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質問

i have a strategy in jessy can you convert it to pine so i can use it on tradingveiw
code : from jesse.strategies import Strategy
import jesse.indicators as ta
from jesse import utils


class AlligatorAi(Strategy):
@property
def long_term_candles(self):
# Get candles for a larger timeframe to analyze long-term trends
big_tf = '4h'
if self.timeframe == '4h':
big_tf = '6h'
return self.get_candles(self.exchange, self.symbol, big_tf)

@property
def adx(self):
# Calculate the ADX (Average Directional Index) to determine trend strength
return ta.adx(self.candles) > 30

@property
def cmo(self):
# Calculate the CMO (Chande Momentum Oscillator) for momentum analysis
return ta.cmo(self.candles, 14)

@property
def srsi(self):
# Calculate the Stochastic RSI for overbought/oversold conditions
return ta.srsi(self.candles).k

@property
def alligator(self):
# Calculate the Alligator indicator for trend direction
return ta.alligator(self.candles)

@property
def big_alligator(self):
# Calculate the Alligator indicator on a larger timeframe for long-term trend direction
return ta.alligator(self.long_term_candles)

@property
def trend(self):
# Determine the current trend based on the Alligator indicator
if self.price > self.alligator.lips > self.alligator.teeth > self.alligator.jaw:
return 1 # Uptrend
if self.price < self.alligator.lips < self.alligator.teeth < self.alligator.jaw:
return -1 # Downtrend
return 0 # No clear trend

@property
def big_trend(self):
# Determine the long-term trend based on the Alligator indicator on a larger timeframe
if self.price > self.big_alligator.lips > self.big_alligator.teeth > self.big_alligator.jaw:
return 1 # Long-term uptrend
if self.price < self.big_alligator.lips < self.big_alligator.teeth < self.big_alligator.jaw:
return -1 # Long-term downtrend
return 0 # No clear long-term trend

@property
def long_term_ma(self):
# Calculate the 100-period EMA on a larger timeframe for long-term trend confirmation
e = ta.ema(self.long_term_candles, 100)
if self.price > e:
return 1 # Price is above the EMA
if self.price < e:
return -1 # Price is below the EMA

def should_long(self) -> bool:
# Determine if conditions are met to enter a long position
return self.trend == 1 and self.adx and self.big_trend == 1 and self.long_term_ma == 1 and self.cmo > 20 and self.srsi < 20

def should_short(self) -> bool:
# Determine if conditions are met to enter a short position
return self.trend == -1 and self.adx and self.big_trend == -1 and self.long_term_ma == -1 and self.cmo < -20 and self.srsi > 80

def go_long(self):
# Execute a long position
entry = self.price
stop = entry - ta.atr(self.candles) * 2 # Set stop loss based on ATR
qty = utils.risk_to_qty(self.available_margin, 3, entry, stop, fee_rate=self.fee_rate) * 3 # Calculate position size
self.buy = qty, entry # Place buy order

def go_short(self):
# Execute a short position
entry = self.price
stop = entry + ta.atr(self.candles) * 2 # Set stop loss based on ATR
qty = utils.risk_to_qty(self.available_margin, 3, entry, stop, fee_rate=self.fee_rate) * 3 # Calculate position size
self.sell = qty, entry # Place sell order

def should_cancel_entry(self) -> bool:
return True

def on_open_position(self, order) -> None:
# Set stop loss and take profit when a position is opened
if self.is_long:
self.stop_loss = self.position.qty, self.price - ta.atr(self.candles) * 2
self.take_profit = self.position.qty, self.price + ta.atr(self.candles) * 2
if self.is_short:
self.stop_loss = self.position.qty, self.price + ta.atr(self.candles) * 2
self.take_profit = self.position.qty, self.price - ta.atr(self.candles) * 2

思考

Converting code

The task is to convert a Python-based trading strategy from 'jesse' to PineScript for use on TradingView, ensuring the logic is preserved.

Translating to PineScript

I’m setting up a PineScript strategy to match the original logic, focusing on indicators like long_term_candles and adx, and crafting methods such as should_long and go_long.

Choosing higher timeframes

I'm noting the distinction between default '4h' candles and '6h' candles when the timeframe is '4h', and the need to utilize PineScript for dynamically selecting higher timeframes.

Setting up time periods

I'm working through defining big_tf as either '240' or '360' minutes, or '4H' and '6H' respectively, depending on the timeframe's availability.

Crafting the timeframe logic

I’m thinking through a PineScript code snippet to handle varying timeframes. Using conditional statements to select either '4H' or '6H' based on the current timeframe, I’m pulling higher timeframe data with the security function.

Crafting the Alligator

I'm diving into the Alligator indicator, using PineScript to compute smoothed moving averages with different periods and shifts, aligning with the built-in TradingView implementation.

Charting smooth averages

I’m working through calculating the SMMA (Smoothed Moving Average) in PineScript, which involves defining jaw, teeth, and lips lines with shifts backward due to PineScript's constraints.

Simulating data shifts

To mimic the Alligator indicator in PineScript, I shift data backward, revealing how future data might influence past states by using historically shifted values.

