medusa/the_information_nexus
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

major updates

Browse Source
This commit is contained in:
Jason Davis
2023-11-11 12:32:35 -07:00
parent 6566454d27
commit 6d22eee90c
70 changed files with 30 additions and 2129 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
docs/financial_docs/heavily traded forex pairs.md Normal file
View File

@@ -0,0 +1,18 @@
1. EUR/USD (Euro/US Dollar): This is the most traded currency pair globally, as it represents the world's two largest economies. The pair experiences high volatility and liquidity, making it attractive to traders.
2. USD/JPY (US Dollar/Japanese Yen): This pair represents the US and Japanese economies, and it is the second most traded currency pair. The Japanese Yen is a safe-haven currency, so the pair often experiences significant price movements during periods of economic uncertainty.
3. GBP/USD (British Pound/US Dollar): Known as "Cable," this pair represents the economies of the United Kingdom and the United States. It is one of the oldest traded pairs and is popular among traders due to its high liquidity and relatively stable price action.
4. USD/CAD (US Dollar/Canadian Dollar): This currency pair represents the US and Canadian economies, with the Canadian Dollar being heavily influenced by the country's commodity-driven economy. The pair experiences relatively high volatility and liquidity, making it popular among traders.
5. AUD/USD (Australian Dollar/US Dollar): This pair is heavily influenced by commodity prices, particularly gold, as Australia is a major exporter of the precious metal. The AUD/USD pair is popular among traders due to its high liquidity and strong price action.
6. USD/CHF (US Dollar/Swiss Franc): This pair represents the US and Swiss economies, with the Swiss Franc being a safe-haven currency. The pair is popular among traders due to its relatively stable price action and liquidity.
7. NZD/USD (New Zealand Dollar/US Dollar): This pair represents the economies of New Zealand and the United States, with the New Zealand Dollar being heavily influenced by the country's agricultural exports. The pair has high liquidity and is popular among traders due to its strong price action.
These are the most heavily traded forex pairs, but other pairs involving major currencies and emerging market currencies can also experience significant price action and volume. It is important to note that market conditions and rankings can change over time, so it's essential to stay updated on the latest market news and trends.
#forex #trading

62
docs/financial_docs/pine.md Normal file
View File

@@ -0,0 +1,62 @@
optomize the following pinescript version 5 code:
//@version=5
strategy("Swing Trading Strategy with ATR Stop Loss and Take Profit", overlay=true)
// Define Daily Chart EMAs
ema20_length = input.int(title="EMA 20 Length", defval=20, minval=1)
ema50_length = input.int(title="EMA 50 Length", defval=50, minval=1)
ema100_length = input.int(title="EMA 100 Length", defval=100, minval=1)
ema200_length = input.int(title="EMA 200 Length", defval=200, minval=1)
daily_ema20 = ta.ema(close, ema20_length)
daily_ema50 = ta.ema(close, ema50_length)
daily_ema100 = ta.ema(close, ema100_length)
daily_ema200 = ta.ema(close, ema200_length)
// Define 4-Hour Chart EMAs
ema20_4h = ta.ema(close, ema20_length*6)
ema50_4h = ta.ema(close, ema50_length*6)
ema100_4h = ta.ema(close, ema100_length*6)
ema200_4h = ta.ema(close, ema200_length*6)
// Define Trend
daily_trend = daily_ema20 > daily_ema50 and daily_ema50 > daily_ema100 and daily_ema100 > daily_ema200
four_hour_trend = ema20_4h > ema50_4h and ema50_4h > ema100_4h and ema100_4h > ema200_4h
// Define RSI
rsi_length = input.int(title="RSI Length", defval=14, minval=1)
rsi_overbought_level = input.float(title="RSI Overbought Level", defval=70.0, minval=0.0, maxval=100.0)
rsi_oversold_level = input.float(title="RSI Oversold Level", defval=30.0, minval=0.0, maxval=100.0)
rsi = ta.rsi(close, rsi_length)
// Define ATR Multiplier
atr_multiplier = input.float(title="ATR Multiplier", defval=2.0, minval=0.0)
atr_period = input.int(title="ATR Period", defval=14, minval=1)
atr = ta.atr(atr_period)
// Define Additional Entry Criteria
price_action_high_length = input.int(title="Price Action High Length", defval=10, minval=1)
price_action_low_length = input.int(title="Price Action Low Length", defval=10, minval=1)
price_action_signal = ta.highest(high, price_action_high_length) > ta.highest(high, price_action_high_length _ 2) and ta.lowest(low, price_action_low_length) > ta.lowest(low, price_action_low_length _ 2)
supp_tf = input.timeframe(title="Support/Resistance Timeframe", defval="D")
supp_length = input.int(title="Support/Resistance Length", defval=30, minval=1)
buy_condition = daily_trend and four_hour_trend and rsi < rsi_oversold_level and price_action_signal
sell_condition = not four_hour_trend or rsi > rsi_overbought_level
stop_loss = atr _ atr_multiplier
take_profit = atr _ atr_multiplier \* 2
if buy_condition
strategy
strategy.entry("Buy", strategy.long)
strategy.exit("Exit", "Buy", stop=stop_loss, limit=take_profit)
to include the following updates:
Optimizing input parameters: In this code, the input parameters for the EMAs, RSI, ATR, and additional entry criteria are fixed. However, it might be more efficient to optimize these input parameters based on the specific instrument being traded. Using a script that can perform parameter optimization, such as the PineCoders BBands Optimizer, can help improve the performance of the strategy.
Using more advanced indicators: While this strategy uses several popular indicators, there are more advanced technical indicators that could potentially provide better signals. For example, using the Ichimoku Cloud or Bollinger Bands could provide additional information about price trends and support/resistance levels.
Adding a trailing stop: A trailing stop is a stop loss order that can be set at a fixed distance away from the current market price, and it moves up as the price increases. This can help protect profits and potentially maximize gains in a trending market.
Incorporating fundamental analysis: While this code focuses solely on technical analysis, it can be helpful to incorporate fundamental analysis as well. For example, monitoring economic indicators or news events related to the instrument being traded can provide additional context and help identify potential market-moving events.
Backtesting: Before deploying any trading strategy, it's important to backtest it using historical data to see how it would have performed in the past. This can help identify any potential weaknesses in the strategy and allow for modifications before trading with real money. Using a backtesting platform such as TradingView's Strategy Tester can help with this process.