FluxFilter Trend Strategy [BITsPIP]Hello fellow traders, I'm excited to share with you the FluxFilter Trend Strategy, a trading approach I've developed for those interested in exploring trend-following strategies. My goal was to create something straightforward and accessible, so traders looking to refine their portfolios can easily integrate its features. By the end of this guide, I hope you'll have a solid grasp of how the FluxFilter Trend Strategy functions, appreciate its benefits, understand its potential drawbacks, and see how it might fit into various trading contexts.
I) Overview
The FluxFilter Trend Strategy is tailored to align with the market's long-term trend. It examines the price data from the previous year to gauge the market's overall trajectory by employing moving averages. Subsequently, within shorter timeframes, the strategy utilizes a combination of modified Supertrend, Hull Suite, and various trend-following and filtering techniques to generate buy or sell signals. Although its advanced take profit and stop loss mechanisms might initially present a learning curve, they are integral to the strategy's effectiveness. They are designed to secure gains by capturing prevailing trends and mitigating the impact of false reversal signals.
II) Deep Backtesting
Deep backtesting stands as a cornerstone in the development of trading strategies, offering a robust method for traders to assess the performance of their strategy against historical data. This process yields a retrospective view, illustrating how the strategy might have navigated through past market fluctuations, thereby shedding light on its potential robustness and areas for refinement. However, it's crucial to acknowledge that a strategy's performance can be influenced by a myriad of factors including market dynamics, the chosen timeframe, and the inherent attributes of the traded asset. Consequently, it's advisable to conduct thorough backtesting under various conditions to ascertain the strategy's reliability before applying it to actual trading scenarios.
III) Benefits
A primary advantage of the FluxFilter Trend Strategy is its proficiency in discerning genuine market trends from mere price fluctuations, thereby avoiding premature or uncertain trades. Unlike approaches that take high risks on speculative trades, this strategy prioritizes a high degree of confidence in the direction of the trade. It meticulously waits for a clear confirmation of the market trend. Once this certainty is established, the strategy promptly generates trade signals, ensuring that traders are positioned to capitalize on optimal market entry points without delay. This approach not only enhances the potential for profit but also aligns with a disciplined and methodical trading ethos.
IV) Applications
FluxFilter Trend Strategy can be applied across various timeframes, with a particular efficacy in those under 15 minutes. Its adaptable framework means it can be customized to cater to a variety of asset classes, encompassing stocks, commodities, forex, and cryptocurrencies. Initially, the strategy was specifically calibrated for low-volatile cryptocurrencies, as reflected in the default settings for stop loss and take profit values. It's important to recognize that the unique volatility and trend patterns of your selected market necessitate careful adjustments to these parameters. This fine-tuning of profit targets and stop loss thresholds is crucial for aligning the strategy with the specific dynamics of your chosen market, which I will discuss shortly.
V) Strategy's Logic
1. Trend Identification: My conviction lies in the power of trend trading to yield long-term gains. Central to the FluxFilter Trend Strategy is the Hull Suite indicator, a tool developed by InSilico, serving as one of the confirmation indicators. This indicator acts as a compass for trend direction; a price residing above the Hull Suite line signals an uptrend, potentially marking an entry point for a buy position or confirming it. In contrast, a price positioned below this line suggests a downtrend, potentially indicating a strategic moment to sell or confirming the sell.
2. Noise Reduction: The financial markets are known for their 'noise'—short-lived price movements that can obscure the true market direction. The FluxFilter Trend Strategy is designed to sift through this noise, thereby facilitating more lucid and informed trading decisions. It employs a set of straightforward yet innovative techniques to single out significant misleading fluctuations. This is achieved by analyzing recent bars to spot bars with unusually large bodies, which often represent misleading market noise.
3. Risk Management: A key facet of the strategy is its emphasis on pragmatic risk management. Traders are empowered to establish practical stop-loss and take-profit levels, tailoring these crucial parameters to the specific market they are engaging in. This customization is instrumental in optimizing long-term profitability, ensuring that the strategy adapts fluidly to the unique characteristics and volatility patterns of different trading environments.