Adjusting strategy logic

I'm tweaking the strategy logic to manage future Alligator line values. Instead, I'm computing lines without shifting, focusing on non-shifted SMMA values for conditions.

Setting up variables

I’m establishing key metrics by defining the higher timeframe. Using a conditional statement, I set up the timeframe and then fetch data for open, high, low, and close prices.

Breaking down the trend indicator

I’m calculating the Alligator trend indicator by smoothing different market data series over various time frames. This reveals an uptrend, downtrend, or no clear trend based on relationships between these smoothed lines.

Crafting trend indicators

I'm coding two trend indicators using PineScript. The first uses close and other specified indicators; the second, a higher timeframe.

Translating ADX calculation

I’m translating the ADX calculation from Pytho→ to PineScript, focusing on computing: adx(self) to ta.adx(14).

Mapping CMO implementation

I’m identifying the CMO calculation in PineScript, mapping it from Python code to ta.cmo(close, 14).

Adapting SRSI logic

I’m aligning the Stochastic RSI logic with PineScript. Using ta.stochrsi(close, 14).k for consistency with the original Python code's stoch_rsi_k.

Integrating variables

I’m working through the 14-period Stochastic RSI and the 100-period EMA on a larger timeframe to confirm long-term trends. The EMA direction indicates if the price is above or below it.

Adjusting conditions

I’m tweaking the PineScript conditions for long and short trades, defined by trend, ADX, big trend, EMA, CMO, and Stochastic RSI.

Translating trading strategies

I’m working through translating Python trading strategies into PineScript, focusing on stop loss, take profit, and position size. Using ATR for risk management is a key aspect.

Crafting an entry point

OK, let me see. I'm setting the strategy by calculating the ATR, then executing long and short positions with specific stop loss and take profit levels.

Setting up the logic

Ensuring only one opening position in PineScript and utilizing the on_open_position method to establish stop loss and take profit settings.

Crafting the alligator trend

I’m working on an alligator indicator strategy with moving averages and trend analysis. This method blends insights from both lower and higher timeframes to gauge market momentum.

Plotting and analyzing

I’m charting Alligator lines and accommodating higher timeframe lines, along with ADX, CMO, and Stochastic RSI values. This paints a detailed picture of market trends and dynamics.

Adjusting the strategy

I’m mindful of avoiding repainting in PineScript by adjusting the security function with lookahead=barmerge.lookahead_on or lookahead=barmerge.lookahead_off to ensure no future data is used for backtesting.

Adjusting calculations

I’m thinking through how to shift Alligator lines for calculations without accessing future data. By using the 'offset' parameter for plotting and incorporating historical data for computations, I can ensure calculations align with the appropriate timeframe.

Refining trend analysis

I’m updating the trend determination by using shifted jaw, teeth, and lips values, comparing them to the close price to identify upward, downward, or stable trends.

Ensuring accurate calculations

To maintain precision in a strategy, ensuring future data is not accessed is crucial. Properly adjusting calculations and source data is essential to avoid issues.

Validating trend alignment

I'm pulling together the Alligator strategy with SMMA for jaw, teeth, and lips. Considering higher timeframes for trend detection.

Assessing trend indicators

I'm analyzing long and short trading probabilities using trend, ADX, big trend, long-term moving average, CMO, and Stochastic RSI. Then, I plot the Alligator lines and optional higher timeframe lines.

回答

Certainly! I'll convert your Jesse strategy code into PineScript, which you can use on TradingView. Here's the equivalent PineScript code with explanations:

//@version=5
strategy("AlligatorAi Strategy", overlay=true)

// Determine the higher timeframe to analyze long-term trends
big_tf = timeframe.period == '240' ? '360' : '240'  // Use '6h' if current timeframe is '4h', else '4h'

// Fetch higher timeframe OHLC data
[htf_open, htf_high, htf_low, htf_close] = request.security(syminfo.tickerid, big_tf, [open, high, low, close], lookahead=barmerge.lookahead_off)

// Calculate the median price for both current and higher timeframes
hl2 = (high + low) / 2
hl2_htf = (htf_high + htf_low) / 2

// Alligator indicator parameters
jaw_length = 13
jaw_offset = 8
teeth_length = 8
teeth_offset = 5
lips_length = 5
lips_offset = 3

// Helper function for SMMA (equivalent to RMA in PineScript)
smma(src, length) => ta.rma(src, length)

// Calculate Alligator lines for the current timeframe
jaw = smma(hl2, jaw_length)
teeth = smma(hl2, teeth_length)
lips = smma(hl2, lips_length)

// Shift the Alligator lines for plotting and calculations
jaw_shifted = nz(jaw[jaw_offset])
teeth_shifted = nz(teeth[teeth_offset])
lips_shifted = nz(lips[lips_offset])

// Calculate Alligator lines for the higher timeframe
jaw_htf = smma(hl2_htf, jaw_length)
teeth_htf = smma(hl2_htf, teeth_length)
lips_htf = smma(hl2_htf, lips_length)

