probability that we have tails is 20%. Now it is becoming interesting ... -That means 10$ x 80% versus 10$ x 20%. 8$ versus 2$. That means over time you will win 8$ risking only 2$ on each toss of coin.

-Let's complicate it more: you win 80% of the time but only 2$, I win 20% of the time but 8$. The calculation is: 80% * 2$ versus 20% * 8$. It is becoming boring again because overtime you win $1.6$ (80% x 2$) and me $1.6 (20% * 8$) too.

-The question is: How do you calculate that? how do you know if you wanna play? +Trading is all about probability. No one can claim that he has a strategy working all the time. You have to assume that sometimes you lose. + +But it doesn't mean there is no rule, it only means rules should work "most of the time". Let's play a game: we toss a coin, heads: I give you 10$, tails: you give me 10$. Is it an interesting game? No, it's quite boring, isn't it? + +But let's say the probability that we have heads is 80% (because our coin has the displaced distribution of mass or other defect), and the probability that we have tails is 20%. Now it is becoming interesting... + +That means 10$ X 80% versus 10$ X 20%. 8$ versus 2$. That means over time you will win 8$ risking only 2$ on each toss of coin. + +Let's complicate it more: you win 80% of the time but only 2$, I win 20% of the time but 8$. The calculation is: 80% X 2$ versus 20% X 8$. It is becoming boring again because overtime you win $1.6$ (80% X 2$) and me $1.6 (20% X 8$) too. + +The question is: How do you calculate that? How do you know if you wanna play? + The answer comes to two factors: - Win Rate - Risk Reward Ratio - ### Win Rate -Means over X trades what is the percentage of winning trades to total number of trades (note that we don't consider how much you gained but only If you won or not). +Win Rate (*W*) is is the mean over some amount of trades (*N*) what is the percentage of winning trades to total number of trades (note that we don't consider how much you gained but only if you won or not). + W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N -`W = (Number of winning trades) / (Total number of trades)` +Complementary Loss Rate (*L*) is defined as + + L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N + +or, which is the same, as + + R = 1 – W ### Risk Reward Ratio -Risk Reward Ratio is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose: +Risk Reward Ratio (*R*) is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose: -`R = Profit / Loss` + R = Profit / Loss Over time, on many trades, you can calculate your risk reward by dividing your average profit on winning trades by your average loss on losing trades: -`Average profit = (Sum of profits) / (Number of winning trades)` + Average profit = (Sum of profits) / (Number of winning trades) -`Average loss = (Sum of losses) / (Number of losing trades)` + Average loss = (Sum of losses) / (Number of losing trades) -`R = (Average profit) / (Average loss)` + R = (Average profit) / (Average loss) ### Expectancy +At this point we can combine *W* and *R* to create an expectancy ratio. This is a simple process of multiplying the risk reward ratio by the percentage of winning trades and subtracting the percentage of losing trades, which is calculated as follows: -At this point we can combine W and R to create an expectancy ratio. This is a simple process of multiplying the risk reward ratio by the percentage of winning trades, and subtracting the percentage of losing trades, which is calculated as follows: - -Expectancy Ratio = (Risk Reward Ratio x Win Rate) – Loss Rate + Expectancy Ratio = (Risk Reward Ratio X Win Rate) – Loss Rate = (R X W) – L So lets say your Win rate is 28% and your Risk Reward Ratio is 5: -`Expectancy = (5 * 0.28) - 0.72 = 0.68` + 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 losers. 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 .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. 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. -You can also use this number to evaluate the effectiveness of modifications to this system. +You can also use this value to evaluate the effectiveness of modifications to this system. -**NOTICE:** It's important to keep in mind that Edge is testing your expectancy using historical data , there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology, but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades. +**NOTICE:** It's important to keep in mind that Edge is testing your expectancy using historical data, there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology, but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades. ## How does it work? -If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over X trades for each stoploss. Here is an example: +If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over *N* trades for each