VI) Strategy's Input Settings and Default Values
1. Modified Supertrend
i. Factor: Serving as a multiplier in the Average True Range (ATR) calculation, this parameter adjusts the distance of the Supertrend line relative to the price chart. Elevating the factor value widens the gap between the Supertrend line and price, offering a more conservative stance. On the flip side, diminishing the factor value pulls the Supertrend line closer to the price action, heightening its sensitivity. While the preset value is 1, you have the flexibility to modify this to suit your trading approach.
ii. ATR Length: This defines the count of bars that are incorporated into the ATR computation, directly influencing the Supertrend's adaptability to market changes. With a default setting of 30 bars, it strikes a balance, smoothing over short-term fluctuations while maintaining a meaningful sensitivity to market trends. Adjusting this parameter allows you to tailor the indicator's responsiveness to suit your trading strategy, considering the volatility and behavioral patterns of the asset you are trading.
2. Hull Suite
i. Hull Suite Length: Designed for capturing long-term trends, the Hull Suite Length is configured at 1000. Functioning comparably to moving averages, the Hull Suite features upper and lower bands, though these are not employed in our current strategy.
ii. Length Multiplier: It's advisable to maintain a minimal value for the Length Multiplier, prioritizing the optimization of the Hull Suite Length. Presently, it is set to 1.
3. Filtering Indicators
i. Fluctuation Filtering Percentage: It's advisable to set this parameter to ten times the size of the average bar in your specific market, as this helps effectively mitigate the impact of market fluctuations. While the initial default is 0.4(%), based on the BTCUSDT market, it's crucial to adjust this figure to align with the characteristics of different assets or markets you're trading in.
ii. Fluctuation Filtering Bars: This parameter designates the count of preceding bars to consider when assessing market fluctuations. It's fully customizable, allowing you to tailor it based on your market insights. The preset default is 3, a balance chosen to minimize susceptibility to potentially misleading signals.
iii. Trend Confirmation Percentage: This metric is pivotal for verifying the viability of a trend post-entry. If the trade doesn't achieve this percentage in profit, it indicates a deviation from the expected trend. Under such circumstances, it may be prudent to exit the trade prematurely rather than awaiting the stop-loss trigger. It's recommended to set this parameter at half the size of the average candle body for the market you're analyzing. The initial default is set at 0.2(%).
4. StopLoss and TakeProfit
i. StopLoss and TakeProfit Settings: Two distinct approaches are available. Semi-Automatic StopLoss/TakeProfit Setting and Manual StopLoss/TakeProfit Setting. The Semi-Automatic mode streamlines the process by allowing you to input values for a 5-minute timeframe, subsequently auto-adjusting these values across various timeframes, both lower and higher. Conversely, the Manual mode offers full control, enabling you to meticulously define TakeProfit values for each individual timeframe.
ii. TakeProfit Threshold # and TakeProfit Value #: Imagine this mechanism as an ascending staircase. Each step represents a range, with the lower boundary (TakeProfit Value) designed to close the trade upon being reached, and the upper boundary (TakeProfit Threshold) upon being hit, propelling the trade to the next level, and forming a new range. This stair-stepping approach enhances risk management and has the potential to increase profitability. The pre-set configurations are tailored for volatile markets, such as BTCUSDT. It's advisable to devote time to tailoring these settings to your specific market, aiming to achieve optimal results based on backtesting.
iii. StopLoss Value: In line with its name, this value marks the limit of loss you're prepared to accept should the market trend go against your expectations. It's crucial to note that once your asset reaches the first TakeProfit range, the initial StopLoss value becomes obsolete, supplanted by the first TakeProfit Value. The default StopLoss value is pegged at 1.8(%), a figure worth considering in your trading strategy.
VII) Entry Conditions
The principal element that triggers the signal is the Modified Supertrend. Additional indicators serve as confirmatory tools. Nonetheless, to refine your strategy effectively, it's crucial to fine-tune the parameters. This involves adjusting input variables such as take profit levels, threshold parameters, and the filtering values discussed previously.