// Shift the higher timeframe Alligator lines
jaw_htf_shifted = nz(jaw_htf[jaw_offset])
teeth_htf_shifted = nz(teeth_htf[teeth_offset])
lips_htf_shifted = nz(lips_htf[lips_offset])

// Determine the current trend based on the Alligator indicator
var trend = 0
if close > lips_shifted and lips_shifted > teeth_shifted and teeth_shifted > jaw_shifted
    trend := 1  // Uptrend
else if close < lips_shifted and lips_shifted < teeth_shifted and teeth_shifted < jaw_shifted
    trend := -1  // Downtrend
else
    trend := 0  // No clear trend

// Determine the long-term trend based on the higher timeframe Alligator indicator
var big_trend = 0
if close > lips_htf_shifted and lips_htf_shifted > teeth_htf_shifted and teeth_htf_shifted > jaw_htf_shifted
    big_trend := 1  // Long-term uptrend
else if close < lips_htf_shifted and lips_htf_shifted < teeth_htf_shifted and teeth_htf_shifted < jaw_htf_shifted
    big_trend := -1  // Long-term downtrend
else
    big_trend := 0  // No clear long-term trend

// Calculate the ADX to determine trend strength
adx_value = ta.adx(14)
adx_condition = adx_value > 30

// Calculate the CMO (Chande Momentum Oscillator)
cmo_value = ta.cmo(close, 14)

// Calculate the Stochastic RSI (%K line)
stoch_rsi_k = ta.stochrsi(close, 14).k

// Calculate the 100-period EMA on the higher timeframe for long-term trend confirmation
ema_htf = ta.ema(htf_close, 100)
long_term_ma = close > ema_htf ? 1 : -1

// Calculate the ATR for stop loss and take profit levels
atr_value = ta.atr(14)

// Define conditions to enter a long position
long_condition = (trend == 1) and adx_condition and (big_trend == 1) and (long_term_ma == 1) and (cmo_value > 20) and (stoch_rsi_k < 20)

// Define conditions to enter a short position
short_condition = (trend == -1) and adx_condition and (big_trend == -1) and (long_term_ma == -1) and (cmo_value < -20) and (stoch_rsi_k > 80)

// Execute a long position with stop loss and take profit
if long_condition
    strategy.entry("Long", strategy.long)
    strategy.exit("Exit Long", "Long", stop=close - 2 * atr_value, limit=close + 2 * atr_value)

// Execute a short position with stop loss and take profit
if short_condition
    strategy.entry("Short", strategy.short)
    strategy.exit("Exit Short", "Short", stop=close + 2 * atr_value, limit=close - 2 * atr_value)

// Plot the Alligator indicator lines
plot(jaw, color=color.blue, title="Jaw", offset=jaw_offset)
plot(teeth, color=color.red, title="Teeth", offset=teeth_offset)
plot(lips, color=color.green, title="Lips", offset=lips_offset)

Explanation:

  • Higher Timeframe Data (big_tf): The script adjusts the higher timeframe to '6h' if the current chart is '4h', otherwise it uses '4h'.

  • Alligator Indicator: Calculated using the ta.rma() function (which is equivalent to SMMA). The Alligator lines are shifted using the offset parameter for plotting and adjusted for calculations using array indexing.

  • Trend Determination: The script checks the positioning of the Alligator lines to determine if the market is in an uptrend, downtrend, or no clear trend.

  • Indicators:

    • ADX: Determines if the trend strength is significant (greater than 30).
    • CMO: Measures momentum; conditions check if it's above 20 or below -20.
    • Stochastic RSI (stoch_rsi_k): Identifies overbought or oversold conditions.

  • Long-Term Moving Average (long_term_ma): Uses a 100-period EMA on the higher timeframe to confirm long-term trend direction.

  • Entry and Exit Conditions:

    • Long Positions: Entered when all long conditions are met.
    • Short Positions: Entered when all short conditions are met.
    • Stop Loss and Take Profit: Set at twice the ATR value from the entry price.

Notes:

  • Repainting Issues: The request.security() function uses lookahead=barmerge.lookahead_off to prevent future data from affecting calculations, which is essential for backtesting accuracy.

  • Alligator Line Shifts: Since PineScript cannot access future data, the script adjusts the Alligator lines by shifting the data backward in calculations and forward in plotting.

  • Position Sizing: The script uses default position sizing. If you want to adjust the position size based on risk, you can modify the strategy.entry() function with the qty parameter.

Testing the Strategy:

  • Add the script to your TradingView chart.
  • Adjust the chart timeframe to your preference.
  • Run a backtest to see how the strategy performs.
  • Tweak parameters like jaw_length, teeth_length, lips_length, and indicator thresholds to optimize performance.

Caveats:

  • Indicator Settings: Ensure that the indicator periods match those used in your original Jesse strategy.

  • Data Availability: Higher timeframe data must be available on your chart; otherwise, the script may not function as intended.

Let me know if you have any questions or need further adjustments!