stoploss. Here is an example: | Pair | Stoploss | Win Rate | Risk Reward Ratio | Expectancy | |----------|:-------------:|-------------:|------------------:|-----------:| -| XZC/ETH | -0.03 | 0.52 |1.359670 | 0.228 | | XZC/ETH | -0.01 | 0.50 |1.176384 | 0.088 | | XZC/ETH | -0.02 | 0.51 |1.115941 | 0.079 | +| XZC/ETH | -0.03 | 0.52 |1.359670 | 0.228 | +| XZC/ETH | -0.04 | 0.51 |1.234539 | 0.117 | The goal here is to find the best stoploss for the strategy in order to have the maximum expectancy. In the above example stoploss at 3% leads to the maximum expectancy according to historical data. -Edge then forces stoploss to your strategy dynamically. +Edge module then forces stoploss value it evaluated to your strategy dynamically. ### Position size -Edge dictates the stake amount for each trade to the bot according to the following factors: +Edge also dictates the stake amount for each trade to the bot according to the following factors: - Allowed capital at risk - Stoploss Allowed capital at risk is calculated as follows: -**allowed capital at risk** = **capital_available_percentage** X **allowed risk per trade** + Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade) -**Stoploss** is calculated as described above against historical data. +Stoploss is calculated as described above against historical data. Your position size then will be: -**position size** = **allowed capital at risk** / **stoploss** + Position size = (Allowed capital at risk) / Stoploss -Example:
-Let's say the stake currency is ETH and you have 10 ETH on the exchange, your **capital_available_percentage** is 50% and you would allow 1% of risk for each trade. thus your available capital for trading is **10 x 0.5 = 5 ETH** and allowed capital at risk would be **5 x 0.01 = 0.05 ETH**.
-Let's assume Edge has calculated that for **XLM/ETH** market your stoploss should be at 2%. So your position size will be **0.05 / 0.02 = 2.5ETH**.
-Bot takes a position of 2.5ETH on XLM/ETH (call it trade 1). Up next, you receive another buy signal while trade 1 is still open. This time on BTC/ETH market. Edge calculated stoploss for this market at 4%. So your position size would be 0.05 / 0.04 = 1.25ETH (call it trade 2).
-Note that available capital for trading didn’t change for trade 2 even if you had already trade 1. The available capital doesn’t mean the free amount on your wallet.
-Now you have two trades open. The Bot receives yet another buy signal for another market: **ADA/ETH**. This time the stoploss is calculated at 1%. So your position size is **0.05 / 0.01 = 5ETH**. But there are already 4ETH blocked in two previous trades. So the position size for this third trade would be 1ETH.
-Available capital doesn’t change before a position is sold. Let’s assume that trade 1 receives a sell signal and it is sold with a profit of 1ETH. Your total capital on exchange would be 11 ETH and the available capital for trading becomes 5.5ETH.
-So the Bot receives another buy signal for trade 4 with a stoploss at 2% then your position size would be **0.055 / 0.02 = 2.75**. +Example: + +Let's say the stake currency is ETH and you have 10 ETH on the exchange, your capital available percentage is 50% and you would allow 1% of risk for each trade. thus your available capital for trading is **10 x 0.5 = 5 ETH** and allowed capital at risk would be **5 x 0.01 = 0.05 ETH**. + +Let's assume Edge has calculated that for **XLM/ETH** market your stoploss should be at 2%. So your position size will be **0.05 / 0.02 = 2.5 ETH**. + +Bot takes a position of 2.5 ETH on XLM/ETH (call it trade 1). Up next, you receive another buy signal while trade 1 is still open. This time on **BTC/ETH** market. Edge calculated stoploss for this market at 4%. So your position size would be 0.05 / 0.04 = 1.25 ETH (call it trade 2). + +Note that available capital for trading didn’t change for trade 2 even if you had already trade 1. The available capital doesn’t mean the free amount on your wallet. + +Now you have two trades open. The bot receives yet another buy signal for another market: **ADA/ETH**. This time the stoploss is calculated at 1%. So your position size is **0.05 / 0.01 = 5 ETH**. But there are already 3.75 ETH blocked in two previous trades. So the position size for this third trade would be **5 – 3.75 = 1.25 ETH**. + +Available capital doesn’t change before a position is sold. Let’s assume that trade 1 receives a sell signal and it is sold with a profit of 1 ETH. Your total capital on exchange would be 11 ETH and the available capital for trading becomes 5.5 ETH. + +So the Bot receives another buy signal for trade 4 with a stoploss at 2% then your position size would be **0.055 / 0.02 = 2.75 ETH**. ## Configurations -Edge has following configurations: +Edge module has following configuration options: #### enabled -If true, then Edge will run periodically.