VIII) Exit Conditions
The strategy stipulates exit conditions primarily governed by stop loss and take profit parameters. On infrequent occasions, if the trend lacks confirmation post-entry, the strategy mandates an exit upon the issuance of a reverse signal (whether confirmed or unconfirmed) by the strategy itself.
Good Luck!!
Longterm
Swing Algo V1.4◆ Introduction
The latest version of the Swing Algo features a complementary system consisting of two internal swing trading logics: an enhanced Swing Algo V1.3 and a secondary control engine to stabilize the overall strategy behaviour in times of increased market chop. Both algorithms feature different averaging lines as well as oscillators, leading to a higher strategy diversification for swing trading as well as a reduced maximum drawdown in comparison to each stand-alone strategy.
While the Swing Algo V1.x series so far featured a single trend-following swing algorithm for each release, where one just switches between Long and Short trades based on one general logic, here two strategies, which act independently of each other, are applied. Due to this, we introduce a third position a trader can be in: the Hedge. The overall logic is as follows:
When both sub-logics are Long, the overall strategy is Long.
When both sub-logics are Short, the overall strategy is Short.
When one sub-logic is Long and the other is Short, the overall strategy is in a Hedge position. It doesn't matter which component is Short and which is Long.
As PineScript doesn't currently offer a real steady hedging-function for two competing swing trading sub-logics (in the sense of a continuously applied Hedge state after hedging conditions are met at least once for an entry), a workaround via position closes was created for this release. For each new internal sub-signal, the overall strategy changes its state (Long/Short/Hedge) visibly on the chart, and the trader can adjust their position accordingly.
For detailed differences to previous Swing Algo V1.x releases, see further below.
◆ Purpose of this Script
This indicator will give Long, Short and Hedge signals on the chart that can be used for e.g. swing trading. Each of the aforementioned sub-logics uses a combination of several (custom) functions and rules to find good entry points for trend trading. After many iterations and tests I came up with this particular setup, which is highly optimized for the ETH/USD trading pair on the daily (D) timeframe.
Attention was also paid to stability, as all parameters are set onto plateaus, so that smaller changes in the characteristic price action should not affect the efficiancy too much, done as an attempt to reduce overfitting as much as possible. Additionally this dual algorithm system is specifically designed to have a safety net: should for the unlikely scenario one swing trading algorithm not trigger at a certain mid-term reversal point, the probability is high that the other will trigger, resulting in an overall hedged position (so that no money is lost in the meantime) until the first algorithm can rejoin at the next mid-term trend change.
For other assets and/or timeframes it is in principle possible to change algorithmic parameters within the indicator settings to tune the swing algorithms, though it is strongly recommended to use the standard asset and timeframe mentioned above.
◆ Viability
For the here presented backtest data, we omitted the biggest portion of the cryptocurrency bullrun in 2017 (starting only at 1st July 2017) so that the results become more realistic for long-term swing traders (investing at least 2-4 years into trading) if such large runs do not happen again. As cryptocurrencies like Ethereum are still to this date capable of doing comparatively smaller runs of about 2-3x in a few weeks/months during accumulation phases (as witnessed e.g. in 2020 and more recently in 2023) and bigger runs during bullmarkets (as witnessed in 2021), the quality of the shown results is still realistic for long-term trend trading efforts over several years, Note that very conservative trading parameters as mentioned below in "Forwardtesting and Backtesting" are used here.
Generally do not expect results in a matter of days or weeks, and of course as with any trading strategy past performances are not indicative of future results.
◆ Forwardtesting and Backtesting
The individual components have been back- and partially forwardtested: The first sub-logic is an advancement of Swing Algo V1.3, with which we have extensive experience running back to October 2020 for its release, while the secondary control strategy, which was privately published for DeanTrader members as a stand-alone script on TradingView in June 2022 and was running in the background since then, is showing good & expected behaviour so far.
While this does not mean that fowardtesting was performed specifically for the combined Swing Algo V1.4 system we have now (which cannot be done realistically considering the timeframes used, i.e. months and especially years), we can at least look at some considerable experience with the individual components. Then again, as I have implemented an exact hedging-function so that both sub-algorithms run independently from each other, it is not likely to see any unexpected behaviour resulting purely from the combination into one script.