質問

i get error on line 64 : Could not find function or function reference 'ta.adx'

思考

Converting and addressing errors

The assistant converted code from Jesse strategy to PineScript for TradingView. The user reports an error on line 64 about a missing function reference 'ta.adx'. The assistant needs to identify and resolve the issue.

Mapping trends

I’m analyzing the Alligator indicator for trend directions on both current and higher timeframes. Using ADX and CMO for trend strength analysis.

Assessing trend direction

I’m digging into the 100-period EMA on a higher timeframe to confirm the long-term trend. The current price being higher than this EMA suggests a bullish trend.

Implementing trade conditions

I'm working through adding ATR-based stop loss and take profit levels for long and short trades, plotted with the Alligator indicator lines for a clear market trend.

Fact-checking function validity

OK, I’m checking if 'ta.adx' exists in PineScript version 5. It does, and 'ta.adx(length)' calculates the Average Directional Index.

Investigating function

I’m piecing together why 'ta.adx' might not work. It seems 'adx' might be more straightforward. I’m also considering if 'ta.adx' needs specific parameters.

Figuring out ADX function issues

I’m exploring the potential problems with 'ta.adx()' in PineScript v5. It might be tied to an outdated version or a script error. Testing a basic script can help clarify.

Adjusting the function

I’m thinking through the issue of 'ta.adx' not being recognized. Confirming PineScript version 5, I’m suggesting using 'adx(14)' instead of 'ta.adx(14)' or verifying the correct function call.

Updating function parameters

I’m updating the 'ta.adx()' function to include 'high', 'low', 'close', and 'length' parameters, ensuring alignment with PineScript's requirements. This adjustment should resolve the issue.

Ensuring accurate function usage
I'm fixing PineScript function calls, especially 'ta.adx()', 'ta.cmo()', and 'ta.stochrsi()'. Double-checking parameters and documentation keeps the script precise and functional.

回答

I'm sorry to hear you're experiencing issues with the code. The error you're encountering is due to incorrect usage of the ta.adx() function in PineScript. Let's address this issue and ensure that all indicator functions are used correctly.

Issue Explanation:

In PineScript version 5, the ta.adx() function requires the high, low, and close series as arguments, along with the length parameter. The correct syntax is:

ta.adx(high, low, close, length)

In the previous code, I mistakenly omitted the high, low, and close parameters.

Updated Code:

Below is the corrected version of the strategy, with proper usage of the ta.adx(), ta.cmo(), and ta.stochrsi() functions:

//@version=5
strategy("AlligatorAi Strategy", overlay=true)

// Determine the higher timeframe to analyze long-term trends
big_tf = timeframe.period == '240' ? '360' : '240'  // Use '6h' if current timeframe is '4h', else '4h'

// Fetch higher timeframe OHLC data
[htf_open, htf_high, htf_low, htf_close] = request.security(syminfo.tickerid, big_tf, [open, high, low, close], lookahead=barmerge.lookahead_off)

// Calculate the median price for both current and higher timeframes
hl2 = (high + low) / 2
hl2_htf = (htf_high + htf_low) / 2

// Alligator indicator parameters
jaw_length = 13
jaw_offset = 8
teeth_length = 8
teeth_offset = 5
lips_length = 5
lips_offset = 3

// Helper function for SMMA (equivalent to RMA in PineScript)
smma(src, length) => ta.rma(src, length)

// Calculate Alligator lines for the current timeframe
jaw = smma(hl2, jaw_length)
teeth = smma(hl2, teeth_length)
lips = smma(hl2, lips_length)

// Shift the Alligator lines for plotting and calculations
jaw_shifted = nz(jaw[jaw_offset])
teeth_shifted = nz(teeth[teeth_offset])
lips_shifted = nz(lips[lips_offset])

// Calculate Alligator lines for the higher timeframe
jaw_htf = smma(hl2_htf, jaw_length)
teeth_htf = smma(hl2_htf, teeth_length)
lips_htf = smma(hl2_htf, lips_length)

// Shift the higher timeframe Alligator lines
jaw_htf_shifted = nz(jaw_htf[jaw_offset])
teeth_htf_shifted = nz(teeth_htf[teeth_offset])
lips_htf_shifted = nz(lips_htf[lips_offset])

// Determine the current trend based on the Alligator indicator
var trend = 0
if close > lips_shifted and lips_shifted > teeth_shifted and teeth_shifted > jaw_shifted
    trend := 1  // Uptrend
else if close < lips_shifted and lips_shifted < teeth_shifted and teeth_shifted < jaw_shifted
    trend := -1  // Downtrend
else
    trend := 0  // No clear trend

// Determine the long-term trend based on the higher timeframe Alligator indicator
var big_trend = 0
if close > lips_htf_shifted and lips_htf_shifted > teeth_htf_shifted and teeth_htf_shifted > jaw_htf_shifted
    big_trend := 1  // Long-term uptrend
else if close < lips_htf_shifted and lips_htf_shifted < teeth_htf_shifted and teeth_htf_shifted < jaw_htf_shifted
    big_trend := -1  // Long-term downtrend
else
    big_trend := 0  // No clear long-term trend