-(default to false) +If true, then Edge will run periodically. + +(defaults to false) #### process_throttle_secs -How often should Edge run in seconds?
-(default to 3600 so one hour) +How often should Edge run in seconds? + +(defaults to 3600 so one hour) #### calculate_since_number_of_days Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy -Note that it downloads historical data so increasing this number would lead to slowing down the bot.
-(default to 7) +Note that it downloads historical data so increasing this number would lead to slowing down the bot. + +(defaults to 7) #### capital_available_percentage -This is the percentage of the total capital on exchange in stake currency.
-As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital.
-(default to 0.5) +This is the percentage of the total capital on exchange in stake currency. + +As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital. + +(defaults to 0.5) #### allowed_risk -Percentage of allowed risk per trade.
-(default to 0.01 [1%]) +Percentage of allowed risk per trade. + +(defaults to 0.01 so 1%) #### stoploss_range_min -Minimum stoploss.
-(default to -0.01) +Minimum stoploss. + +(defaults to -0.01) #### stoploss_range_max -Maximum stoploss.
-(default to -0.10) +Maximum stoploss. + +(defaults to -0.10) #### stoploss_range_step -As an example if this is set to -0.01 then Edge will test the strategy for [-0.01, -0,02, -0,03 ..., -0.09, -0.10] ranges. -Note than having a smaller step means having a bigger range which could lead to slow calculation.
-if you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10.
-(default to -0.01) +As an example if this is set to -0.01 then Edge will test the strategy for \[-0.01, -0,02, -0,03 ..., -0.09, -0.10\] ranges. +Note than having a smaller step means having a bigger range which could lead to slow calculation. + +If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. + +(defaults to -0.01) #### minimum_winrate -It filters pairs which don't have at least minimum_winrate. -This comes handy if you want to be conservative and don't comprise win rate in favor of risk reward ratio.
-(default to 0.60) +It filters out pairs which don't have at least minimum_winrate. + +This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. + +(defaults to 0.60) #### minimum_expectancy -It filters paris which have an expectancy lower than this number . -Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return.
-(default to 0.20) +It filters out pairs which have the expectancy lower than this number. + +Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. + +(defaults to 0.20) #### min_trade_number -When calculating W and 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. 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.
-(default to 10, it is highly recommended not to decrease this 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. + +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. + +(defaults to 10, it is highly recommended not to decrease this number) #### 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.
-**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).
-(default to 1 day, 1440 = 60 * 24) +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. + +**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.). + +(defaults to 1 day, i.e. to 60 * 24 = 1440 minutes) #### 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.
-(default to false) +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. + +(defaults to false) ## Running Edge independently @@ -199,14 +238,14 @@ python3 ./freqtrade/main.py edge --stoplosses=-0.01,-0.1,-0.001 #min,max,step python3 ./freqtrade/main.py edge --timerange=20181110-20181113 ``` -Doing --timerange=-200 will get the last 200 timeframes from your inputdata. You can also specify specific dates, or a range span indexed by start and stop. +Doing `--timerange=-200` will get the last 200 timeframes from your inputdata. You can also specify specific dates, or a range span indexed by start and stop. The full timerange specification: -* Use last 123 tickframes of data: --timerange=-123 -* Use first 123 tickframes of data: --timerange=123- -* Use tickframes from line 123 through 456: --timerange=123-456 -* Use tickframes till 2018/01/31: --timerange=-20180131 -* Use tickframes since 2018/01/31: --timerange=20180131- -* Use tickframes since 2018/01/31 till 2018/03/01 : --timerange=20180131-20180301 -* Use tickframes between POSIX timestamps 1527595200 1527618600: --timerange=1527595200-1527618600 +* Use last 123 tickframes of data: `--timerange=-123` +* Use first 123 tickframes of data: `--timerange=123-` +* Use tickframes from line 123 through 456: `--timerange=123-456` +* Use tickframes till 2018/01/31: `--timerange=-20180131` +* Use tickframes since 2018/01/31: `--timerange=20180131-` +* Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301` +* Use tickframes between POSIX timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`