For strategy backtesting you can choose the backtest time interval to test the performance of this algorithm for different time windows and different trading pairs. Here various backtesting parameters (e.g. trading fees) can be customized. Default settings for the shown backtest are a starting balance of $1000, a slippage of 20 ticks (= $0.20) and a trading fee of 0.05 % (which is the worst taker fee on the Kraken Pro futures exchange) to have realistic settings. However as we do not conduct many trades with this strategy, fees should not impact our performance too much. As long-term swing traders, we at DeanTrader generally devote one initial portion of our portfolio to swing trading and from then on always use 100% of this portion for the next trade to get the compounding starting. This is in difference to other trading styles which use various, often very small, percentage values for their short- or mid-term trades. Please note that for the here presented backtest only 10% of compounded equity is used for each successive trade to show an estimation for a lower risk & lower reward approach . Keep this in mind when evaluating the backtest data. You can set appropriate values for each backtest parameter in the "Properties" setting menu of the strategy, including the order size percentage of equity value for your trades. Also note that due to the small number of trades the statistical significance is low. It is not possible to gather an abundance of long-term trend signals in the order of hundreds or thousands trades, as much more time would have to pass for this in the case of rather new assets like Ethereum.
Additionally to the TradingView Strategy Tester you can also plot your equity directly on the chart to get a sense for the performance. For this you can also scale the equity graph to e.g. match the starting point of your equity with some price point on the chart to get a direct comparison to 'Buy & Hold' strategies over time.
This indicator (and all other content I provide) is no financial advice. If you use this indicator you agree to my Terms and Conditions which can be found on my website linked on my TradingView profile or in my signature.
◆ Visual Representation on the Chart
Shown below is a screenshot of how the chart looks like when the strategy is applied. Here we can see two different averaging lines, where each line belongs to one of the two sub-logics respectively. Note that this is not a MA-crossover strategy, and the crossing of the lines is not accounted for in the code at all and therefore has no effect on the strategy's signal output. Also note that the price scale is set on logarithmic.
The space between the lines is filled with a faint background color as a rough visual indicator. Magenta-colored fills indicate zones where only Short or Hedge signals can appear, while green-colored fills indicate zones where only Long or Hedge signals can appear. Gray-colored fills mark zones where only Hedge signals can appear, which also means that Hedge signals can appear in any zone. So treat those background fills more as a visual aid to roughly know what can happen next, but pay most attention to the actual signals (with arrows) that appear on the chart.
◆ Differences to Other Versions
Consists now of two competing sub-algorithms instead of just one algorithm. The new system outputs Long, Short and Hedge signals instead of just Long and Short signals.
The first sub-logic is the spiritual successor of the original Swing Algo V1.3 release, with a modified oscillator part.
The second sub-logic serves as a control algorithm (while still having equal rights in terms of strategy impact), newly introduced to the Swing Algo series, but already forwardtested for roughly a year at time of release.
Lowers risk significantly by diversifying swing trading strategies, so that for the rare scenario of a missed trend on one sub-algorithm, losses are prevented as the overall strategy is hedged during that time.
Lowers risk further as the maximum drawdown of the combined strategy is reduced by roughly 1/3 in comparison to each stand-alone strategy while almost retaining the same net profit over a 6-year backtest compared to the first, leading sub-logic.
No guesswork anymore when to use which short leverage (1x corresponding to a Hedge, or 2x corresponding to a Short with an asset-value-change-to-gain-proportionality of -1) as it is clearly defined within the trading system via the displayed signals. In earlier Swing Algo versions, the short leverage for any particular Short signal had to be chosen by hand dependent on market sentiment, which required further market analysis, or was fixed at 2x, leading to less flexibility.
◆ Access
For access please contact me via DM on TradingView or via other channels (linked on my TradingView profile and in my signature).
Balance of Power Heikin Ashi Investing Strategy Balance of Power Heikin Ashi Investing Strategy
This is a swing strategy designed for investment help.
Its made around the Balace of Power indicator, but has been adapted on using the Monthly Heikin Ashi candle from the SPY asset in order to be used with correlation for US Stock/ETF/Index Markets.