// Calculate the ADX to determine trend strength
adx_value = ta.adx(high, low, close, 14)
adx_condition = adx_value > 30

// Calculate the CMO (Chande Momentum Oscillator)
cmo_value = ta.cmo(close, 14)

// Calculate the Stochastic RSI (%K line)
stoch_rsi_k = ta.stochrsi(close, 14, 14, 3)

// Calculate the 100-period EMA on the higher timeframe for long-term trend confirmation
ema_htf = request.security(syminfo.tickerid, big_tf, ta.ema(close, 100), lookahead=barmerge.lookahead_off)
long_term_ma = close > ema_htf ? 1 : -1

// Calculate the ATR for stop loss and take profit levels
atr_value = ta.atr(14)

// Define conditions to enter a long position
long_condition = (trend == 1) and adx_condition and (big_trend == 1) and (long_term_ma == 1) and (cmo_value > 20) and (stoch_rsi_k < 20)

// Define conditions to enter a short position
short_condition = (trend == -1) and adx_condition and (big_trend == -1) and (long_term_ma == -1) and (cmo_value < -20) and (stoch_rsi_k > 80)

// Execute a long position with stop loss and take profit
if long_condition
    strategy.entry("Long", strategy.long)
    strategy.exit("Exit Long", "Long", stop=close - 2 * atr_value, limit=close + 2 * atr_value)

// Execute a short position with stop loss and take profit
if short_condition
    strategy.entry("Short", strategy.short)
    strategy.exit("Exit Short", "Short", stop=close + 2 * atr_value, limit=close - 2 * atr_value)

// Plot the Alligator indicator lines
plot(jaw_shifted, color=color.blue, title="Jaw", offset=-jaw_offset)
plot(teeth_shifted, color=color.red, title="Teeth", offset=-teeth_offset)
plot(lips_shifted, color=color.green, title="Lips", offset=-lips_offset)

Key Corrections and Explanations:

  1. Correct Usage of ta.adx():

    • Previous Code: 
      adx_value = ta.adx(14)
    • Corrected Code: 
      adx_value = ta.adx(high, low, close, 14)
    • The ta.adx() function requires the high, low, close, and length parameters.

  2. Adjusting the Alligator Line Shifts:

    • When shifting series in PineScript, it's important to consider that you cannot access future values. Therefore, we need to adjust the offsets properly.
    • For calculations, we shift the Alligator lines using array indexing. For plotting, we use the offset parameter with a negative value to shift the lines backward.
    • Plotting Adjustments: 
      plot(jaw_shifted, color=color.blue, title="Jaw", offset=-jaw_offset)
plot(teeth_shifted, color=color.red, title="Teeth", offset=-teeth_offset)
plot(lips_shifted, color=color.green, title="Lips", offset=-lips_offset)

  3. Calculating the 100-period EMA on the Higher Timeframe:

    • The EMA should be calculated on the higher timeframe's close data.
    • Previous Code: 
      ema_htf = ta.ema(htf_close, 100)
    • Corrected Code: 
      ema_htf = request.security(syminfo.tickerid, big_tf, ta.ema(close, 100), lookahead=barmerge.lookahead_off)
    • This ensures the EMA is correctly computed on the higher timeframe.

  4. Correct Usage of ta.stochrsi():

    • The ta.stochrsi() function requires source, rsiLength, stochLength, and kSmoothLength parameters.
    • Previous Code: 
      stoch_rsi_k = ta.stochrsi(close, 14).k
    • Corrected Code: 
      stoch_rsi_k = ta.stochrsi(close, 14, 14, 3)
    • This provides the %K line of the Stochastic RSI as a series.

  5. Additional Corrections:

    • Ensured that all functions have the correct parameters.
    • Used request.security() to fetch higher timeframe EMA properly.
    • Adjusted plotting to correctly display the Alligator lines with proper shifting.

Testing the Updated Strategy:

  • Add the Updated Script:

    • Copy and paste the corrected code into a new PineScript editor in TradingView.

  • Set the Chart Timeframe:

    • Ensure your chart is set to a timeframe where the higher timeframe data (big_tf) is available.

  • Run a Backtest:

    • Use the TradingView strategy tester to run a backtest and analyze the results.

  • Adjust Parameters if Needed:

    • You can modify the lengths and thresholds for indicators like ADX, CMO, and Stochastic RSI to better suit your trading style or the asset you're analyzing.

Additional Notes:

  • Indicator Settings:

    • Make sure that the indicator periods (e.g., lengths for ADX, CMO, EMA) match those used in your original Jesse strategy.

  • Repainting Issues:

    • The use of lookahead=barmerge.lookahead_off in request.security() prevents future data from influencing current calculations, which is essential for accurate backtesting.

  • Position Sizing:

    • The script uses default position sizing. If you want to adjust the position size based on your available margin or risk management strategy, you can modify the strategy.entry() function with the qty parameter.

    • Example:

      strategy.entry("Long", strategy.long, qty=your_calculated_quantity)

Let me know if you have any more questions or need further assistance!

質問

give me the corect code all in one

思考

Figuring out the issue

The problem involves the incorrect usage of 'ta.adx()', with an updated PineScript code correction needed.

