feliperazeek

//@version=5
strategy("NakInvest - PFR Strategy", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=10)

// === INPUTS ===
shortEmaLength = input.int(9, title="Short EMA Length")
longEmaLength = input.int(100, title="Long EMA Length")
stochLength = input.int(8, title="Stochastic Length")
stochK = input.int(3, title="Stoch %K Smoothing")
stochD = input.int(3, title="Stoch %D Smoothing")
rsiLength = input.int(8, title="RSI Length")

feliperazeek

//@version=5
indicator("NakInvest - Bullish Candlestick Patterns", overlay=true)

// Function to identify a Hammer pattern
isHammer() =>
    lowerShadow = low < close - (high - low) * 0.5
    upperShadow = high - close < (high - low) * 0.1
    bodySize = math.abs(close - open)
    bodySize < (high - low) * 0.3 and lowerShadow and upperShadow

feliperazeek

//@version=5
indicator("NakInvest - Bullish Candlestick Patterns with Doji", overlay=true)

// Function to identify a Hammer pattern
isHammer() =>
    lowerShadow = low < close - (high - low) * 0.5
    upperShadow = high - close < (high - low) * 0.1
    bodySize = math.abs(close - open)
    bodySize < (high - low) * 0.3 and lowerShadow and upperShadow

feliperazeek

import org.apache.hadoop.conf.Configuration
import org.apache.iceberg.hadoop.HadoopFileIO
import org.apache.iceberg.spark.Spark3Util
import org.apache.iceberg.{DataFile, Snapshot, TableProperties, Table => IcebergTable}
import org.apache.spark.SparkConf
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{StringType, StructField, StructType, TimestampType}
import org.apache.spark.sql.{DataFrame, Row, SparkSession}
import org.junit.runner.RunWith
import org.scalatest.BeforeAndAfterAll

feliperazeek

import org.apache.hadoop.conf.Configuration
import org.apache.iceberg.hadoop.HadoopFileIO
import org.apache.iceberg.spark.Spark3Util
import org.apache.iceberg.{DataFile, PartitionSpec, Schema, Snapshot, TableMetadata, TableMetadataParser, Table => IcebergTable}
import org.apache.spark.SparkConf
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{StringType, StructField, StructType, TimestampType}
import org.apache.spark.sql.{DataFrame, Row, SparkSession}
import org.junit.runner.RunWith
import org.scalatest.BeforeAndAfterAll

feliperazeek

import turtle
import random
t = turtle.Turtle()
scorey = turtle.Turtle()
score = 0
t.goto(0,0)
scorey.penup()
scorey.goto(200,200)
t.setheading(90)
c = turtle.Turtle()

feliperazeek

{
  "type" : "record",
  "name" : "twitter_schema",
  "namespace" : "foo.bar",
  "fields" : [ {
    "name" : "username",
    "type" : "string",
    "doc" : "Name of the user account on Twitter.com"
  }, {
    "name" : "tweet",

feliperazeek

### My publicly-auditable identity:

https://keybase.io/felipera

### From the command line:

Consider the [keybase command line program](https://keybase.io/download).

```bash
# look me up

feliperazeek

import java.io.*;
import java.util.*;

public class Solution {
    
    private static void work(int[] a) {
        int swaps = 0;
        
        int n = a.length;
        

feliperazeek

import java.io.*;
import java.util.*;
import java.text.*;
import java.math.*;
import java.util.regex.*;

public class Solution {
    
    private static boolean isPrime(int n) {
        if (n <= 1) return false;