The BOP acts as an oscilallator showing the power of a bull trend when its positive and a bearish trend when its in negative. At the same time we can spot reversals, based on the percentiles ( 99/1)
The rules for entry :
For long : The 99 percentile is ascending, and we are either in a positive value (>0), or we crossed the bottom place ( -0.35)
For short : the 99 and 1 percentile are descending, and we are either in a negative value(<0), or we crossed down the top place ( 0.6)
If you have any questions please let me know !
Profit Maxima: a crypto strategyThis strategy is designed for those who are looking for long-term positions with low risk and high profitability.
How does it work?
In short, the basis of this strategy is the frequent modeling of the price using regression equations and the estimation of the range of price movements.
The price modeling process starts from the first bars and will be repeated on each bar. This process is performed in each candle based on the data available up to that candle, and data for subsequent bars is not used.
There is also no fixed price model, but it will change from one candle to the next; Therefore, the more candles there are, the larger the statistical population and therefore the quality of the price model increases.
I have also used the concept of scarcity. Bitcoin is the first scarce digital object in the world. Once something becomes scarce enough, it can be used as money. This scarcity gradually increases and affects the price. The entire crypto market also follows Bitcoin.
However, always remember that past results in no way guarantee future performance.
Why this strategy generates a small number of trades?
Preston Pysh believed Bitcoin cycles happen in three phases: the Bull Run, the Correction, and the Reversion to the Mean. He estimates there are about 200,000 blocks per cycle and there are about 144 blocks per day.
Therefore, each cycle of Bitcoin lasts about four years. The entire crypto market follows bitcoin. On the other hand, cryptocurrency is a new phenomenon. They have a limited price history.
This strategy is designed to open a long position at the lowest possible price. In addition, due to the concept of scarcity and its continued impact on prices, trading in the “short” direction is avoided.
The combination of these factors leads to generate a small number of trades. However, you can test it on several different charts to make sure it works properly.
Default settings
{ default_qty_type } = strategy.percent_of_equity
{ default_qty_value } = 3.3
{ commission_value } = 0.1
{ pyramiding } = 3
{ close_entries_rule } = "ANY"
In a simple word, buy (Entry) and sell (take-profit) orders are each done at three different levels. At each level, 3.3% of equity is used (9.9% in total)
0.1% commission is considered for each transaction.
“close_entries_rule” determines the order in which orders are closed. The default is FIFO (first in, first out), but in this strategy, orders are executed in “first in, last out” order. In this way, the lowest buy (Entry) order corresponds to the lowest sell (take profit) order.
Choose the best chart
Charts have a significant impact on the performance of the strategy. As mentioned, the more historical bars there are, the larger the statistical population and therefore the quality of the price model increases.
You can use the Chart Quality panel to choose the appropriate chart:
The ‘Historical Bars’ field shows the number of candles in the chart. Choose the chart of an exchange that has the most historical bars.
The ‘Recommended Chart’ field shows the suggested chart for some symbols.
The “Predictability” field indicates to what extent price movements can be predicted using the model; the higher the “predictability”, the more credible the results of the strategy. "Predictability" indicates that the results of the strategy are reliable or not.
The image below shows the recommended chart for 20 different symbols:
How to use
You don't need automated trading platforms to use it. It can be used by placing simple buy and sell (take-profit) orders manually.
The green and red lines indicate the 'Entry' and 'Profit' levels respectively. If there is no order (buy / sell) active on one of these levels, it will be displayed in gray. The corresponding values are displayed in the Entry & Profit Limits table.
After choosing the appropriate chart, you can use this table to place your orders manually.
Note that trading in the "short" direction is not recommended at all.
Samples
Godtrix's Crypto HA+RSI+EMA+ATH+DCA Strategy 3.0New Updates is here! Upgrade from previous version 2.0 (Please avoid using v2.0 as it's outdated.)
Great stability, Repaint bug fixes, and New features!
==================
| Introduction: |
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This is a Long Term Strategy, using compounding profit method, it can generate high returns, but it also risk for losses, this can be overcome if you set Stop Loss to over 25% for bitcoin & 60% for Altcoins.