Compiling the code

I’m gathering and refining the code, making sure it’s all in one piece and accurate. Including a brief note on corrections helps clarify any modifications.

Adjusting parameters

I fixed script errors and tweaked Alligator indicator parameters. Also calculated ADX, CMO, and %K for trend analysis.

Here's a brief explanation of EMA with a 100-period lookahead: it calculates the Exponential Moving Average and uses bar merge.

Refining conditions

I’m incorporating ADX and CMO conditions to improve long and short position setups, ensuring clarity in strategy execution.

Adjusting settings

I'm digging into the scaling of stoch_rsi and ensuring the correct timeframe EMA. Let's clarify stoch_rsi_k and calculate the EMA with higher timeframe data using request.security().

Revising the code

I’m transforming the request for higher timeframe close data and recalculating the EMA to ensure accurate indicator values.

Ensuring accuracy

I’m working through making sure the Alligator lines are calculated using higher timeframe data within request.security(), as using current timeframe data could lead to incorrect analysis.

Evaluating methods

I’m considering adjusting the code to calculate Alligator lines on higher timeframes, either using smma or defining a custom function. The goal is to apply these calculations to current timeframe data.

Defining functions for higher timeframe Alligator lines

I’m curious about defining functions specifically for higher timeframe Alligator lines, such as jaw_htf and teeth_htf, by utilizing request.security() to fetch higher timeframe high and low values.

Ensuring accurate higher timeframe data

OK, let me see. The code is correct because request.security() in PineScript accesses higher timeframe data, ensuring accurate calculations using high, low, and close from the specified timeframe.

Revisiting the approach

Sneakily tweaking higher timeframe data to match lower timeframe might not give precise Alligator lines. Instead, I’m recalculating them in the higher timeframe and converting.

Assessing trend dynamics

I’m mapping out the Alligator indicator on both current and higher timeframes, and integrating the ADX, Chande Momentum Oscillator (CMO), Stochastic RSI, and a 100-period EMA for clearer long-term trend insights.

Analyzing moving averages

I’m digging into the EMA (Exponential Moving Average) with a 100-bar lookback period. Now, I’m thinking about how it intersects with the long-term MA. This gives me a clearer picture of underlying trends.

Setting trading rules

I’m calculating ATR for stop loss and take profit. Defining long and short conditions. Executing trades with exits, using the Alligator indicator for higher timeframe accuracy.

回答

Certainly! Here is the corrected and complete PineScript code for your AlligatorAi strategy, incorporating all the necessary adjustments:

//@version=5
strategy("AlligatorAi Strategy", overlay=true)

// Determine the higher timeframe to analyze long-term trends
big_tf = timeframe.period == '240' ? '360' : '240'  // Use '6h' if current timeframe is '4h', else '4h'

// Helper function for SMMA (equivalent to RMA in PineScript)
smma(src, length) => ta.rma(src, length)

// Calculate the median price (hl2)
hl2 = (high + low) / 2

// Alligator indicator parameters
jaw_length = 13
jaw_offset = 8
teeth_length = 8
teeth_offset = 5
lips_length = 5
lips_offset = 3

// Calculate Alligator lines for the current timeframe
jaw = smma(hl2, jaw_length)
teeth = smma(hl2, teeth_length)
lips = smma(hl2, lips_length)

// Shift the Alligator lines for plotting and calculations
jaw_shifted = nz(jaw[jaw_offset])
teeth_shifted = nz(teeth[teeth_offset])
lips_shifted = nz(lips[lips_offset])

// Calculate Alligator lines for the higher timeframe
jaw_htf = request.security(syminfo.tickerid, big_tf, smma((high + low) / 2, jaw_length), lookahead=barmerge.lookahead_off)
teeth_htf = request.security(syminfo.tickerid, big_tf, smma((high + low) / 2, teeth_length), lookahead=barmerge.lookahead_off)
lips_htf = request.security(syminfo.tickerid, big_tf, smma((high + low) / 2, lips_length), lookahead=barmerge.lookahead_off)

// Shift the higher timeframe Alligator lines
jaw_htf_shifted = nz(jaw_htf[jaw_offset])
teeth_htf_shifted = nz(teeth_htf[teeth_offset])
lips_htf_shifted = nz(lips_htf[lips_offset])

// Determine the current trend based on the Alligator indicator
trend = 0
if close > lips_shifted and lips_shifted > teeth_shifted and teeth_shifted > jaw_shifted
    trend := 1  // Uptrend
else if close < lips_shifted and lips_shifted < teeth_shifted and teeth_shifted < jaw_shifted
    trend := -1  // Downtrend
else
    trend := 0  // No clear trend

// Determine the long-term trend based on the higher timeframe Alligator indicator
big_trend = 0
if close > lips_htf_shifted and lips_htf_shifted > teeth_htf_shifted and teeth_htf_shifted > jaw_htf_shifted
    big_trend := 1  // Long-term uptrend
else if close < lips_htf_shifted and lips_htf_shifted < teeth_htf_shifted and teeth_htf_shifted < jaw_htf_shifted
    big_trend := -1  // Long-term downtrend
else
    big_trend := 0  // No clear long-term trend