Best profit plan with this strategy is you trade on Future leverage while you hold on to your coin, so that when price goes up, your coin value goes up, and at the same time, you trade with your leverage to earn even more, easily doubling up your total profit.
Benefits:
Fully customizable and you can easily personalized it and FINE TUNE it according to the market or coin you trading on.
The strategy is based on REAL PRACTICAL trading skills, so it works in real-world.
I fixed the "repainting" issue so the backtest it shows you IS ACCURATE when you run for real-time.
We all know one indicator is not going to help you win your trades, so this strategy combines ALL three: EMA for long+short term trend, HA for short term trend, RSI for entry/exit
This strategy is designed for LONG trade (Buy low, Sell high), not for SHORT trade.
This is not day trading, it is more to mid-term trading, where there's only few trades per month
Mainly is coded to work with 3Commas bot auto trading, so you only need to key in your Bot ID & Email Token.
Bot trading NOTE:
- You need to replace the Alert Message with this: {{strategy.order.alert_message}}
- And you'll need the Bot's webhook Url set with the Alert too.
- One Alert will work for both Buy and Sell Order
- If you using other Bot service, you can enter Custom Command in Input Settings too, it works on any bot service.
Lastly,
regarding the setting advice, I would say you try playing with different settings and your objective is to achieve a backtest result that has:
1) Profitable is > 80%
2) Losing trades is nearly 0 or below 25% of your winning trades. Trick is using far stop loss %
3) Net Profit be almost same or more than "Buy & Hold Profit"
==================
| Latest Updates: |
==================
=| Tidy Up Codings |=
- Group input fields so it'll be easier to understand and find the settings
- Upgrade code for obsolete 'transp' options
=| Repaint Issues |=
- Previous v2.0's RSI has repaint issue, creating false result against real-time data. I've fixed this.
- Also done fine-tuning other parts of the codes to prevent possible repaint issues.
=| Bot System |=
- Improved Custom Bot system, so that you're able to set dynamic order size/quantity with my custom keyword: and
Base Order Example:
{ 'message_type': 'bot', 'bot_id': 1234567, 'email_token': 'abcdefgh-1234-1234-1234', 'base_order': , 'delay_seconds': 0, 'pair': 'USDT_BTC'}
=| EMA Downtrend Exit |=
- Added option for you to decide whether to close position when detected EMA Long term downtrend.
=| EMA 2 (short term) is removed |=
- After several test, I've decided to remove this because it doesn't contribute to improving the results.
=| Heikin Ashi System |=
- Improved the chart display, now you'll see the HA candle 'shadowed' behind, so you'll see both actual price candle and HA candle at same time.
- Added the system that detect the HA candle sizes to decide specifically when it's suitable for Entry and Exit.
>> For "Entry/Exit Range"
- This means after HA is valid for Entry or Exit, how many following bars are allowed to stay valid so it will match other requirements to be completely fulfilled for Entry or Exit.
>> For "Crossing Interval"
- This means after detected HA line crossover, how many HA intervals is allow to Entry or Exit
>> For "Reversed Exit"
- This function let's you decide whether to close position if after HA bull (green candle) changed into HA Bear (red candle)
=| RSI A Entry |=
- Added option to avoid Entry during NTZ (No trade Zone)
- Also added the option to avoid next same condition RSI A entry too soon
=| RSI B Entry |=
- This function is for Entry if RSI is going very low, mostly due to bigger price drops in short time, it's good for buying DIP, however we'll never be able to know when a DIP ends, so do more test on this settings before put into real use.
- Added "avoid" options to help avoid getting Entry at "false" DIP, more like a short & fast pullback which causes RSI to drop very low but actually the price is near ATH or Recent High.
- Added option for Entry with Trailing Price Lower Buy combine with a limit order that grabs low price, so whichever it fulfill first.
=| New: Avoid Entry |=
- Well, it's a pain if you bought at the top, so I've added two options that will avoid buying near ATH and Recent High.
=| Time-limit Removed |=
- Sorry that I've missed look on the script policy which I'm not allowed to put a time-limit for public scripts.
=| System Improvements |=
- HA condition detection is optimized and bug fixed
- RSI values now reads accurately on each bar despite using higher timeframe, especially when moving to next interval
=| New: Dollar Cost Averaging (DCA) Orders |=
- Although DCA strategy is not appealing for Long term strategy, but I've added it for your extra options and flexibilities.
- The settings are quite straight-forward and standard, so I won't be explaining here.
=| New: Backtest Start & End Date |=
- This is very good function when you need more accurate result starting at specific date & time.
- Also if you set the date & time for your real trading starts, it'll much result the same as your actual trading records, which helps you to see clearer and make future decisions.
Any found bugs or flaws, please feel free to PM me, I can't get notifications from comments here below, so I'll not able to reply you the soonest possible, still not sure how to turn on notification for comments, anyone who knows can PM and teach me, lol... Thanks in advance!
Well, this is free version, hope it helps! Feedbacks are all welcome :)
(To Moderators: I've fully use the "f_security()" guideline, but instead of creating a separate function, I apply directly on all security() function. Please don't ban my script before fully check if I've truly fixed repaint. Thank you.)
BITSTAMP:BTCUSD COINBASE:BTCUSD COINBASE:ETHUSD BINANCE:BNBUSDT
Buy and hold calculatorThis is a simple buy and hold calculator.
You have an initial date and once that dated it passed it will sell the product that was bought initially.
This strategy buys and sell 100% of the initial volume.
Buy the Dips (by Coinrule)Taking your first steps into automated trading may be challenging. Coinrule's mission is to make it as easy as possible, also for beginners.
Here follows the best trading strategy to get started with Coinrule. This strategy doesn't involve complex indicators, yet was proved to be effective in the long term for many coins. Results seem to be improved when trading a coin vs Bitcoin.
The strategy buys the dips of a coin to sell with a profit. A stop-loss protects every trade.
Crypto markets offer high volatility and, thus, excellent opportunities for trading. Excluding times of severe downtrend, buying the dip is a simple and effective long-term trading strategy. The buy-signal is set to a 2% drop in a 30-minutes time frame.
Each trade comes with a take profit and a stop loss. Both set at 2%.
You can adjust these percentages to the market volatility as an advanced setup. You can backtest the outcomes using the backtesting tool from Tradingview
The strategy assumes each order to trade 30% of the available capital. A trading fee of 0.1% is taken into account. The fee is aligned to the base fee applied on Binance, which is the largest cryptocurrency exchange.
RSI + MACD Strategy (ETHUSD 1D)This script uses a combination of MACD, RSI and Stochastic RSI to find tops and bottoms with a ~70% profit rate. While it is successful with all of the top market coins, ETHUSD is by far showing the best results. It is fine tuned only for 1D charts, any other time interval results in losses. It's been tested with shorting as well, but longing + selling gives better returns.
Each market is slightly different, but backtests from October 2015 on Poloniex show almost 300,000%(!!) gains. While it is unlikely to produce such gains going forward (but who knows!), the profit rate is still much higher than buy–and–hold on any long-term backtests. As always, don't follow a script blindly and use your own best judgement when trading.
DISCLAIMER:
This script should be used for educational purposes only, and is not intended to provide trading advice. Use at your own risk!
Long Term Strategy (100% BTC, only Longs)My BTCUSD Long Term Strategy,
based on Ichimoku, BB, EMA's & some William's Alligator & Fractals.
Up to this point only gave profitable long trades on BTCUSD (100% profit factor).
Have fun & be careful, nothing is bullet proof.
Ciao!
Long Only Investment Strategy, Alpha (by ChartArt)Here is my strategy with the working title "alpha" which works best with the published default setting on the 'S&P 500' monthly chart. The strategy is intended for investments in long-term time-frames (the current average of the default setting is a holding period of long positions of 40 months).
If you don't want to lose all your money due to some random strategy you found on the Internet, here is a warning:
All trading involves high risk; past performance is not necessarily indicative of future results. Hypothetical or simulated performance results have certain inherent limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not actually been executed, the results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown.