// Calculate the ADX to determine trend strength
adx_value = ta.adx(high, low, close, 14)
adx_condition = adx_value > 30

// Calculate the CMO (Chande Momentum Oscillator)
cmo_value = ta.cmo(close, 14)

// Calculate the Stochastic RSI (%K line)
stoch_rsi = ta.stochrsi(close, 14, 14, 3)
stoch_rsi_k = stoch_rsi * 100  // Convert to percentage

// Calculate the 100-period EMA on the higher timeframe for long-term trend confirmation
ema_htf = request.security(syminfo.tickerid, big_tf, ta.ema(close, 100), lookahead=barmerge.lookahead_off)
long_term_ma = close > ema_htf ? 1 : -1

// Calculate the ATR for stop loss and take profit levels
atr_value = ta.atr(14)

// Define conditions to enter a long position
long_condition = (trend == 1) and adx_condition and (big_trend == 1) and (long_term_ma == 1) and (cmo_value > 20) and (stoch_rsi_k < 20)

// Define conditions to enter a short position
short_condition = (trend == -1) and adx_condition and (big_trend == -1) and (long_term_ma == -1) and (cmo_value < -20) and (stoch_rsi_k > 80)

// Execute a long position with stop loss and take profit
if long_condition
    strategy.entry("Long", strategy.long)
    strategy.exit("Exit Long", "Long", stop=close - 2 * atr_value, limit=close + 2 * atr_value)

// Execute a short position with stop loss and take profit
if short_condition
    strategy.entry("Short", strategy.short)
    strategy.exit("Exit Short", "Short", stop=close + 2 * atr_value, limit=close - 2 * atr_value)

// Plot the Alligator indicator lines
plot(jaw_shifted, color=color.blue, title="Jaw", offset=-jaw_offset)
plot(teeth_shifted, color=color.red, title="Teeth", offset=-teeth_offset)
plot(lips_shifted, color=color.green, title="Lips", offset=-lips_offset)

Key Points in the Code:

  • Higher Timeframe Adjustments:

    • The higher timeframe (big_tf) is set to '6h' if the current timeframe is '4h', otherwise it defaults to '4h'.
    • All calculations for the higher timeframe are done within the request.security() function to ensure accurate data is used.

  • Alligator Indicator:

    • The smma() function is used to calculate the Alligator lines, mimicking the ta.rma() function in PineScript.
    • The Alligator lines are shifted appropriately for both plotting and calculations.

  • Trend Determination:

    • The current trend (trend) and the long-term trend (big_trend) are determined based on the positioning of the Alligator lines.
    • Conditions check whether the price and Alligator lines are in alignment for an uptrend or downtrend.

  • Indicators Used:

    • ADX: Used to assess the strength of the trend. The condition checks if the ADX value is greater than 30.
    • CMO: The Chande Momentum Oscillator is used to measure momentum. Conditions check if it's above 20 or below -20.
    • Stochastic RSI: Used to identify overbought or oversold conditions. The %K line is multiplied by 100 to get a percentage value.

  • Entry and Exit Conditions:

    • Long Positions: Entered when all long conditions are met, with stop loss and take profit set at twice the ATR value from the entry price.
    • Short Positions: Entered when all short conditions are met, with stop loss and take profit similarly set.

  • Plotting:

    • The Alligator lines (Jaw, Teeth, and Lips) are plotted on the chart with appropriate offsets to visually represent the indicator.

Notes:

  • Repainting Prevention:

    • The lookahead=barmerge.lookahead_off parameter in the request.security() function ensures that the script doesn't use future data, which is crucial for accurate backtesting.

  • Position Sizing:

    • The script uses default position sizing. If you wish to adjust the position size based on your risk management strategy, you can modify the strategy.entry() function with the qty parameter.

    • Example:

      strategy.entry("Long", strategy.long, qty=your_desired_quantity)

  • Customization:

    • You can adjust the indicator lengths and thresholds to better fit the specific asset or timeframe you're trading.

How to Use the Script:

  1. Copy and Paste:

    • Copy the entire script and paste it into a new Pine Editor window in TradingView.

  2. Select Timeframe:

    • Set your chart to the desired timeframe. Ensure that the higher timeframe data (big_tf) is available for the asset you're analyzing.

  3. Add to Chart:

    • Click on "Add to Chart" to apply the strategy.

  4. Backtesting:

    • Use the Strategy Tester in TradingView to backtest the strategy's performance and view detailed reports.

  5. Optimization:

    • Adjust the parameters such as jaw_length, teeth_length, lips_length, and indicator thresholds if needed.

Final Remarks:

  • This script aims to replicate your Jesse strategy in PineScript for use on TradingView.
  • Ensure that all functions and parameters align with your original strategy to maintain consistency.
  • Always test the strategy thoroughly before using it in live trading.

Let me know if you have any further questions or need additional assistance!

質問

send me whole script in once so i can copy it

思考

Providing the complete code

I shared the entire corrected PineScript code for the AlligatorAi strategy, including all required modifications.

Providing the code

OK, let me see. The assistant should give the entire script in one block, without explanations, as requested by the user.

Examining PineScript code for AlligatorAi strategy

回答

Certainly! Here's the complete PineScript code for your AlligatorAi strategy:

//@version=5
strategy("AlligatorAi Strategy", overlay=true)

// Determine the higher timeframe to analyze long-term trends
big_tf = timeframe.period == '240' ? '360' : '240'  // Use '6h' if current timeframe is '4h', else '4h'

// Helper function for SMMA (equivalent to RMA in PineScript)
smma(src, length) => ta.rma(src, length)

// Calculate the median price (hl2)
hl2 = (high + low) / 2

// Alligator indicator parameters
jaw_length = 13
jaw_offset = 8
teeth_length = 8
teeth_offset = 5
lips_length = 5
lips_offset = 3

// Calculate Alligator lines for the current timeframe
jaw = smma(hl2, jaw_length)
teeth = smma(hl2, teeth_length)
lips = smma(hl2, lips_length)

// Shift the Alligator lines for plotting and calculations
jaw_shifted = nz(jaw[jaw_offset])
teeth_shifted = nz(teeth[teeth_offset])
lips_shifted = nz(lips[lips_offset])

// Calculate Alligator lines for the higher timeframe
jaw_htf = request.security(syminfo.tickerid, big_tf, smma((high + low) / 2, jaw_length), lookahead=barmerge.lookahead_off)
teeth_htf = request.security(syminfo.tickerid, big_tf, smma((high + low) / 2, teeth_length), lookahead=barmerge.lookahead_off)
lips_htf = request.security(syminfo.tickerid, big_tf, smma((high + low) / 2, lips_length), lookahead=barmerge.lookahead_off)

// Shift the higher timeframe Alligator lines
jaw_htf_shifted = nz(jaw_htf[jaw_offset])
teeth_htf_shifted = nz(teeth_htf[teeth_offset])
lips_htf_shifted = nz(lips_htf[lips_offset])

// Determine the current trend based on the Alligator indicator
trend = 0
if close > lips_shifted and lips_shifted > teeth_shifted and teeth_shifted > jaw_shifted
    trend := 1  // Uptrend
else if close < lips_shifted and lips_shifted < teeth_shifted and teeth_shifted < jaw_shifted
    trend := -1  // Downtrend
else
    trend := 0  // No clear trend

// Determine the long-term trend based on the higher timeframe Alligator indicator
big_trend = 0
if close > lips_htf_shifted and lips_htf_shifted > teeth_htf_shifted and teeth_htf_shifted > jaw_htf_shifted
    big_trend := 1  // Long-term uptrend
else if close < lips_htf_shifted and lips_htf_shifted < teeth_htf_shifted and teeth_htf_shifted < jaw_htf_shifted
    big_trend := -1  // Long-term downtrend
else
    big_trend := 0  // No clear long-term trend

// Calculate the ADX to determine trend strength
adx_value = ta.adx(high, low, close, 14)
adx_condition = adx_value > 30

// Calculate the CMO (Chande Momentum Oscillator)
cmo_value = ta.cmo(close, 14)

// Calculate the Stochastic RSI (%K line)
stoch_rsi = ta.stochrsi(close, 14, 14, 3)
stoch_rsi_k = stoch_rsi * 100  // Convert to percentage

// Calculate the 100-period EMA on the higher timeframe for long-term trend confirmation
ema_htf = request.security(syminfo.tickerid, big_tf, ta.ema(close, 100), lookahead=barmerge.lookahead_off)
long_term_ma = close > ema_htf ? 1 : -1

// Calculate the ATR for stop loss and take profit levels
atr_value = ta.atr(14)

// Define conditions to enter a long position
long_condition = (trend == 1) and adx_condition and (big_trend == 1) and (long_term_ma == 1) and (cmo_value > 20) and (stoch_rsi_k < 20)

// Define conditions to enter a short position
short_condition = (trend == -1) and adx_condition and (big_trend == -1) and (long_term_ma == -1) and (cmo_value < -20) and (stoch_rsi_k > 80)

// Execute a long position with stop loss and take profit
if long_condition
    strategy.entry("Long", strategy.long)
    strategy.exit("Exit Long", "Long", stop=close - 2 * atr_value, limit=close + 2 * atr_value)

// Execute a short position with stop loss and take profit
if short_condition
    strategy.entry("Short", strategy.short)
    strategy.exit("Exit Short", "Short", stop=close + 2 * atr_value, limit=close - 2 * atr_value)

// Plot the Alligator indicator lines
plot(jaw_shifted, color=color.blue, title="Jaw", offset=-jaw_offset)
plot(teeth_shifted, color=color.red, title="Teeth", offset=-teeth_offset)
plot(lips_shifted, color=color.green, title="Lips", offset=-lips_offset)

You can now copy this entire script and paste it into the Pine Editor on TradingView. Make sure to save and add it to your chart to see it in action. Let me know if you need any more assistance!

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